Free and Premium Amazon Web Services SAA-C03 Dumps Questions Answers

To decouple the monolith and enhance scalability, AWS best practice is to introduce an asynchronous message queue, such as Amazon SQS, between the web/API tier and the order-processing logic.

AWS Lambda functions consuming from the SQS queue provide serverless, auto-scaling processing without managing servers.

To track and reprocess failed orders, SQS supports dead-letter queues (DLQs). Messages that cannot be processed successfully after a configurable number of attempts are automatically moved to the DLQ, where operations teams or automated processes can inspect and reprocess them.

Why others are not correct:

B: ECS tasks can consume an SQS queue, but this requires managing container infrastructure and does not inherently provide as simple reprocessing/visibility as combining Lambda with a DLQ. Visibility timeout is not a tracking or archival mechanism.

C: Kinesis is a streaming service designed for ordered event streams, not primarily for order-queue semantics and DLQs; SQS is simpler and purpose-built for this pattern.

D: Long polling reduces empty responses and API calls but does nothing for tracking or reprocessing failed messages; without a DLQ, failed orders are harder to manage

To allocate costs based on team ownership, AWS recommends tagging resources using cost allocation tags. Activating the “owner” tag as a cost allocation tag allows AWS Cost Explorer to categorize and group spending. Additionally, filtering for Amazon ECS services enables the visibility into service-specific usage. This approach is both scalable and operationally efficient.

This solution is designed to provide high availability and low-latency access to block storage across multiple Availability Zones with minimal implementation effort.

Windows Server Cluster Across AZs: Configuring a Windows Server Failover Cluster (WSFC) that spans two Availability Zones ensures that the application can failover from one instance to another in case of a failure, meeting the high availability requirement.

Amazon FSx for Windows File Server: FSx for Windows File Server provides fully managed Windows file storage that is accessible via the SMB protocol, which is suitable for Windows-based applications. It offers high availability and can be used as shared storage between the cluster nodes, ensuring that both nodes have access to the same data with low latency.

Why Not Other Options?:

Option B (EBS with application-level replication): This requires complex configuration and management, as EBS volumes cannot be directly shared across AZs. Application-level replication is more complex and prone to errors.

Option C (FSx for NetApp ONTAP with iSCSI): While this is a viable option, it introduces additional complexity with iSCSI and requires more specialized knowledge for setup and management.

Option D (EBS with EBS-level replication): EBS-level replication is not natively supported across AZs, and setting up a custom replication solution would increase the implementation effort.

AWS References:

Amazon FSx for Windows File Server- Overview and benefits of using FSx for Windows File Server.

Windows Server Failover Clustering on AWS- Guide on setting up a Windows Server cluster on AWS.

The requirement is for immediate processing of uploaded videos and prompt notification to users. According to AWS best practices for event-driven architectures, using S3 event notifications to trigger a Lambda function upon an object creation (upload) is the optimal solution for real-time processing. Lambda can process the file as soon as it is uploaded, ensuring low latency. Once processing is complete, Lambda can save the processed file back to S3 and use Amazon SNS to notify the user.

This approach uses managed services with minimal operational overhead, is scalable, and ensures event-driven processing with instant user feedback. AWS Amplify primarily facilitates application development and hosting but does not natively provide direct push notification support for this backend workflow; instead, SNS is designed for such notification scenarios.

Reference Extract from AWS Documentation / Study Guide:

"Amazon S3 can publish events to AWS Lambda when objects are created. AWS Lambda runs code in response to events and can interact with other AWS services. Amazon SNS is a flexible, fully managed pub/sub messaging and mobile notifications service for coordinating the delivery of messages to subscribing endpoints and clients."

Source: AWS Certified Solutions Architect – Official Study Guide, Event-driven Architectures section; AWS Lambda Developer Guide (S3 triggers); Amazon SNS User Guide.

A: Amazon Macie can scan S3 buckets for sensitive data and identify PII.

C: S3 Object Lock legal hold prevents object deletion until a review is complete, meeting compliance requirements.

E: EventBridge can trigger the Lambda function automatically when Macie detects sensitive data, creating an automated workflow.

AWS Documentation Extract:

"Amazon Macie automatically discovers, classifies, and protects sensitive data in AWS. You can use EventBridge to invoke a Lambda function for post-processing, such as applying Object Lock legal hold to flagged objects."

(Source: AWS Macie and S3 Object Lock documentation)

B, D: Moving to Glacier Deep Archive or applying retention in governance mode is not specific to deletion prevention pending review.

F: MFA Delete adds a security layer but does not automate object retention for flagged PII.

Amazon S3 supports one Lifecycle configuration per bucket that can contain multiple rules. Lifecycle rules can include filters, including object size filters: ObjectSizeGreaterThan and ObjectSizeLessThan. You can combine rules so that one targets large objects and another provides a default expiration. Here, configure Rule 1 with a size filter ObjectSizeGreaterThan = 1 TB and Expiration = 2 days (48 hours) to purge very large videos early. Configure Rule 2 with Expiration = 7 days without a size filter to serve as a catch-all maximum retention for all objects. Options B and D are invalid because S3 does not allow multiple Lifecycle configurations per bucket. Option C cannot distinguish large files; it would delete all files at the same schedule without honoring the 48-hour policy for >1 TB objects. This single configuration with two rules meets both retention targets with minimal overhead and full S3-native capability.

AWS Shield Advanced is designed to provide enhanced protection against DDoS attacks with proactive engagement and response capabilities, making it the best solution for this scenario.

AWS Shield Advanced: This service provides advanced protection against DDoS attacks. It includes detailed attack diagnostics, 24/7 access to the AWS DDoS Response Team (DRT), and financial protection against DDoS-related scaling charges. Shield Advanced also integrates with Route 53 and the Application Load Balancer (ALB) to ensure comprehensive protection for your web applications.

Route 53 and ALB Protection: By adding your Route 53 hosted zones and ALB resources to AWS Shield Advanced, you ensure that these components are covered under the enhanced protection plan. Shield Advanced actively monitors traffic and provides real-time attack mitigation, minimizing the impact of DDoS attacks on your application.

Why Not Other Options?:

Option A (AWS Config): AWS Config is a configuration management service and does not provide DDoS protection or detection capabilities.

Option B (AWS WAF): While AWS WAF can help mitigate some types of attacks, it does not provide the comprehensive DDoS protection and proactive engagement offered by Shield Advanced.

Option C (GuardDuty): GuardDuty is a threat detection service that identifies potentially malicious activity within your AWS environment, but it is not specifically designed to provide DDoS protection.

AWS References:

AWS Shield Advanced- Overview of AWS Shield Advanced and its DDoS protection capabilities.

Integrating AWS Shield Advanced with Route 53 and ALB- Detailed guidance on how to protect Route 53 and ALB with AWS Shield Advanced.

AWS Glue provides a serverless ETL solution requiring minimal development. Glue supports conversion to Parquet with managed jobs and integrates with S3 for output.

AWS Documentation References:

AWS Glue Overview

AWS Budgets allows you to set custom cost and usage budgets. When actual or forecasted usage exceeds the threshold, AWS Budgets can automatically send alerts to an Amazon SNS topic. You can use AWS Cost Explorer in parallel for visual tracking of spending. This solution requires no code and has minimal operational overhead.

Setting the RDS backup retention policy to 30 days forautomated backupsthrough AWS Backup allows the company to retain backups cost-effectively. Manual backups, however, are notautomatically managed by RDS's retention policy, so they need to be manually deleted if they are older than 30 days to avoid unnecessary storage costs.

Key AWS features:

Automated Backups: Can be configured with a retention policy of up to 35 days, ensuring that older automated backups are deleted automatically.

Manual Backups: These are not subject to the automated retention policy and must be manually managed to avoid extra costs.

AWS Documentation: AWS recommends using backup retention policies for automated backups while manually managing manual backups​.

Enabling Multi-AZ deployment for an Amazon RDS DB instance is the simplest and most effective way to improve database availability and eliminate a single point of failure. Multi-AZ deployments provide automatic failover to a standby instance in case of a failure of the primary instance. This approach is managed by AWS and only requires a modification to the existing instance, with minimal manual intervention or downtime, thus providing the least implementation effort.

Reference Extract from AWS Documentation / Study Guide:

"With Multi-AZ deployments, Amazon RDS automatically creates a primary DB Instance and synchronously replicates the data to a standby instance in a different Availability Zone (AZ). In case of a failure of the primary DB Instance, Amazon RDS automatically fails over to the standby so that database operations can resume quickly."

Source: AWS Certified Solutions Architect – Official Study Guide, RDS High Availability section.

Option A:The ALB must accept HTTPS traffic from the public internet. Allowing inbound traffic on port 443 from 0.0.0.0/0 enables this functionality.

Option C:The ALB must forward HTTPS traffic to the web application servers on port 443. Outbound traffic for port 443 must be allowed for this communication.

Option E:The ALB must perform health checks on the web application servers over HTTPS on port 8443. Outbound traffic for port 8443 must be allowed for this purpose.

Option B:Allowing all outbound traffic is overly permissive and does not align with the specific requirements.

Option D and F:Inbound traffic to the ALB from the web application instances is unnecessary because the flow of traffic is from the ALB to the web application instances, not vice versa.

AWS Documentation References:

Application Load Balancer Security Groups

Health Checks for ALBs

Aurora Global Database: Provides cross-Region disaster recovery with minimal data loss (<1 second replication latency).

S3 Cross-Region Replication (CRR): Automatically replicates data between buckets in different Regions.

“Aurora Global Database replicates your data with typically under one second of latency to secondary Regions.”

“Amazon S3 Cross-Region Replication automatically replicates objects across buckets in different AWS Regions.”

— Aurora Global Database

— S3 Cross-Region Replication

This meets the multi-Region DR requirement with minimal data loss.

AWS Transit Gateway is purpose-built for large-scale, hub-and-spoke network architectures. It simplifies connectivity between multiple VPCs and on-premises environments, which is ideal for managing hundreds of VPCs.

“AWS Transit Gateway enables you to connect your VPCs and on-premises networks through a central hub. This simplifies your network and puts an end to complex peering relationships.”

— Transit Gateway Documentation

Features:

Scales to thousands of VPCs.

Integrates with AWS Direct Connect via Direct Connect Gateway.

Centralized routing control.

Incorrect Options:

A: VPC endpoints are for private access to AWS services—not VPC-to-VPC connectivity.

C: Route 53 is DNS, not a network transport layer.

D: Secrets Manager is for secret storage, not networking.

For Amazon RDS relational databases experiencing read-heavy workloads, AWS best practice is to create read replicas and offload read traffic to those replicas. A read replica:

Uses asynchronous replication from the primary.

Can serve read-only queries, thereby reducing load and query latency on the primary.

Can be used behind an application-level read/write splitting mechanism or via endpoints that direct read traffic to replicas.

Performance Insights (Option A) helps diagnose performance problems but does not itself offload traffic.

DAX (Option B) is a cache for DynamoDB, not RDS.

Kinesis Data Firehose (Option D) is a streaming ingestion service and cannot increase RDS query concurrency.

Amazon API Gateway supportsresource policies, which allow you to control access to your API by specifying the IP addresses or ranges that can access the API. By creating a resource policythat explicitly denies access to any IP address outside the allowed set, you can ensure that only trusted IP addresses (such as those from your internal network) can access the critical information in your API. This approach provides fine-grained access control without the need for additional infrastructure or complex configurations.

Option A (Private integration): API Gateway private integrations are for creating private APIs that are only accessible within a VPC, but this solution is about restricting access to certain IP addresses.

Option C (Private subnet and ACLs): Deploying the API in a private subnet and using network ACLs adds unnecessary complexity and isn't the best fit for HTTP APIs.

Option D (Security group): API Gateway doesn't have a security group because it isn't a resource inside a VPC. Instead, resource policies are the correct mechanism for controlling IP-based access.

AWS References:

Controlling Access to API Gateway with Resource Policies

Comprehensive and Detailed Step-by-Step Explanation:

To accurately charge customers based on their monthly data download usage, the following solution is recommended:

Structured Logging Configuration:

Action:Ensure that the AWS Transfer for SFTP server is configured to log user activity, including details about file downloads, to Amazon S3 in a structured format.

Implementation:Utilize AWS Transfer Family's structured logging feature to capture detailed information about user sessions, including actions performed and data transferred.

docs.aws.amazon.com

Justification:Structured logs provide comprehensive data necessary for analyzing customer-specific download activities.

Data Analysis with Amazon Athena:

Action:Use Amazon Athena to run SQL queries on the structured log data stored in the S3 bucket to calculate the amount of data each customer has downloaded.

Implementation:

a.Define a Schema:Create a table in Athena that maps to the structure of your log files. This involves specifying the format of the logs and the location in S3.

b.Query Data:Write SQL queries to sum the total bytes downloaded by each customer over the billing period. This can be achieved by filtering logs based on user identifiers and summing the data transfer amounts.

Justification:Athena allows for efficient querying

To break down consolidated billing costs by “product line” using tags, the company must use cost allocation tags and ensure those tags are activated for billing at the payer/management level. Because the teams have already applied user-defined tags to the compute resources across accounts, the correct approach is to (1) use those user-defined tags in the billing tools and (2) activate them so they appear in cost and usage reporting for consolidated billing.

B is required because cost reporting by product line depends on selecting the user-defined cost allocation tag key (for example, ProductLine=...) in AWS billing and cost management tooling (such as Cost Explorer reports). AWS-generated tags are not what teams typically use for chargeback by business dimension, and the scenario explicitly says teams tagged resources (i.e., user-defined).

E is required because in an AWS Organizations consolidated billing setup, cost allocation tag activation is handled centrally from the Organizations management account so that the tags can be used consistently in consolidated cost views and reporting. Activating tags centrally ensures the billing system recognizes and includes those tag dimensions in cost allocation data.

Option C is not relevant to billing breakdown; Resource Groups is for organizing and operating on resources, not for consolidated billing allocation. Option D is unnecessary in this scenario’s intended best practice because activation for consolidated billing reporting is done from the management account to standardize reporting. In short, you tag resources (already done), then activate the tag for billing and use it in billing reports to see cost by product line across accounts and Regions.

Therefore, B and E together meet the requirement with the correct billing-focused steps.

Amazon FSx for Lustre is the ideal solution for high-performance computing (HPC) workloads that require parallel access to a shared file system with low latency. FSx for Lustre is designed specifically to meet the needs of such workloads, offering sub-millisecond latencies, which makes it well-suited for the 1 ms latency requirement mentioned in the question.

Here is why FSx for Lustre is the best fit:

Parallel File System: FSx for Lustre is a parallel file system that can scale across hundreds of Amazon EC2 instances, providing high throughput and low-latency access to data. It is optimized for processing large datasets in parallel, which is essential for HPC workloads.

Low Latency: FSx for Lustre is capable of providing access latencies well within 1 ms, making it ideal for performance-sensitive workloads like HPC.

Seamless Integration with Amazon S3: FSx for Lustre can be linked to an Amazon S3 bucket. This integration allows data to be imported from S3 into FSx for Lustre before the workload begins and exported back to S3 after processing. This feature is crucial for manual postprocessing because it enables engineers to access the dataset in S3 after processing.

Performance: FSx for Lustre is built for workloads that require high performance, such as machine learning, analytics, media processing, and financial simulations, which are typical for HPC environments.

In contrast:

Amazon EFS (Option A): While EFS provides shared file storage and scales across multiple EC2 instances, it does not offer the same level of performance or sub-millisecond latencies as FSx for Lustre. EFS is more suited for general-purpose workloads, not high-performance computing.

Mounting S3 as a file system (Option B and D): S3 is object storage, not a file system designed for low-latency access and parallel processing. Mounting S3 buckets directly or using AWS Resource Access Manager to share the bucket would not meet the low-latency (1 ms) or performance requirements needed for HPC workloads.

Therefore, Amazon FSx for Lustre (Option C) is the most appropriate and verified solution for this scenario.

AWS References:

Amazon FSx for Lustre

Best Practices for High Performance Computing (HPC)

Amazon FSx and Amazon S3 Integration

Cluster Placement Group: This type of placement group is designed to provide low-latency network performance and high throughput by grouping instances within a single Availability Zone. It is ideal for applications that require tightly coupled node-to-node communication.

Configuration:

When launching EC2 instances, specify the option to launch them in a cluster placement group.

This ensures that the instances are physically located close to each other, reducing latency and increasing network throughput.

Benefits:

Low-Latency Communication: Instances in a cluster placement group benefit from enhanced networking capabilities, enabling low-latency communication.

High Network Throughput: The network performance within a cluster placement group is optimized for high throughput, which is essential for HPC workloads.

Amazon Aurora PostgreSQL with Babelfish allows Aurora to understand SQL Server T-SQL and the SQL Server wire protocol. This enables applications to continue using SQL Server drivers, minimizing code changes (Option B).

Migration of schema and data is performed using AWS Schema Conversion Tool (SCT) and AWS Database Migration Service (DMS) (Option C), which is the AWS-recommended migration pattern for heterogeneous database migrations.

AWS DMS (Option E) does not rewrite application SQL.

RDS Proxy (Option D) does not translate SQL Server protocols.

Option A requires rewriting application queries, which contradicts the “minimal changes” requirement.

The company wants to reduce end-to-end load time for its global customer base.AWS Global Acceleratorprovides a network optimization service that reduces latency by routing traffic to the nearest AWS edge locations, improving the user experience for globally distributed customers.

AWS Global Accelerator:

Global Acceleratorimproves the performance of your applications by routing traffic through AWS’s global network infrastructure. This reduces the number of hops and latency compared to using the public internet.

By creating astandard acceleratorand configuring the existing NLBs as target endpoints, Global Accelerator ensures that traffic from users around the world is routed to the nearest AWS edge location and then through optimized paths to the NLBs in each region. This significantly improves end-to-end load time for global customers.

Why Not the Other Options?:

Option A (ALBs instead of NLBs): ALBs are designed for HTTP/HTTPS traffic and provide layer 7 features, but they wouldn’t solve the latency issue for a global customer base. The key problem here is latency, and Global Accelerator is specifically designed to address that.

Option B (Route 53 weighted routing): Route 53 can route traffic to different regions, but it doesn’t optimize network performance. It simply balances traffic between endpoints without improving latency.

Option C (Additional NLBs in more regions): This could potentially improve latency but would require setting up infrastructure in multiple regions. Global Accelerator is a simpler and more efficient solution that leverages AWS’s existing global network.

AWS References:

AWS Global Accelerator

By usingAWS Global Acceleratorwith the existing NLBs, the company can optimize global traffic routing and improve the customer experience by minimizing latency. Therefore,Option Dis the correct answer.

Maintaining two NAT gateways for production ensures high availability. Reducing to one NAT gateway in non-production environments lowers cost while maintaining necessary connectivity. This approach is recommended by AWS for cost optimization in non-critical environments.

Reference Extract:

"For environments that do not require high availability, you can reduce costs by using a single NAT gateway and updating route tables accordingly."

Source: AWS Certified Solutions Architect – Official Study Guide, Cost Optimization and NAT Gateway section.

Amazon Aurora Serverless is a fully managed, MySQL-compatible database that automatically scales based on demand and provides high availability. Combining this with EC2 Auto Scaling and an Application Load Balancer achieves both application and database high availability and scalability.

Reference Extract:

"Aurora Serverless automatically starts up, shuts down, and scales capacity based on your application's needs, providing a cost-effective, highly available database solution."

Source: AWS Certified Solutions Architect – Official Study Guide, Aurora Serverless and Scaling section.

AWS Config has a built-in managed rule (access-keys-rotated) that evaluates IAM access key age. Config rules detect non-compliant resources automatically, without building custom logic.

After Config identifies old keys, EventBridge can trigger an AWS Lambda function to disable and delete the keys. Lambda provides a fully managed compute layer requiring no servers or batch environments.

Using AWS Batch (Options A, B, and D) adds unnecessary operational overhead for a simple automation task.

=====================================================

For a MySQL database experiencing high write operations, using Amazon RDS with Provisioned IOPS (io1 or io2) SSD storage is recommended to achieve consistent and low-latency performance. Provisioned IOPS allows you to specify a desired IOPS rate, which is crucial for write-intensive workloads.

Monitoring write operation metrics through Amazon CloudWatch enables you to observe performance and adjust the provisioned IOPS as needed to meet application demands.

Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) is a fully managed, serverless service for real-time processing of streaming data. You can consume data from Kinesis Data Streams, perform anomaly detection, and then output the results to another Kinesis stream. This approach is loosely coupled, fully managed, and does not require modifying the application code.

AWS Documentation Extract:

“Amazon Managed Service for Apache Flink enables you to process streaming data in real time, integrating with Kinesis Data Streams as source and sink, and is fully managed and serverless.”

(Source: Apache Flink on AWS documentation)

B: S3/Redshift is not real-time and adds complexity.

C, D: EC2/ECS solutions are not serverless or fully managed.

This solution is the most cost-effective and scalable for analyzing large amounts of web traffic logs.

Amazon S3: Storing the logs in Amazon S3 is highly scalable, durable, and cost-effective. S3 is designed to handle large-scale data storage, which is ideal for storing tens of gigabytes of log data generated daily by multiple websites.

Amazon Athena: Athena is a serverless, interactive query service that allows you to analyze data in S3 using standard SQL. It works directly with the data stored in S3, so there’s no need to load the data into a database, which saves on costs and reduces complexity. Athena charges based on the amount of data scanned by queries, making it a cost-effective solution for on-demand analysis that only occurs once a week.

Why Not Other Options?:

Option B (Amazon RDS): Storing logs in a relational database like Amazon RDS would be more expensive due to the storage and I/O costs associated with RDS. Additionally, it would require more management overhead.

Option C (Amazon OpenSearch Service): OpenSearch is a good option for full-text search and analytics on log data, but it might be more costly and complex to manage compared to the simplicity and cost-effectiveness of Athena for periodic SQL-based queries.

Option D (Amazon EMR): While EMR can handle large-scale data processing, it involves more operational overhead and might be overkill for the type of ad-hoc, SQL-based analysis required here. Additionally, EMR costs can be higher due to the need to maintain a cluster.

AWS References:

Amazon S3- Information on how to store and manage data in Amazon S3.

Amazon Athena- Documentation on using Amazon Athena for querying data stored in S3 using SQL.

Amazon EMR allows the creation of security configurations to specify settings for encrypting data at rest, data in transit, or both. These configurations can be applied to clusters to ensure that data stored in Amazon S3, local disks, and data moving between nodes is encrypted.

By creating and applying an EMR security configuration, the company can ensure that all data processing complies with encryption requirements for sensitive patient data.

The most cost-effective and scalable solution for generating thumbnails when photos are uploaded to an S3 bucket is to useS3 event notificationsto trigger anAWS Lambda function. This approach avoids the need for a long-running EC2 instance or EMR cluster, making it highly cost-effective because Lambda only charges for the time it takes to process each event.

S3 Event Notifications: Automatically triggers the Lambda function when a new photo is uploaded to the S3 bucket.

AWS Lambda: A serverless compute service that scales automatically and only charges for execution time, which makes it the most economical choice when dealing with periodic events like photo uploads.

The Lambda function can generate the thumbnail and upload it to a second S3 bucket, fulfilling the requirement efficiently.

Option AandOption B(EMR or EC2 with scheduled scripts)**: These are less cost-effective as they involve continuously running infrastructure, which incurs unnecessary costs.

Option D (S3 Storage Lens): S3 Storage Lens is a tool for storage analytics and is not designed for event-based photo processing.

AWS References:

Amazon S3 Event Notifications

AWS Lambda Pricing

Amazon S3 with Amazon CloudFront:

Amazon S3 provides highly scalable and durable storage for petabytes of data.

Amazon CloudFront, as a content delivery network (CDN), caches frequently accessed data at edge locations to reduce latency. This combination is ideal for storing and accessing engineering drawings.

Incorrect Options Analysis:

Option B: Amazon S3 Glacier Deep Archive is for long-term archival storage, not frequent access.

Option C: Amazon EBS is unsuitable for large-scale, multi-user data access and does not support caching directly.

Option D: AWS Storage Gateway is for hybrid cloud storage, which is unnecessary for a fully cloud-based architecture.

Amazon Route 53 health checks can automatically perform DNS failover to healthy endpoints. When integrated with an Application Load Balancer, this provides a highly resilient system architecture that automatically routes traffic to healthy resources across Regions or endpoints.

From AWS Documentation:

“Route 53 DNS failover automatically routes traffic away from unhealthy resources and directs it to healthy resources that you specify in DNS records.”

(Source: Amazon Route 53 Developer Guide – DNS Failover)

Why B is correct:

Route 53 health checks automatically monitor endpoint health and switch to a healthy target when the primary endpoint fails.

The failover process is automatic, with no manual updates to DNS records required.

Combined with ALB’s built-in multi-AZ resilience, this provides maximum availability.

Why others are incorrect:

A & C: Require manual DNS updates, which are not automatic and introduce latency.

D: Creating new ALBs during failover increases downtime and is operationally inefficient.

Amazon FSx for Lustreis a high-performance, fully managed file system that is ideal for applications requiring low-latency access to shared storage, especially in use cases like gaming where high throughput and low latency are essential. It integrates easily with EC2 instances, providing fast and scalable shared storage, and supports custom protocols for specific application needs.

Option A (FSx File Gateway): FSx File Gateway is designed for hybrid cloud storage and is not suited for high-performance gaming workloads.

Option B (EC2 Windows instance): Setting up a file share on a Windows instance would introduce additional administrative overhead and would not provide the necessary performance.

Option C (EFS with Lustre): While Lustre is integrated with FSx, EFS does not natively support Lustre.

AWS References:

Amazon FSx for Lustre

TheGateway Load Balancer (GWLB)is specifically designed to route traffic through a security appliance in a hub-and-spoke model, making it the ideal solution for inspecting traffic between multiple AWS accounts. GWLB enables you to simplify, scale, and deploy third-party virtual appliances transparently, and it can work across multiple VPCs or accounts using interface endpoints (Gateway Load Balancer Endpoints).

Key AWS features:

Traffic Inspection: The GWLB allows the centralized security appliance to inspect traffic between different VPCs, making it suitable for inspecting inter-account interactions.

Interface VPC Endpoints: By using interface endpoints in the application accounts, traffic can securely and efficiently be routed to the security appliance in the networking account.

AWS Documentation: The use of GWLB aligns with AWS’s best practices for centralized network security, simplifying architecture and reducing operational complexity​.

Amazon S3 uses gateway VPC endpoints, which enable private, secure access to S3 without traversing the internet, compatible with IPv6.

Amazon DynamoDB uses interface VPC endpoints (powered by AWS PrivateLink) for private connectivity within the VPC.

Therefore, for secure private communication without public internet, the correct solution is to implement a gateway VPC endpoint for S3 and an interface VPC endpoint for DynamoDB.

Option B is incorrect because S3 does not support interface endpoints; Option C is incorrect because DynamoDB does not support gateway endpoints. Option D reverses the correct endpoint types.

Amazon API Gateway with CloudFront: API Gateway allows you to create, deploy, and manage APIs, while CloudFront provides a CDN to deliver content with low latency and high transfer speeds.

AWS WAF (Web Application Firewall):

AWS WAF can be configured in CloudFront to protect against common web exploits, including SQL injection and cross-site scripting (XSS).

WAF allows you to create custom rules to block specific attack patterns and can be managed centrally.

Configuration:

Deploy your APIs using Amazon API Gateway.

Set up an Amazon CloudFront distribution in front of the API Gateway.

Configure AWS WAF on the CloudFront distribution to apply security rules.

Operational Efficiency: This solution provides robust protection with minimal operational overhead by leveraging managed AWS services, ensuring that your APIs are secure without extensive custom implementation.

The Kubernetes Horizontal Pod Autoscaler (HPA) scales pods, not nodes. When the cluster runs out of node capacity, the HPA cannot schedule more pods.

On Amazon EKS, AWS recommends using the Kubernetes Cluster Autoscaler to automatically adjust the number of nodes in the cluster’s underlying Auto Scaling group based on pending pods.

Cluster Autoscaler watches for pods that cannot be scheduled because of insufficient resources and then scales out nodes automatically with minimal administrative overhead. It also scales in nodes when they are underutilized.

Why others are not correct:

A: Just tracking memory usage does not automatically scale nodes or integrate with Kubernetes scheduling.

C: A custom Lambda-based resizer is unnecessary undifferentiated heavy lifting compared to the native Cluster Autoscaler.

D: An Auto Scaling group is already typically used under EKS, but by itself it does not respond to pod scheduling pressure without Cluster Autoscaler.

Key Requirements:

Host the frontend on AWS as a static website.

Protect the application from common web vulnerabilities.

Minimal operational overhead.

Analysis of Options:

Option A:

Hosting the frontend on EC2 with an ALB introduces unnecessary complexity for serving static content.

AWS WAF rules can protect the ALB, but managing EC2 instances adds operational overhead.

Incorrect Approach:High operational complexity for a simple static website.

Option B:

Amazon CloudFront:Acts as a global CDN, reducing latency and protecting against DDoS attacks.

Multiple Origins:Allows static content to be served from S3 while routing API traffic to the on-premises backend.

AWS WAF:Integrates with CloudFront to provide web application protection.

Correct Approach:Offers low operational overhead with optimal security and performance.

Option C:

Using NLB and Network Firewall is unnecessary for a static website. This approach increases cost and complexity without addressing the frontend requirements effectively.

Incorrect Approach:Over-engineered solution.

Option D:

Hosting the frontend on S3 and using API Gateway is a viable option, but managing AWS WAF rules separately for both the S3 bucket and the REST API increases complexity.

Incorrect Approach:Less efficient than using CloudFront with multiple origins.

AWS Solution Architect References:

Amazon CloudFront Overview

AWS WAF with CloudFront

Explanation (AWS Docs):

You cannot “place” DynamoDB inside a VPC. Instead, you use a VPC endpoint.

A Gateway VPC Endpoint for DynamoDB enables private connectivity between your VPC and DynamoDB without traversing the public internet.

“Use a gateway VPC endpoint to privately connect your VPC to DynamoDB without requiring an internet gateway or NAT.”

— Gateway VPC Endpoints

EBS Elastic Volumes allow you to dynamically increase storage size, adjust performance, and change volume types without downtime, supporting operational efficiency and scalability for SaaS applications that need to allocate varying storage amounts to customers.

Migrating from EBS to S3 (Option A) is not suitable since S3 is object storage, not block storage, and does not support block-level I/O required by many applications. EFS (Option B) and FSx (Option C) are shared file systems, which might add unnecessary complexity and cost, especially if the application depends on block storage semantics.

Using Lambda to automate Elastic Volumes resizing provides cost efficiency by allocating resources on demand and reduces operational overhead, aligning with AWS operational excellence and cost optimization best practices.

AWS Secrets Manager natively supports automatic rotation of RDS for Oracle credentials, fully integrating with Amazon RDS. Rotation workflows are managed automatically by the service, eliminating custom scripting and reducing operational effort.

Migrating to EC2 (Option C) requires custom rotation logic. Converting to DynamoDB or Neptune (Options A and D) would require complete database redesign and do not fulfill the requirement to run Oracle.

To enforce that IAM users can only access Amazon RDS and no other AWS services, the recommended approach is to use a Deny statement with NotAction. This ensures that all actions are denied except RDS actions. Options A and B do not fully achieve the restriction: A only allows RDS but does not explicitly deny access to other services if another policy grants access; B’s explicit Deny for “*” would override all other permissions, including the intended RDS Allow, which would result in no access at all. Option D with permissions boundaries still allows other attached policies to grant access outside RDS. Therefore, C is the correct approach to enforce RDS-only access.

Detailed Explanation:

A. RDS:Suitable for relational databases but does not provide native support for data lakes or row-level access.

B. Redshift:Primarily for analytics, not designed for large-scale data lake governance.

C. S3 + Lake Formation:Provides native support for data lakes with granular access control, including row-level permissions.

D. Glue Catalog + DataBrew:Focused on data preparation and metadata management, not row-level access control.

Using SQS as a buffer between S3 and the Lambda function ensures durability and allows for retries in case of processing failures. Messages in the queue can be retried by Lambda, and failed processing can be directed to a dead-letter queue for further inspection. This guarantees reliable and scalable message-driven processing.

AWS Global Accelerator is designed to improve the availability and performance of applications with global users by using the AWS global network. It provides static anycast IP addresses and routes user traffic over the AWS edge network to the optimal AWS Region and endpoint based on health, geography, and routing policies. Global Accelerator supports both TCP and UDP traffic and can have Network Load Balancers as endpoints.

For latency-sensitive workloads such as multiplayer gaming, Global Accelerator reduces latency and jitter compared to internet-based routing and handles Regional failover quickly.

CloudFront (Option A) is optimized for HTTP/HTTPS content caching and is not appropriate for arbitrary TCP/UDP gaming traffic. Application Load Balancers (Option B) do not support UDP traffic. API Gateway (Option D) is for HTTP APIs and is not suitable for raw TCP/UDP game traffic.

To optimize storage costs, migrating data from Amazon EFS to Amazon S3 with the Intelligent-Tiering storage class is a cost-effective solution.

Amazon S3 Intelligent-Tiering: This storage class automatically moves data between frequent, infrequent, and archive access tiers based on access patterns, reducing storage costs without impacting performance.

Cost Savings: By leveraging Intelligent-Tiering, the company can achieve significant cost savings, especially for data with unpredictable access patterns.

Application Update: The application must be updated to interact with Amazon S3 APIs for storing and retrieving files, ensuring seamless integration with the new storage solution.

This approach provides a scalable, durable, and cost-effective storage solution, aligning with the company's optimization goals.

This solution meets the company's requirements with minimal administrative overhead and ensures security and ease of management.

AWS AppConfig: AWS AppConfig is a service designed to manage application configuration in a secure and validated way. It allows you to deploy configurations safely and quickly without affecting the application's performance or availability.

AWS Secrets Manager: AWS Secrets Manager is specifically designed to manage, retrieve, and rotate credentials for databases and other services. It integrates seamlessly with AWS services like Amazon RDS, making it an ideal solution for securely storing and retrieving database credentials. Secrets Manager also provides automatic rotation of credentials, reducing the operational burden.

Why Not Other Options?:

Option B (AWS Lambda + Parameter Store): While AWS Lambda can be used for managing configurations and AWS Systems Manager Parameter Store can store credentials, this approach involves more manual setup and does not offer the same level of integrated management and security as AppConfig and Secrets Manager.

Option C (Encrypted S3 Configuration File): Storing configuration and credentials in S3 files involves more manual management and security considerations, increasing the administrative overhead.

Option D (AppConfig + RDS for credentials): RDS is not designed for storing application credentials; it's better suited for managing database instances and their configurations.

AWS References:

AWS AppConfig- Describes how to use AWS AppConfig for managing application configurations.

AWS Secrets Manager- Provides details on securely storing and retrieving credentials using AWS Secrets Manager.

Aurora PostgreSQL supports the pgvector extension, which allows storage and querying of vector embeddings directly inside the database. This eliminates the need for external vector databases and provides cost-effective and performant integration for AI workloads.

For steady, predictable EC2 usage with potential changes in instance families over time, AWS recommends Savings Plans. Compute Savings Plans “apply to any EC2 instance regardless of region, instance family, operating system, or tenancy,” and also apply to AWS Fargate and AWS Lambda, delivering the most flexibility over a multi-year horizon. A 3-year term provides the highest discount among Savings Plans for long-lived workloads. EC2 Instance Savings Plans are limited to a chosen instance family in a region; as needs evolve (e.g., size or family changes), discounts may not fully apply. Spot Instances are not appropriate for long, interruption-sensitive jobs because Spot capacity can be reclaimed with short notice. Therefore, a Compute Savings Plan (3-year) best matches cost optimization with flexibility for growth and changes.

This is a classic use case for Amazon Kinesis Data Streams + OpenSearch + QuickSight:

Kinesis Data Streams: For real-time ingestion and processing

OpenSearch Service: For fast full-text search, indexing, and analysis

QuickSight: For rich dashboard visualizations

This stack is fully managed, scalable, and native to AWS, minimizing operational overhead.

TooManyRequestsException errors occur when Lambda exceeds concurrency limits. The recommended pattern is to use Amazon SQS with Lambda to decouple and buffer traffic, ensuring that bursts of requests are queued and processed smoothly. Enabling provisioned concurrency for Lambda ensures that functions are pre-initialized and ready to handle spikes in load with low latency. Step Functions (B) is designed for workflow orchestration, not high-throughput request buffering. SNS with Lambda (C) does not provide buffering and may overwhelm Lambda during bursts. Reserved concurrency (D) limits function scaling instead of improving resilience. Therefore, option A provides a scalable and resilient solution, minimizing errors during traffic surges.

IAM Roles for Service Accounts (IRSA): IRSA allows you to associate an IAM role with a Kubernetes service account. This enables pods to assume the IAM role and access AWS resources securely.

Annotating Service Accounts: By annotating Kubernetes service accounts with the ARN of the IAM role, you establish the association required for IRSA.

OIDC Identity Provider: EKS clusters use OpenID Connect (OIDC) to authenticate service accounts. Setting up a trust relationship between the IAM role and the OIDC provider allows the Kubernetes service account to assume the IAM role.

This workload has two distinct needs: (1) a managed way for external clients to upload files using a familiar managed file transfer protocol, and (2) an automated, low-ops method to run a watermarking step and store the resulting images as objects. AWS Transfer Family provides a fully managed capability to receive files over protocols such as SFTP, while landing those files directly into Amazon S3 as objects. That directly satisfies the requirement that “uploaded images must be stored as objects” with minimal infrastructure management.

To automate watermarking with minimal operational overhead, invoking AWS Lambda is a strong fit. Lambda is serverless and scales automatically with incoming uploads, and it can run the watermarking logic as code without managing servers. Transfer Family supports managed workflows that can orchestrate post-upload actions; using a Transfer Family workflow to invoke a Lambda function provides a clean, managed, event-driven pipeline: clients upload via SFTP → objects land in S3 → workflow triggers watermark processing → output is written back to S3.

Option A adds operational burden by requiring an EC2-based application tier for watermarking, which increases patching and scaling responsibilities. Option B can work (S3 + Batch), but it requires building and operating the upload mechanism (REST APIs) and typically more orchestration than necessary for simple “upload then process” flows; Batch is better when you need large-scale, long-running batch compute rather than lightweight per-object processing. Option D is unsuitable because S3 Glacier Deep Archive is intended for long-term archival with slow retrieval, which conflicts with active workflow processing and watermark automation.

Therefore, C best meets the requirements using managed services end-to-end: Transfer Family for ingestion, S3 for object storage, and Lambda for automated watermarking.

Apresigned URLallows temporary access to a specific object in an S3 bucket without needing to make the bucket public or creating and managing additional IAM users. The URL is time-limited, and permissions are granted only to the specific object (in this case, the annual report), making it a highly secure and operationally efficient solution.

With a presigned URL, the consultant can access the report for the specified duration (7 days), after which the URL will expire automatically, removing the need for manual intervention to revoke access.

AWS References:

Amazon S3 Presigned URLsexplain how to generate a presigned URL to grant temporary access to S3 objects.

Best Practices for S3 Securityemphasize using presigned URLs for sharing temporary access to S3 objects securely.

Why the other options are incorrect:

A. Public static website: This approach involves making the S3 bucket publicly accessible, which is unnecessary and insecure for sensitive data.

B. Enable public access: Granting public access to the entire bucket, even temporarily, is a security risk and violates best practices.

C. Create an IAM user: Creating an IAM user and managing credentials is unnecessary overhead and less secure compared to a presigned URL for this short-term need.

Amazon DynamoDB is a serverless, NoSQL database that is fully managed, highly available, and scales automatically. It is ideal for data without a fixed schema and for use cases where fields can vary by user. DynamoDB Streams enables the capture of changes to table items in real time, which is ideal for triggering additional processing or workflows on data modifications.

Reference Extract from AWS Documentation / Study Guide:

"DynamoDB provides a scalable, highly available NoSQL database service for applications requiring flexible schema. DynamoDB Streams captures table activity for processing changes in real time."

Source: AWS Certified Solutions Architect – Official Study Guide, DynamoDB and Serverless section.

Why Option D is Correct:

IAM Roles with Instance Profiles: Allow applications to access AWS services securely without hardcoding long-term access keys.

Short-Term Credentials: IAM roles issue short-term credentials dynamically managed by AWS.

Why Other Options Are Not Ideal:

Option A and B: Embedding or retrieving long-term access keys introduces security risks and operational overhead.

Option C: Combining IAM roles with Parameter Store adds unnecessary complexity.

AWS References:

IAM Roles and Instance Profiles:AWS Documentation - IAM Roles

To access an EFS file system across accounts and VPCs, the EFS must be mounted using VPC peering or AWS Transit Gateway, and the EC2 instances must use the amazon-efs-utils package with the correct mount target or access point.

Using an EFS access point simplifies access management, especially across accounts, by providing a POSIX identity and access policy layer.

VPC sharing doesn’t support EFS directly unless the subnet and resources are shared properly, which requires redeployment. Therefore, option D is the most complete and correct.

AWS provides EBS Block Public Access at the AWS Organizations level, which, when enabled, prevents EBS snapshots from being shared publicly across all member accounts. This is an organization-wide control that requires minimal configuration and no ongoing monitoring to prevent public sharing.

This directly satisfies the requirement:

Covers all accounts managed by Organizations.

Explicitly prevents public snapshot sharing.

Has the least operational overhead because it is a single centralized control.

AWS Config (Option A) only monitors and alerts, but does not prevent public sharing.

An IAM policy in the root account (Option C) is harder to enforce consistently across all accounts and may not cover all permission paths.

CloudTrail (Option D) only logs events and does not prevent public access.

Amazon QLDB is the most cost-effective and suitable service for maintainingimmutable, cryptographically verifiable logsof data changes. QLDB provides a fully managed ledgerdatabase with a built-in cryptographic hash chain, making it ideal for recording changes to accounting records, ensuring data integrity and security.

QLDB reduces operational overhead by offering fully managed services, so there’s no need for server management, and it’s built specifically to ensure immutability and verifiability, making it the best fit for the given requirements.

Option A (Redshift): Redshift is designed for analytics and not for immutable, cryptographically verifiable logs.

Option B (Neptune): Neptune is a graph database, which is not suitable for this use case.

Option C (Timestream): Timestream is a time series database optimized for time-stamped data, but it does not provide immutable or cryptographically verifiable logs.

AWS References:

Amazon QLDB

How QLDB Works

Using S3 event notifications to trigger AWS Lambda for file processing is a cost-effective and serverless solution. Lambda scales automatically with upload volume, and processing each file takes less than 5 seconds, fitting within Lambda's execution time.

Option A:AWS AppSync is designed for GraphQL APIs and is not suitable for file processing.

Option C:Kinesis is overkill and more expensive for this use case.

Option D:Running an EC2 instance incurs ongoing costs and is less flexible compared to Lambda.

AWS Documentation References:

Amazon S3 Event Notifications

AWS Lambda Overview

AWS KMS with SSE-KMS provides granular key management and auditability. All use of KMS keys is logged in AWS CloudTrail, which allows compliance teams to monitor encryption and decryption operations. A bucket policy can be configured to enforce uploads only with the designated KMS key, ensuring that users cannot bypass encryption or change methods. Option A (SSE-S3 with bucket policy) enforces encryption but does not provide the same level of control or auditable key usage. Option C (client-side encryption) increases complexity and key management burden. Option D prevents bucket setting changes but does not prevent unencrypted uploads. Therefore, B ensures the most granular control, auditability, and compliance with financial data requirements.

A. EventBridge rule:Triggers an event whenever there is a change in CloudFront distribution, ensuring real-time monitoring.

B. ALB with WAF:Focuses on application-level security, not CloudFront logging.

C. Lambda + SNS:Provides notifications upon detection of changes in logging configuration.

D. GuardDuty:Monitors anomalies but does not specifically address CloudFront logging changes.

E. Private API + WAF:Irrelevant to CloudFront logging changes.

For high availability, Amazon ElastiCache for Redis supports Multi-AZ with automatic failover, which provides primary and replica nodes in different Availability Zones. If the primary node fails, Redis automatically promotes a replica to primary.

From AWS Documentation:

“When you enable Multi-AZ with automatic failover, Amazon ElastiCache automatically detects failures and promotes a read replica to primary with minimal downtime.”

(Source: Amazon ElastiCache for Redis User Guide)

Why B is correct:

Multi-AZ provides automatic failover and data replication.

Ensures continuous availability and protects against node or AZ failures.

Fully managed with no manual intervention needed.

Why others are incorrect:

A: Manual promotion is not automatic.

C & D: Restoring from backup is slow and meant for disaster recovery, not failover.

Option B: FSx for Lustre is designed for HPC workloads with high IOPS.

Option E: A cluster placement group ensures low-latency networking for HPC analytics workloads.

Option A: Amazon EFS is not optimized for HPC.

Option D: Mountpoint for S3 does not meet high IOPS needs.

Amazon Kinesis Data Streams provides a highly scalable and durable service for ingesting real-time streaming data. By decoupling ingestion and processing, Kinesis can handle large spikes in traffic without service disruption. Lambda functions (or other consumers) can then process the data as it arrives, scaling automatically. This pattern avoids 503 errors due to compute saturation and delivers a resilient, serverless, and highly scalable architecture.

Reference Extract from AWS Documentation / Study Guide:

"Kinesis Data Streams provides a scalable and durable real-time data streaming service. Coupling Kinesis with AWS Lambda enables event-driven processing, elasticity, and decoupling between ingestion and processing layers."

Source: AWS Certified Solutions Architect – Official Study Guide, Streaming and Serverless section.

The recommended and simplest method for granting cross-account access to an S3 bucket is to use a bucket policy that grants permission to the other AWS account.

This provides direct, controlled, IAM-based access without duplication of data or use of distribution mechanisms like CloudFront.

Transfer Acceleration and replication are unrelated to permissions.

=====================================================

To securely access AWS services like S3 and Secrets Manager from a private VPC without using public IPs, interface VPC endpoints are required. These endpoints are accessible via private IP addresses. For the application to reach these endpoints, appropriate routes must be configured in the route table.

AWS WAF (Web Application Firewall): AWS WAF allows you to create custom rules to block or allow web requests based on conditions that you specify.

Web ACL (Access Control List):

Create a web ACL and associate it with the ALB.

Use IP rule sets to specify the IP addresses of the retail locations that are allowed to access the application.

Security and Flexibility:

AWS WAF provides a scalable way to manage access control, ensuring that only traffic from registered IP addresses is allowed.

You can dynamically update the IP rule sets to add or remove IP addresses as needed.

Operational Simplicity: Using AWS WAF with a web ACL is straightforward and integrates seamlessly with the ALB, providing an efficient solution for managing access control based on IP addresses.

Amazon S3 Intelligent-Tiering is the most cost-effective solution for this scenario, providing both high availability and durability while adjusting automatically to changing access patterns. It moves data across two access tiers: one optimized for frequent access and another for infrequent access, based on usage patterns. This tiering ensures that the company avoids paying for unused storage while also keeping frequently accessed data in a more accessible tier.

Key AWS references and benefits ofS3 Intelligent-Tiering:

High Durability and Availability: Amazon S3 offers 99.999999999% durability and 99.9% availability for objects stored, ensuring data is always protected.

Automatic Tiering: Data is automatically moved between tiers based on access patterns, making it ideal for workloads with unpredictable or variable access patterns.

No Retrieval Fees: Unlike S3 One Zone-IA or Glacier, there are no retrieval fees, making this more cost-effective in scenarios where access patterns vary over time.

AWS Documentation: According to the AWS Well-Architected Framework under theCost Optimization Pillar, S3 Intelligent-Tiering is recommended for storage when access patterns change over time, as it minimizes costs while maintaining availability​.

Since the application uses long-lived TCP connections and must remain idle for up to 1 hour without timeout, all components involved in the connection path (ALB, GWLB, and firewall) must have their idle timeout values configured to at least 1 hour.

Gateway Load Balancer supports transparent insertion of firewalls, and configuring consistent idle timeouts ensures connections don’t drop mid-session.

Using just one firewall (option B) introduces a single point of failure. Increasing capacity (option D) doesn't solve idle timeout issues. Therefore, C provides a resilient and complete configuration.

Amazon S3 Express One Zone provides single-digit millisecond latency and high throughput, making it ideal for ML workloads that require multiple compute nodes and fast access. It is also more cost-effective than traditional file or block storage for temporary, high-speed needs.

Option B:A gateway endpoint for S3 allows traffic to S3 without using public IPs and integrates with route tables.

Option D:Deploying EC2 instances in a private subnet with a NAT gateway enables outbound internet connectivity for other requirements without public IPs.

Option A:Egress-only internet gateways are for IPv6 traffic and do not work for IPv4 in this context.

Option C:Interface endpoints are not required for S3 as gateway endpoints are more suitable and cost-effective.

Option E:A customer gateway is for hybrid connectivity (e.g., on-premises), not suitable for this case.

AWS Documentation References:

VPC Endpoints

Amazon S3 Gateway Endpoints

Option A: Importing customer-managed keys into AWS KMS ensures that encryption key material remains under the company’s control.

Option D: S3 Glacier with server-side encryption using customer-provided keys (SSE-C) complies with the need for controlled encryption and provides cost-effective storage for backups.

AWS Key Management Service Importing Keys Documentation,S3 Encryption Documentation

Amazon Neptune is a fully managed graph database service optimized for storing and navigating complex many-to-many relationships like social graphs or network topologies.

It supports Gremlin and openCypher queries that allow efficient traversal of connections even multiple levels deep. SQL JOINs (Athena or RDS) are inefficient for deep relationship queries due to exponential complexity.

QuickSight is used for data visualization, not graph traversal.

AmazonEventBridgeAPI destinations allow you to send data from AWS to external systems, like Salesforce, using HTTP APIs, including those secured with OAuth. This provides a secure and scalable solution for sending messages from the order processing application to Salesforce.

Option A and B (SNS): SNS is not ideal for OAuth-secured external APIs and lacks the necessary OAuth integration.

Option D (MSK): Amazon MSK is a Kafka-based streaming solution, which is overkill for simple message forwarding to Salesforce.

AWS References:

Amazon EventBridge API Destinations

Same scenario as above—cost-effective cold start reduction for Java Lambdas is best achieved by using Lambda SnapStart with the correct code structure. Unique IDs and similar non-deterministic logic should be in the handler or in a pre-snapshot hook. SnapStart is supported only for published versions. This combination offers cold start performance at a lower cost than provisioned concurrency.

AWS Documentation Extract:

"When using Lambda SnapStart for Java, ensure non-deterministic initialization code, such as unique ID generators, is executed inside the handler or in a pre-snapshot hook. SnapStart is supported only on published versions."

(Source: AWS Lambda documentation, SnapStart for Java)

Amazon RDS Proxy helps manage and pool database connections from serverless compute like AWS Lambda, significantly reducing the stress on the database during unpredictable traffic surges. It improves scalability and resiliency by efficiently managing connections, protecting the database from being overwhelmed, and enabling failover handling.

Option A is the most cost-effective and operationally efficient approach to handling unpredictable surges and improving resilience without requiring major application changes.

Option B involves a migration to DynamoDB, which is a significant architectural change and costlier initially. Option C is manual connection cleanup, insufficient for unpredictable surges. Option D increases resources but does not solve connection storm problems efficiently and is more costly.

AWS Config is a service designed to assess, audit, and evaluate the configurations of AWS resources. It continuously monitors and records your AWS resource configurations and allows you to automate the evaluation of recorded configurations against desired configurations. By starting a configuration recorder, AWS Config will capture changes to supported resource types as configuration items—without the need to modify any of the existing resources. This provides a full history of configuration changes and is specifically intended for exactly this use case.

AWS Documentation Extract:

“AWS Config provides a detailed view of the configuration of AWS resources in your AWS account. This includes how the resources are related to one another and how they were configured in the past so you can see how the configurations and relationships change over time.”

“You can start the configuration recorder, which will record the configuration changes of the supported resources in your AWS account.”

(Source: AWS Config documentation, What is AWS Config?)

Other options:

B: CloudFormation drift detection only works for resources created and managed by CloudFormation and requires stacks.

C: Amazon Detective is used for analyzing and investigating security findings, not for resource configuration tracking.

D: AWS Audit Manager is used for automating evidence collection to help with audits, not for tracking resource configurations.

Amazon MSK is a fully managed, highly available Apache Kafka service for streaming data with low latency. Kafka Connect and stream processors enable ingest from heterogeneous sources and perform in-stream transformation before delivery to consumers (e.g., the dashboard service). This satisfies real-time updates from diverse schemas and formats. Kinesis alternatives could work, but among the given choices, MSK is the only streaming option designed for sub-second, continuous pipelines. Kinesis Data Firehose (B) buffers and batches data to S3 and is optimized for delivery to storage, not low-latency dashboards. AWS DMS schema conversion (C) focuses on database migration, not ongoing real-time, multi-format streaming for dashboards. AWS DataSync (D) is for file/object transfer, not database change streams. Hence, MSK best meets minimal-latency, transform-in-flight needs with managed operations.

The AWS Cost and Usage Report (CUR) delivers detailed, line-item billing data to Amazon S3. AWS recommends querying CUR with Amazon Athena by creating external tables over the CUR S3 location (partitioned by time) to produce daily cost aggregations such as NAT Gateway (EC2:NatGateway) usage and cost. Amazon QuickSight natively connects to Athena as a data source to build and share dashboards with visuals (tables, time series) filtered from the start of the current month. DataSync (A, C) is a file transfer service and cannot query data. CloudWatch dashboards (C, D) visualize metrics/logs, not CUR datasets. Therefore, using Athena to query CUR and QuickSight to present a daily NAT gateway cost dashboard is the most direct and operationally efficient approach.

AWS Certificate Manager (ACM) issues and automatically renews public TLS certificates used by Amazon CloudFront. With DNS validation, ACM creates CNAME records that prove domain control; once validated, renewals occur automatically without manual approval, providing the lowest operational effort. For CloudFront, ACM certificates must be in the US East (N. Virginia) Region. CloudFront security policies (A) configure protocol/cipher requirements, not certificate issuance. OAC (B) secures origin access and is unrelated to certificate management. Email validation (D) requires mailbox approvals and operational handling at issuance/renewal, which is less efficient than DNS validation. Thus, ACM with DNS validation delivers automated creation and renewal with minimal overhead.

For globally distributed consumers of REST APIs, API Gateway edge-optimized endpoints “route requests to the nearest CloudFront edge location, which then forwards them to your API in the [home] Region,” reducing latency for users worldwide and scaling automatically for unknown or spiky traffic. By contrast, Regional endpoints are intended for clients within the same or nearby Region and do not provide global edge acceleration. AWS Lambda provides automatic scaling for serverless backends; latency can be further reduced by right-sizing memory (which also increases CPU) and, when needed, enabling provisioned concurrency to keep functions initialized and eliminate cold starts for critical paths. Using NLBs across Regions is not serverless and adds operational complexity without CloudFront edge acceleration. Therefore, combining API Gateway edge-optimized REST APIs with Lambda meets the requirements of minimal global latency and unknown initial traffic.

Comprehensive and Detailed Step-by-Step Explanation:

The goal is totransfer 500 GB dailyfrom multiple global locationsquicklyintoa single S3 bucketwhile keeping operational complexity low.

Option A:✅

Turn on S3 Transfer Acceleration on the destination S3 bucket. Use multipart uploads to directly upload site data to the destination S3 bucket.

S3 Transfer Acceleration (S3-TA)allowsfasterglobal uploads by routing traffic throughAmazon CloudFront’s globally distributed edge locations.

Multipart uploadsimprove efficiency bybreaking large filesinto smaller parts, transferring them in parallel.

Low operational complexity: No need for additional resources or manual replication.

Why is this best?It ensureshigh-speed transferswhileminimizing complexity.

Serving static content from Amazon S3 is highly cost-effective and scalable. By fronting the S3 bucket with Amazon CloudFront, you also enable global caching, reduced latency, and even lower costs by reducing direct S3 access. There is no need for EC2 or ALB, and no need to manage servers, further lowering operational burden and expenses. This pattern is the recommended AWS architecture for serving static web content.

Reference Extract from AWS Documentation / Study Guide:

"Hosting static websites on Amazon S3 with Amazon CloudFront reduces infrastructure costs and improves performance for global users by caching content at edge locations."

Source: AWS Certified Solutions Architect – Official Study Guide, S3 and CloudFront section.

The best practice for granting AWS resource access to EC2 instances is to use IAM roles, not users or long-lived access keys. You create an IAM role with a policy that grants the minimum permissions required, then attach that role to an instance profile associated with the EC2 instance. The instance then automatically receives temporary credentials for AWS service access.

AWS Documentation Extract:

"Attach an IAM role to your EC2 instances to securely grant permissions to AWS services according to the principle of least privilege. Never embed access keys in EC2 instances."

(Source: AWS EC2 documentation, IAM Roles for EC2)

A, B: Using IAM users and embedding keys violates security best practices.

C: IAM role credentials are automatically managed and never need to be manually attached as keys.

DynamoDB export to Amazon S3 (via point-in-time exports or table backups) “does not consume read capacity” and has no impact on table performance. Once data is in S3, Amazon Redshift can ingest efficiently with COPY from S3, which is the standard, parallel, high-throughput load path with minimal management. Direct COPY from DynamoDB (B) performs a table scan that can consume provisioned throughput, risking throttling. Building Lambda pipelines (C) adds custom code and scheduling overhead. Athena federated queries (D) are ad hoc analytics, not optimized for bulk, recurring warehouse loads. Therefore, exporting to S3 and loading with Redshift COPY maintains DynamoDB throughput and minimizes operational burden.

Pre-Signed URLs: Allow temporary access to S3 buckets, making it easy to manage time-limited upload permissions without complex operational overhead.

Lambda for Automation: Automatically generates and provides pre-signed URLs when users authenticate, minimizing manual steps and code complexity.

Least Operational Overhead: Requires no custom authentication service or deep integration with STS or Cognito.

Amazon S3 Pre-Signed URLs Documentation

Amazon SQS provides durable, decoupled message storage for distributed systems. Using SQS as a job queue enables each EC2 instance to process a message independently. Scaling the Auto Scaling group based on the SQS queue length ensures parallelism and elasticity, aligning compute resources with workload volume.

Amazon Athenais a serverless query service that allows you to run SQL queries directly on data stored in Amazon S3 without the need for a data warehouse. It is cost-effective because you only pay for the queries you run, and it can handleApache Parquetfiles efficiently. Additionally, Athena integrates with KMS, making it suitable for querying encrypted data.

Key AWS features:

Cost-Effective: Athena charges only for the data scanned by the queries, making it a more cost-effective solution compared to Redshift or EMR for occasional queries.

Direct S3 Querying: Athena supports querying data directly in S3, including Parquet files, without needing to move the data.

AWS Documentation: Athena's compatibility with encrypted Parquet files in S3 makes it the ideal choice for this scenario, reducing both cost and complexity​​.

Amazon GuardDuty includes RDS Protection that “monitors and profiles access to your Amazon Aurora and Amazon RDS databases” to detect threats such as suspicious login attempts, brute-force activity, and anomalous authentication patterns. GuardDuty can be enabled organization-wide in AWS Organizations with a delegated administrator to centralize findings for all member accounts, minimizing operational overhead. Findings include context like source IP, user, and DB instance, and integrate with Amazon EventBridge for alerting and automated response. SCPs (A) enforce or deny API permissions but do not provide detection/analytics. Exporting general logs (C) requires building and maintaining custom parsing/analytics pipelines. CloudTrail (D) records AWS control-plane API calls and does not log database-level login attempts. Therefore, enabling GuardDuty RDS Protection across the org provides the most operationally efficient, managed detection of abnormal failed and incomplete login attempts.

End-to-end encryption means TLS is used not only from the client to the ALB, but also from the ALB to the EC2 targets. The least operational effort is achieved by using AWS Certificate Manager (ACM) Amazon-issued certificates where possible, because ACM automates key management, certificate provisioning, and renewal for supported use cases. Associating an ACM certificate with the ALB is a standard managed approach for TLS termination at the load balancer, and using managed certificates reduces the overhead of tracking expiration, renewing, and distributing certificates.

For encryption on the backend connection (ALB to instances), the instances must present a certificate and complete TLS. Using Amazon-issued ACM certificates (via supported mechanisms) reduces manual certificate lifecycle work compared with importing and managing third-party certificates. By avoiding third-party cert procurement and manual renewals, the solution minimizes ongoing operations and reduces the risk of outages due to expired certificates. This aligns with the requirement for the least operational effort while meeting the security requirement.

Option A is far more operationally heavy: CloudHSM is intended for scenarios requiring customer-managed HSMs and adds complexity in deployment, scaling, integration, and operations. Option B introduces self-signed certificates on instances, which increases operational friction (distribution, trust, rotation) and is not as clean for managed operations. Option C works but requires managing third-party certificate lifecycle (renewals, re-import, redeploy to instances), which is more overhead than Amazon-issued managed certificates.

Therefore, D best meets end-to-end encryption needs with the lowest ongoing operational burden by using AWS-managed certificate issuance and lifecycle management capabilities.

Comprehensive and Detailed Step-by-Step Explanation:

The requirement is toprevent Application A from sending traffic to Application B.

Understanding AWS Network Security Components:

Security Groups

Stateful(if traffic is allowed in one direction, it is automatically allowed in the reverse).

Do not support explicit deny rules, onlyallow rules.

Not suitable for blocking traffic in this scenario.

Network ACLs (NACLs)

Stateless(must define explicit rules for both inbound and outbound traffic).

Support explicit DENY rules.

Best suited for blocking traffic between subnets.

Analysis of the Options:

Option A: Deny Outbound Rule in Security Group for Application B❌(Incorrect)

Security Groups do not support explicit deny rules.

Does not block traffic from Application A to Application B.

Option B: Deny Outbound Rule in Security Group for Application A❌(Incorrect)

Security Groups do not support explicit deny rules.

Cannot effectively prevent Application A from sending traffic to Application B.

Option C: Deny Outbound Rule in NACL for Application B Subnet❌(Incorrect)

This wouldprevent Application B from sending traffic, butthe requirement is to block traffic from Application A to Application B.

Incorrect subnet is being modified.

Option D: Deny Outbound Rule in NACL for Application A Subnet✅(Correct Choice)

Prevents Application A from sending traffic to Application B by blocking outbound requests at the network level.

Effectively stops communication from A to B at the subnet level.

Why Option D is the Best Choice?

✅NACLs support explicit deny rules, unlike security groups.✅Blocks outbound traffic from Application A before it reaches Application B.✅Works at the subnet level, making it scalable.

For maximum resiliency and fault tolerance in a mission-critical scenario, AWS recommends redundant Direct Connect connections from multiple data centers to multiple AWS Direct Connect locations.

This protects against:

Data center failure

Device failure

Location outages

“For workloads that require high availability, we recommend that you use multiple Direct Connect connections at multiple Direct Connect locations.”

— AWS Direct Connect Resiliency Recommendations

Option C follows AWS maximum resiliency model.

This solution directly addresses the scalability and high availability requirements with minimal downtime.

Additional Reader Instances: Adding more reader instances to the Aurora cluster will distribute the read load, improving the performance of the application under heavy read traffic. Aurora reader instances automatically replicate the data from the writer instance, enabling you to scale out read operations.

Amazon RDS Proxy: RDS Proxy improves database availability by managing database connections more efficiently and providing a connection pool. This reduces the overhead on the Aurora cluster during peak loads, further enhancing performance and availability without requiring changes to the application code.

Why Not Other Options?:

Option A (EventBridge and Lambda): This doesn’t directly address the performance and availability issues. Logging state changes and adding reader nodes on failure events doesn’t provide proactive scalability.

Option B (Zero-Downtime Restart and Activity Streams): Zero-Downtime Restart (ZDR) is useful for minimizing downtime during maintenance but doesn’t directly improve scalability. Database Activity Streams are more for security monitoring than for performance enhancement.

Option D (ElastiCache for Redis): While adding a caching layer can help with read performance, it introduces complexity and may not be necessary if additional reader instances can handle the load.

AWS References:

Amazon Aurora Scaling- Information on scaling Aurora clusters with reader instances.

Amazon RDS Proxy- Details on how RDS Proxy can improve database performance and availability.

AWS Site-to-Site VPN: This provides a secure and encrypted connection between an on-premises environment and AWS. It is a cost-effective solution suitable for low bandwidth and small traffic needs.

Quick Setup:

Site-to-Site VPN can be quickly set up by configuring a virtual private gateway on the AWS side and a customer gateway on the on-premises side.

It uses standard IPsec protocol to establish the VPN tunnel.

Cost-Effectiveness: Compared to AWS Direct Connect, which requires dedicated physical connections and higher setup costs, a Site-to-Site VPN is less expensive and easier to implement for smaller traffic requirements.

Amazon Route 53 supports failover routing policies, which use health checks to route DNS queries to a secondary Region only if the primary endpoint fails. This design ensures only one Region is active for traffic at any given time. This is the recommended architecture for active-passive, multi-Region DR strategies.

AWS Documentation Extract:

“Failover routing lets you route traffic to a primary resource, such as a web server in one Region, and a secondary resource in another Region. If the primary fails, Route 53 can route traffic to the secondary resource automatically.”

(Source: Amazon Route 53 documentation, Routing Policy Types)

A, D: These options do not configure DNS failover for external users.

C: Geolocation routing is for regional distribution, not DR failover.

For the relational database tier, Amazon RDS Multi-AZ provides high availability with minimal manual intervention. In Multi-AZ mode, RDS maintains a synchronous standby in a different Availability Zone and provides automatic failover during certain failure scenarios. This reduces operational burden because the service handles replication, health monitoring, and failover orchestration.

For the container-based application tier, Amazon ECS with the Fargate launch type minimizes operational overhead because it removes the need to provision, patch, and scale the underlying EC2 instances that host containers. With Fargate, AWS manages the compute infrastructure, and the team focuses on task definitions, scaling policies, and application configuration. This supports a highly available, load-balanced architecture because ECS services can run tasks across multiple Availability Zones behind an Application Load Balancer, and scaling is managed via ECS Service Auto Scaling.

Option B describes read replicas, which are primarily used for scaling reads and, depending on configuration, may not provide the same automated failover characteristics as Multi-AZ for high availability. Read replicas are not a direct substitute for Multi-AZ HA. Option C (self-managed Docker cluster on EC2) is operationally heavy: you must manage node capacity, patching, cluster lifecycle, and scheduling. Option E (ECS on EC2) is a valid container platform but still requires managing EC2 instances, AMIs, and scaling/patching, which increases manual intervention compared to Fargate.

Therefore, combining RDS Multi-AZ (A) for database resilience and ECS Fargate (D) for serverless container operations meets the HA requirement with the least manual effort.

Amazon SQS FIFO queuesensure that orders are processed in the exact order received and maintain message deduplication.

AWS Lambdascales automatically, handling bursts and maintaining high availability in a cost-effective manner.

Option A and D:Amazon SNS does not guarantee ordered processing.

Option C:Standard SQS queues do not guarantee order.

AWS Documentation References:

Amazon SQS FIFO Queues

AWS recommends using cost allocation tags to associate costs with owners (e.g., department, project) and AWS Budgets to create cost or usage budgets with alerts at defined thresholds (e.g., 60%). Budgets can send notifications via email or Amazon SNS when “Actual or forecasted” spend exceeds the threshold. Cost Explorer forecasts help visualize trends but do not provide budget alerting or ownership attribution by themselves. Trusted Advisor does not generate budget threshold alerts. Therefore, tagging resources for accountability and setting a budget with a 60% alert provides governance and proactive notifications aligned with cost control best practices for experimentation.

Amazon EventBridge supports routing application-generated events to AWS Lambda targets. The Lambda function can process and insert events into a DynamoDB table. This is a standard design pattern for connecting event-driven applications with DynamoDB. Option A confuses DynamoDB Streams (which streams changes from DynamoDB) with EventBridge (which is for event routing). Options C and D reference partner event buses, which are intended for SaaS integrations, not custom application events. Therefore, the correct and simplest solution is to use a custom EventBridge event bus with a Lambda target (B).

The requirements specify capturing unpredictable and sudden spikes in customer activity, integrating easily with other web applications, and including authorization.

Amazon API Gateway with Lambda authorizer provides a secure, scalable entry point with flexible authorization mechanisms including token validation.

Amazon Kinesis Data Firehose is a fully managed service to reliably load streaming data into destinations such as Amazon S3, which fits well for capturing streaming customer activity data.

API Gateway integrates natively with Firehose for direct ingestion.

This combination supports unpredictable traffic, smooth scaling, and simple authorization.

Option B uses Kinesis Data Streams, which requires more management than Firehose and is less optimized for direct API integration. Options A and D use Gateway Load Balancer and ECS containers plus EFS, which add complexity and are less suited for unpredictable traffic with integrated authorization.

Amazon Route 53 Multivalue Answer Routing: This routing policy allows Route 53 to return multiple values, such as IP addresses, in response to DNS queries. This can distribute traffic across multiple resources and includes health checks to ensure traffic is only routed to healthy instances.

Health Checks:

Configure health checks for each Region to monitor the health of the website instances.

Route 53 will only include healthy instances in the DNS responses, ensuring that traffic is not routed to an unhealthy Region.

Load Distribution and Disaster Recovery:

Multivalue answer routing helps balance the load between instances in different Regions.

If instances in one Region become unhealthy, Route 53 will route traffic to the healthy instances in the other Region.

Operational Simplicity: This solution does not require complex configurations or additional resources, making it a simple yet effective way to distribute traffic and ensure high availability.

DynamoDB Global Tablesprovide a fully managed, multi-region, and multi-master database solution that allows you to deploy DynamoDB tables in multiple AWS Regions. This ensures high availability and resiliency across different geographical locations, providing a seamlessgaming experience for users. Usingprovisioned capacity modewithauto-scalingensures cost-efficiency by scaling up or down based on actual demand.

Option A: While on-demand capacity mode is flexible, provisioned capacity with auto-scaling is more cost-effective for predictable workloads.

Option B (DAX): DAX improves read performance, but it doesn't provide the multi-region replication needed for high availability and resiliency.

Option C: DynamoDB Streams with manual cross-region replication adds more complexity and operational overhead compared to Global Tables.

AWS References:

DynamoDB Global Tables

AWS Backup is a fully managed backup service that can create backup plans for EC2 instances, including both instance configurations and attached EBS volumes, with scheduled and cross-Region copy capabilities. By adding the EC2 instances to the resource assignment in the backup plan, AWS Backup automatically backs up all configurations and attached EBS volumes, and can copy backups to a secondary Region for disaster recovery, providing the highest operational efficiency with the least manual effort.

AWS Documentation Extract:

“AWS Backup provides fully managed backup for EC2 instances and attached EBS volumes, with scheduling, retention, and cross-Region copy built in. By adding the EC2 instance as a resource, the backup includes both configuration and attached volumes.”

(Source: AWS Backup documentation)

A, D: Custom Lambda scripts increase operational overhead and are not as integrated or robust as AWS Backup.

C: Assigning only EBS volumes does not include the EC2 instance configuration, which is needed for full recovery.

AWS Step Functions supports long-running workflows and error retries, making it ideal for a job composed of many tasks. Integration with EventBridge allows automatic triggering from S3 events. This setup is resilient and supports up to 1-year execution duration.

AWS documentation states that workloads running 24/7 should use Reserved Instances or Savings Plans for the lowest cost. Therefore, the frontend nodes, which always run, should use Reserved Instances.

The backend nodes process asynchronous, restartable jobs, which makes them ideal for EC2 Spot Instances, the most cost-effective compute option for interruption-tolerant workloads.

Fargate (Options A and D) is significantly more expensive for large compute usage. Spot Instances cannot be used for critical frontend nodes (Option C).

=====================================================

The IAM policy includes two statements:

1️⃣ Allow ec2:TerminateInstances on all resources

2️⃣ Deny ec2:TerminateInstances if the request does NOT come from:

192.0.2.0/24

203.0.113.0/24

AWS IAM policy evaluation rules state:

Explicit Deny always overrides Allow.

A request must meet all applicable conditions in the policy to be granted.

aws:SourceIp condition applies when IAM actions are invoked via the public AWS APIs.

In this scenario, the administrator is not originating the request from one of the allowed CIDR blocks, so the Deny condition is triggered, overriding the Allow statement.

Therefore, the operation is blocked with:

403 AccessDenied: explicit deny

This matches the behavior described in the AWS IAM Policy Evaluation Logic used in the Security Pillar of the Well-Architected Framework:

Explicit Deny > Allow > Default Deny

IP-based Conditions restrict API calls originating outside approved network ranges

Options A/B/C do not accurately reflect this explicit Deny condition behavior.

Using API Gateway and Lambda enables serverless handling of form submissions with minimal cost and infrastructure. When coupled with Amazon SNS, it allows instant email notifications without running servers, making it ideal for low-traffic workloads.

To ensure container images are secure and to notify administrators about vulnerabilities, the solution needs (1) a vulnerability scanning capability for container images and (2) a notification mechanism that alerts when findings occur. Amazon Inspector provides automated security assessments and can scan container images stored in Amazon ECR to identify software vulnerabilities and unintended network exposure patterns, producing findings that can be acted upon. Therefore, D addresses the core requirement of detecting vulnerabilities in container images.

To notify administrators with minimal custom work, AWS Config can help by evaluating resources against desired configurations and integrating with notifications through AWS services (for example, via Amazon SNS using Config rules/conformance packs). Using the AWS Config conformance pack for Amazon ECS helps establish a managed set of compliance checks aligned to ECS-related best practices. While Inspector is the system that detects vulnerabilities, Config can be used to enforce and monitor governance controls around the container environment and can trigger notifications when noncompliance is detected. In exam patterns, pairing an Inspector detection capability with a managed governance/notification framework like Config is a common “two-part” answer.

The other options do not meet the requirement: A (privileged mode) can increase risk rather than improve image security; logging does not equal vulnerability detection. C is unrelated because AWS WAF protects web applications at the edge and does not scan container images for CVEs. E is incorrect because Parameter Store stores configuration data and secrets; it does not encrypt container images (ECR encryption at rest is handled by AWS-managed mechanisms and KMS integration, not Parameter Store).

So D provides scanning and B supports managed compliance/notification controls with low operational overhead.

AWS Lake Formation provides centralized data access control, including fine-grained (column-level) permissions for data stored in S3 and accessed through services like Amazon Athena.

Using a Lake Formation blueprint to ingest data from Aurora MySQL into the data lake keeps ingestion and governance integrated. When QuickSight uses Athena as the data source, Athena enforces Lake Formation’s column-level permissions automatically. This allows the marketing team to see only the authorized subset of columns without custom access-control logic.

Options A, B, and C rely on manually limiting columns at ingestion time or using IAM or S3 bucket policies, which do not provide true column-level authorization for SQL queries and require significantly more manual work and maintenance.

Concurrency Scaling allows Amazon Redshift to add transient capacity automatically to handle bursts in concurrent queries. This is ideal for scenarios with predictable surge periods, such as end-of-month reporting.

It improves performance without manual resizing or cluster modification and without affecting availability. Resizing requires manual intervention and potential downtime, and Redshift Spectrum is designed for querying data in S3, not for solving concurrency issues.

For processing thousands of images and generating large files while minimizing manual tasks and operational overhead, usingAWS Batchis the best solution. AWS Batch allows you to run large-scale, parallel, and managed batch computing jobs without needing to manage the underlying infrastructure.

AWS Batch: Automates the image processing jobs, dynamically allocating the necessary resources based on the job requirements, which reduces operational overhead.

AWS Step Functions: Orchestrates the entire image processing workflow, ensuring that tasks are executed in the correct sequence, improving manageability.

Amazon S3: Stores the processed files, providing scalable and cost-effective storage.

Option A (ECS with EC2 Spot Instances): While cost-effective, managing ECS and Spot Instances involves more operational effort.

Option C (Lambda with EC2 Spot): Lambda functions have size and duration limitations, making them less suited for large image processing tasks.

Option D (EC2 with Step Functions): Managing EC2 instances involves more overhead than using AWS Batch.

AWS References:

AWS Batch

AWS Step Functions

AWS Storage Gateway Tape Gateway provides a seamless way to migrate backup data to AWS without requiring changes to the backup software. Migrating to S3 Glacier Deep Archive ensures long-term, cost-effective storage for data that rarely needs retrieval.

Option A:S3 Standard-IA is more expensive than Glacier for long-term storage.

Option B and D:Glacier Flexible Retrieval is costlier than Glacier Deep Archive for archival use cases with low retrieval frequency.

AWS Documentation References:

AWS Storage Gateway Tape Gateway

S3 Glacier Storage Classes

This solution addresses the scalability needs of the workload while preventing failed processing attempts due to resource constraints.

Amazon S3 event notificationscan be used to trigger a message to an SQS queue whenever a new video is uploaded.

The existing Auto Scaling group of EC2 instances can poll the SQS queue, ensuring that the EC2 instances only process videos when there is a job in the queue.

SQS decouplesthe video upload and processing steps, allowing the system to handle sudden spikes in video uploads without overloading EC2 instances.

The use ofAuto Scalingensures that the EC2 instances can scale in or out based on the demand, maintaining cost efficiency while avoiding processing failures due to insufficient resources.

AWS References:

S3 Event Notificationsdetails how to configure notifications for S3 events.

Amazon SQSis a fully managed message queuing service that decouples components of the system.

Auto Scaling EC2explains how to manage automatic scaling of EC2 instances based on demand.

Why the other options are incorrect:

A. AWS Lambda functions: While Lambda can handle some workloads, video processing is often resource-intensive and long-running, making EC2 a more suitable solution.

B. Using SNS with Lambda: Similar to A, Lambda is not ideal for large-scale video processing due to its time and memory limitations.

D. AWS Step Functions: While a valid orchestration solution, this introduces more complexity and overhead compared to the simpler SQS-based solution.

Creating aVPC endpointfor S3 and configuring abucket policyto allow access only from the endpoint ensures that only EC2 instances within the VPC can access the S3 bucket. This solutionimproves security by restricting access at the network level without the need for public internet access.

Option A (IAM policies): IAM policies alone cannot restrict access based on the network location.

Option B and D (Encryption): Encryption secures data at rest but does not restrict network access to the bucket.

AWS References:

Amazon S3 VPC Endpoints

A. EC2 with EBS:Does not support SQL Server Always On availability groups effectively.

B. RDS Multi-AZ:Provides high availability but does not support SQL Server Always On availability groups.

C. EC2 with FSx for Windows:Best solution for SQL Server Always On as FSx provides shared storage compatible with SQL Server clustering.

D. EC2 with S3:S3 is not suitable for SQL Server storage.

By exporting AWS Cost and Usage Reports (CUR) to Amazon S3 and analyzing them with Amazon QuickSight, companies can generate interactive visual dashboards. This solution is fully AWS-managed, requires no third-party tools, and integrates deeply with AWS cost data.

The most secure and least operationally intensive method is to configure cross-account access using resource-based policies on the S3 bucket. This allows trusted external AWS accounts (consulting agencies) to securely access the S3 data without the need to manage user credentials or build additional infrastructure.

Options A and B pose security risks. Option D increases operational complexity and violates least privilege by managing external users inside your AWS account.

To build a distributed, serverless, event-driven workflow with minimal operational overhead, AWS Step Functions orchestrating AWS Lambda is the best fit. Step Functions provides a managed state machine service that coordinates multiple steps, handles retries and error handling patterns, and maintains execution state without requiring the company to run orchestration servers. Lambda provides serverless compute for each workflow task, scaling automatically with demand and eliminating server management.

Option D directly matches “performing different aspects of the workflow” because Step Functions is specifically designed to model multi-step business processes and coordinate activities across services. It also aligns with “minimize operational overhead” because both Step Functions and Lambda are managed services: there are no instances to patch, no cluster management, and scaling is handled by the platform.

Option B contradicts the serverless goal by deploying the application on EC2 instances, increasing operational overhead (capacity management, patching, scaling, and availability concerns). Option C mixes eventing with scheduling: EventBridge can route events, but invoking Lambda “on a schedule” is not the same as orchestrating a multi-step workflow with branching, waiting, retries, compensation, and state tracking. EventBridge is excellent as an event bus, but Step Functions is the purpose-built workflow orchestrator. Option A is a mismatch because AWS Glue is primarily an ETL/data integration service; while it can run jobs and triggers, it is not the general workflow orchestrator for arbitrary application steps, and using it to invoke Lambda for a broad workflow is less direct and typically less operationally clean than a Step Functions state machine.

Therefore, D is the most aligned solution for serverless, distributed workflow orchestration with low operational burden.

AWS Shield Advanced provides advanced DDoS protection for AWS workloads, including EC2, CloudFront, and RDS. When integrated with CloudFront, Shield Advanced offers comprehensive detection and mitigation against large and sophisticated DDoS attacks, along with 24x7 access to the AWS DDoS Response Team (DRT). AWS WAF provides application-level protection, but for complete DDoS mitigation, Shield Advanced is the recommended solution.

Reference Extract from AWS Documentation / Study Guide:

"AWS Shield Advanced provides expanded DDoS attack protection for applications running on AWS. It offers always-on detection and automatic inline mitigations that minimize application downtime and latency."

Source: AWS Certified Solutions Architect – Official Study Guide, Security and DDoS Protection section.

Deploying the web application on EC2 instances in private subnets behind an internal ALB ensures that the application is not directly accessible from the internet. By setting up a Site-to-Site VPN connection, the application can be accessed securely from the company's office network. Deploying NAT gateways in public subnets allows instances in private subnets to initiate outbound connections to the internet for downloading security patches.

This solution provides the most secure access for external support engineers with the least exposure to potential security risks.

AWS Systems Manager (SSM) and Session Manager: Systems Manager Session Manager allows secure and auditable access to EC2 instances without the need to open inbound SSH ports or manage SSH keys. This reduces the attack surface significantly. The SSM Agent must be installed and configured on all instances, and the instances must have an instance profile with the necessary IAM permissions to connect to Systems Manager.

IAM Identity Center: IAM Identity Center provides centralized management of access to the AWS Management Console for external support engineers. By using IAM Identity Center, youcan control console access securely and ensure that external engineers have the appropriate permissions based on their roles.

Why Not Other Options?:

Option B (Local IAM user credentials): This approach is less secure because it involves managing local IAM user credentials and does not leverage the centralized management and security benefits of IAM Identity Center.

Option C (Security group with SSH access): Allowing SSH access opens up the infrastructure to potential security risks, even when restricted by IP addresses. It also requires managing SSH keys, which can be cumbersome and less secure.

Option D (Bastion host): While a bastion host can secure SSH access, it still requires managing SSH keys and opening ports. This approach is less secure and more operationally intensive compared to using Session Manager.

AWS References:

AWS Systems Manager Session Manager- Documentation on using Session Manager for secure instance access.

AWS IAM Identity Center- Overview of IAM Identity Center and its capabilities for managing user access.

AWS Lambda for Operational Efficiency:

AWS Lambdais a serverless compute service that allows you to run code without provisioning or managing servers. It automatically scales based on the number of invocations and eliminates the need to maintain and monitor EC2 instances, making it far more operationally efficient compared to running a cron job on EC2.

By using Lambda, you pay only for the compute time that your function uses. This is especially beneficial when dealing with lightweight tasks, such as scanning a DynamoDB table and sending email messages once a day.

Amazon DynamoDB:

DynamoDBis a highly scalable, fully managed NoSQL database. The table stores employee start dates, and scanning the table to find the employees who have a work anniversary on the current day is a lightweight operation. Lambda can easily perform this operation using theDynamoDB Scan APIor queries, depending on how the data is structured.

Amazon SNS for Email Notifications:

Amazon Simple Notification Service (SNS)is a fully managed messaging service that supports sending notifications to a variety of endpoints, including email. SNS is well-suited for sendingout email messages to employees, as it can handle the fan-out messaging pattern (sending the same message to multiple recipients).

In this scenario, once Lambda identifies employees who have their work anniversaries, it can use SNS to send the email notifications efficiently. SNS integrates seamlessly with Lambda, and sending emails via SNS is a common pattern for this type of use case.

Event Scheduling:

To automate this daily task, you can schedule the Lambda function usingAmazon EventBridge (formerly CloudWatch Events). EventBridge can trigger the Lambda function on a daily schedule (cron-like scheduling). This avoids the complexity and operational overhead of manually setting up cron jobs on EC2 instances.

Why Not EC2 or SQS?:

Option A & Bsuggest running a cron job on anAmazon EC2instance. This approach requires you to manage, scale, and patch the EC2 instance, which increases operational overhead. Lambda is a better choice because it automatically scales and doesn’t require server management.

Amazon Simple Queue Service (SQS)is ideal for decoupling distributed systems but isn’t necessary in this context because the goal is to send notifications to employees on their work anniversaries. SQS adds unnecessary complexity for this straightforward use case, whereSNSis the simpler and more efficient solution.

AWS References:

AWS Lambda

Amazon SNS

Amazon DynamoDB

Amazon EventBridge

Summary:

UsingAWS Lambdacombined withAmazon SNSto send notifications, and scheduling the function withAmazon EventBridgeto run daily, is the most operationally efficient solution. It leverages AWS serverless technologies, which reduce the need for infrastructure management and provide automatic scaling. Therefore,Option Cis the correct and optimal choice.

Detailed Explanation:

A. EventBridge Rule:Detects modifications to CloudFront distributions in real time and triggers the Lambda function for further action.

B. ALB + Config:Focuses on ALB security violations, not relevant for CloudFront logging changes.

C. Lambda + SNS:Provides real-time notifications about changes in logging configuration.

D. GuardDuty:Focuses on threat detection, not logging configuration changes.

E. API Gateway + WAF:Unrelated to CloudFront logging changes.

A. Amazon EFS:Provides a scalable, shared file storage solution with minimal operational overhead. It's ideal for Linux-based workloads.

B. Amazon FSx for NetApp ONTAP:More suited for workloads requiring NetApp-specific features.

C. Amazon EBS:Requires manual management of file shares, which increases operational overhead.

D. Amazon FSx for Windows File Server:Best suited for Windows SMB workloads with low operational overhead.

E. Amazon FSx for OpenZFS:Better for Linux and Unix-based workloads.

Amazon S3 Intelligent-Tiering automatically moves objects between two access tiers based on changing access patterns. Objects not accessed for 30 days move to a lower-cost tier, but are still immediately available with millisecond retrieval. If objects become frequently accessed again, they are moved back to the frequent access tier. There are no retrieval charges and no impact on availability or performance. This storage class is specifically designed for unpredictable access patterns and cost optimization, requiring minimal management.

AWS Documentation Extract:

"S3 Intelligent-Tiering is the only storage class that automatically moves data between frequent and infrequent access tiers when access patterns change, with no retrieval charges and no impact on performance. It is designed to optimize costs automatically when data access patterns are unpredictable."

(Source: Amazon S3 documentation, Intelligent-Tiering storage class)

B: Glacier Deep Archive is for archival, not for low-latency millisecond access.

C: EFS is not optimized for object storage or global CDN distribution.

D: Cache-Control only affects CloudFront or browser caching, not S3 storage cost.

At 10 Mbps, the maximum transferable data in 30 days is far below 200 TB, making any online transfer (Options A, B, C) impossible within the time window.

AWS Snowball Edge storage-optimized devices support high-speed, offline bulk data migration of large datasets. Once the data is delivered to S3, FSx for Lustre can be linked to the S3 bucket to populate the Lustre filesystem.

DataSync cannot meet the time constraint over 10 Mbps. Storage Gateway is not designed for large-scale migrations.

=====================================================

AWS Storage Gateway stored volume gateway is designed for use cases where:

The primary data set remains on premises.

You want to maintain full local access to data with low latency.

You need asynchronous, secure replication and backup to AWS.

With stored volumes, you:

Map gateway storage volumes to local on-premises disks.

Present these volumes as iSCSI block devices to on-prem systems.

Storage Gateway then asynchronously backs up snapshots to Amazon S3, providing secure, durable offsite backup automatically.

Cached volume gateway (Option C) keeps the primary copy in AWS and only a subset cached locally, which does not meet the requirement to maintain full local access to all data.

Snowball and Snowball Edge (Options A and B) are primarily for bulk, one-time or periodic offline data transfer and do not provide an ongoing backup solution with continuous local access.

AWS Transfer Family is a fully managed service that provides SFTP, FTPS, and FTP access directly to Amazon S3. It allows organizations to support legacy data transfer protocols without managing infrastructure. This approach gives the vendors a familiar SFTP interface, with files landing directly in S3, and requires minimal operational effort compared to managing EC2 servers or using gateways.

Reference Extract from AWS Documentation / Study Guide:

"AWS Transfer Family enables secure transfer of files directly into and out of Amazon S3 using SFTP, FTPS, and FTP, and is fully managed by AWS, significantly reducing operational overhead."

Source: AWS Certified Solutions Architect – Official Study Guide, Data Migration and Transfer section.

AWS Lake Formationis designed for managing fine-grained access control to data in an efficient manner:

Granular Permissions: Lake Formation allows column-level, row-level, and table-level access controls, which can precisely define access to PII data.

Integration with AWS Glue Catalog: Lake Formation natively integrates with AWS Glue for seamless data cataloging and access control.

Operational Efficiency: Centralized access control policies minimize the need for separate IAM roles or policies.

Why Other Options Are Not Ideal:

Option A:

Creating multiple IAM policies introduces complexity and lacks column-level access control.Not efficient.

Option B:

Managing multiple IAM roles for granular access is operationally complex.Not efficient.

Option D:

Creating views in Glue adds unnecessary complexity and may not provide the level of granularity that Lake Formation offers.Not the best choice.

AWS References:

AWS Lake Formation:AWS Documentation - Lake Formation

Fine-Grained Permissions with Lake Formation:AWS Documentation - Fine-Grained Permissions

For cost-effectiveness and high availability in Amazon EMR workloads, the best approach is to configure atransient cluster(which runs for the duration of the job and then terminates) withOn-Demand Instancesfor the primary and core nodes, andSpot Instancesfor the task nodes. Here's why:

Primary and core nodes on On-Demand Instances: These nodes are critical because they manage the cluster and store data on HDFS. Running them on On-Demand Instances ensures stability and that no data is lost, as Spot Instances can be interrupted.

Task nodes on Spot Instances: Task nodes handle additional processing and can be used with Spot Instances to reduce costs. Spot Instances are much cheaper but can be interrupted, which is fine for non-critical tasks as the framework can handle retries.

Atransient clusteris more cost-effective than a long-running cluster for workloads that only run for 6 hours a day. Transient clusters automatically terminate after the workload completes, saving costs by not keeping the cluster running when it's not needed.

Option A: A long-running cluster may result in unnecessary costs when the cluster isn't being used.

Option C: Running core nodes on Spot Instances risks data loss if the Spot Instances are interrupted, violating the requirement for zero data loss.

Option D: Running both core and task nodes on Spot Instances is highly risky for data-critical workloads.

AWS References:

Amazon EMR Cluster Management

Using Spot Instances in EMR

Comprehensive and Detailed Step-by-Step Explanation:

The requirement is toprevent data deletion by any user, including administrators, for 7 years while allowing automatic deletion afterward.

S3 Object Lock in Compliance Mode (Correct Choice - C)

Compliance mode ensures that even the root user cannot delete or modify the objects during the retention period.

After 7 years, the S3 Lifecycle policy automatically deletes the objects.

This meets bothimmutability and automatic deletionrequirements.

Governance Mode (Option B - Incorrect)

Governance mode prevents deletion,but administrators can override it.

The requirement explicitly states thateven administrators must not be able to delete the data.

S3 Bucket Policy (Option A - Incorrect)

An S3 bucket policy candeny deletes, but policies can be modified at any time by administrators.

It does not enforce strict retention like Object Lock.

S3 Batch Operations Job (Option D - Incorrect)

A legal hold does not have an automatic expiration.

Legal holds must be manually removed, which is not efficient.

Why Option C is Correct:

S3 Object Lock in Compliance Mode prevents deletion by all users, including administrators.

The S3 Lifecycle policy deletes the data automatically after 7 years, reducing operational overhead.

Amazon S3 Access Points simplify managing data access for shared datasets in S3 by allowing the creation of distinct access policies for different applications or users. Each access point has its own policy and can be managed independently. This method avoids overloading a single bucket policy and helps remain within policy size limits.

Option D provides a scalable and operationally efficient solution by offloading individual access controls from a central bucket policy to individually managed access points, which is ideal for environments with many consuming applications.

For applications requiring high network throughput and low latency between EC2 instances, AWS recommends using a Cluster Placement Group:

Cluster Placement Group: Packs instances close together inside an Availability Zone, enabling workloads to achieve the low-latency network performance necessary for tightly-coupled node-to-node communication.

Enhanced Networking: Selecting instance types that support enhanced networking provides higher bandwidth, higher packet-per-second (PPS) performance, and consistently lower inter-instance latencies.

By combining a Cluster Placement Group with enhanced networking-enabled instance types, the company can meet the stringent performance requirements of its real-time industrial application.

AWS Glue jobs are designed for extract, transform, and load (ETL) operations, which are perfect for transforming clinical trial data. AWS Glue integrates with AWS Key Management Service (KMS), allowing for customer-specific encryption keys, fulfilling the encryption requirement with minimal operational effort. Client-side encryption with AWS KMS ensures that the data is encrypted before it is sent to S3, aligning with the security needs specified in the scenario.

Key aspects:

AWS Glue: This managed ETL service simplifies data transformation, reduces operational overhead, and integrates seamlessly with KMS.

CSE-KMS: Client-side encryption with KMS ensures that the data is encrypted with customer-specific keys before it is processed or stored in S3, offering robust security​​.

Minimal Operational Overhead: Compared to managing an EMR cluster, AWS Glue automates much of the process, making it a lower-effort solution.

AWS Documentation: According to the AWS Well-Architected Framework, encryption with AWS KMS offers strong security controls that meet the needs of industries requiring high levels of confidentiality​​.

AWS Backup Audit Manager automates auditing and reporting of backup activity and compliance, while AWS Config provides visibility into configuration changes. Together, they provide the simplest, most automated, and compliant backup monitoring solution.

From AWS Documentation:

“AWS Backup Audit Manager automatically audits backup activity across AWS resources. You can use predefined or custom frameworks to monitor backup compliance and encryption status.”

(Source: AWS Backup Audit Manager User Guide)

Why D is correct:

Ensures centralized visibility into all backup jobs.

Verifies encryption status automatically.

Generates ready-to-use reports with minimal operational overhead.

Complies with regulatory requirements for data protection.

Why others are incorrect:

A & C: Custom Lambda automation increases maintenance effort.

B: Manual checking is operationally inefficient and error-prone.

AWS best practice for a highly available, resilient web application is:

Run EC2 instances in an Auto Scaling group across multiple Availability Zones.

Put an Application Load Balancer (ALB) in front of the Auto Scaling group.

Use CloudWatch alarms and scaling policies for horizontal scaling based on demand.

Option D implements exactly this: a minimum of three instances spread across AZs (resilience to AZ failure), behind an ALB, with automatic horizontal scaling. This provides both elasticity and high availability with good cost efficiency (instances scale out/in based on traffic).

Option A uses only three fixed instances and relies on vertical scaling, which does not scale as well and is less resilient.

Option B overprovisions to nine instances from the start, which is unnecessarily expensive.

Option C keeps all instances in a single AZ, which does not meet resilience requirements.

VPC Endpoint for S3: A gateway endpoint for Amazon S3 enables you to privately connect your VPC to S3 without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Configuration Steps:

In the VPC console, navigate to "Endpoints" and create a new endpoint.

Select the service name for S3 (com.amazonaws.region.s3).

Choose the VPC and the subnets where your EC2 instances are running.

Update the route tables for the selected subnets to include a route pointing to the endpoint.

Security Compliance: By configuring an S3 gateway endpoint, all traffic between the VPC and S3 stays within the AWS network, complying with the company's security regulations to avoid internet traversal.

Comprehensive and Detailed Explanation (from AWS Solutions Architect documentation):

Amazon DynamoDB is a fully managed NoSQL database engineered for extremely high throughput and millisecond-level response times at any scale. It can automatically scale to support millions of requests per second and requires no management of infrastructure, storage provisioning, or server maintenance.

AWS documentation specifically highlights DynamoDB as the optimal solution for ecommerce workloads that require consistent low-latency performance, massive scalability, and near-zero operational overhead.

Aurora and RDS cannot support millions of requests per second, and Redshift is a data warehouse not intended for transactional workloads.

AmazonSES (Simple Email Service)allows for the automatic ingestion of incoming emails. By setting up email receiving in SES and creating a rule set with a receipt rule, you can configure SES to invoke anAWS Lambda functionwhenever an email is received. The Lambda function can then process the email body and attachments, saving any attachments to an Amazon S3 bucket. This solution is highly scalable, cost-effective, and provides near real-time processing of emails with minimal operational overhead.

Option B (Content filtering): This only filters emails based on content and does not provide the functionality to save attachments to S3.

Option C (S3 Event Notifications): While SES can store emails in S3, SES with Lambda offers more flexibility for processing attachments in real-time.

Option D (EventBridge rule): EventBridge cannot directly listen for incoming emails, making this solution incorrect.

AWS References:

Receiving Email with Amazon SES

Invoking Lambda from SES

This design includes:

ALB: Supports AWS WAF integration.

Auto Scaling Group: Automatically scales based on load.

Multi-AZ Deployment: Increases resiliency and availability.

AWS WAF: Can be attached to ALB for application-layer protection.

“ALB is integrated with AWS WAF. You can deploy your EC2 instances in an Auto Scaling group across multiple Availability Zones to ensure high availability.”

— High Availability with Auto Scaling and ALB

Why not others?

A: Single AZ = not resilient

C: AWS WAF cannot attach to Route 53

D: NLB is not supported by AWS WAF

This solution offers the least operational overhead while meeting the security and global availability requirements:

Amazon CloudFront and S3: Hosting the static frontend on S3 and serving it via CloudFront provides low-latency global distribution, high availability, and security. S3 is a cost-effective and serverless option for hosting static assets, and CloudFront ensures that the application is cached closer to the users, reducing latency globally.

AWS Lambda and API Gateway: Refactoring the microservices to use Lambda functions with API Gateway allows for a fully serverless, scalable, and highly available backend. This reduces the need for managing EC2 instances, as Lambda automatically scales to meet demand and only charges for the actual usage.

RDS for MySQL: Migrating the MySQL database from an EC2 instance to Amazon RDS significantly reduces operational overhead. RDS manages backups, patching, and scaling, and it offers high availability options (e.g., Multi-AZ).

Option AandDinvolve using EC2 Reserved Instances for the database, which requires more operational maintenance than using RDS.

Option Csuggests using a Network Load Balancer with Lambda, which adds unnecessary complexity for this use case.

AWS References:

Amazon S3 and CloudFront Integration

AWS Lambda with API Gateway

Amazon RDS for MySQL

AWS Step Functions provides a low-code, fully managed workflow service with a visual interface to orchestrate AWS Glue jobs in sequence. Step Functions integrates natively with AWS Glue and can be triggered by Amazon EventBridge based on events or schedules.

AWS Documentation Extract:

“AWS Step Functions makes it easy to coordinate multiple AWS services into serverless workflows so you can build and update apps quickly. Step Functions provides visual workflows and integrates with AWS Glue for ETL orchestration.”

(Source: AWS Step Functions documentation)

A: Manual scripting and dashboards do not provide a low-code or integrated visual workflow.

C: QuickSight is for BI, not workflow visualization.

D: ECS adds unnecessary complexity.

Security groups are stateful, but they cannot explicitly deny traffic — only allow.

Network ACLs are stateless and support explicit deny rules.To prevent Application A from sending traffic to Application B, configure a deny outbound rule in the network ACL of Application A’s subnet to block traffic to Application B’s subnet.

“Unlike security groups, network ACLs support both allow and deny rules, enabling you to explicitly block traffic.”

— Network ACLs

This is the correct method to block outbound traffic between subnets.

Amazon Aurora MySQL–compatible offers a distributed, fault-tolerant storage system with Multi-AZ high availability and supports Aurora Replicas that “share the same underlying storage” for low-latency reads. Aurora provides Aurora Auto Scaling to “automatically add or remove Aurora Replicas based on load,” ideal for unpredictable, read-heavy workloads. This architecture offloads reads from the writer and maintains HA through automatic failover. Amazon RDS Single-AZ (B) lacks HA. Redshift (A) is a data warehouse, not a transactional DB. ElastiCache (D) can reduce read pressure but does not provide durable read replicas or automatic HA scaling at the database tier. Aurora’s design directly addresses the requirement to automatically scale reads while maintaining availability, matching Well-Architected guidance to use managed, elastic services for variable demand.

Amazon SQS is the AWS-recommended service for handling decoupled, scalable, ordered, durable message ingestion with potentially large workloads. It supports payloads up to 256 KB directly and up to several MB using S3-backed large-payload extensions.

Lambda functions can process messages from SQS with automatic scaling. ECS with the Fargate launch type provides a serverless container platform for the frontend, scaling based on demand without managing servers.

SNS (Options B and D) is not a queue, does not provide durable message buffering, and is not appropriate for variable, bursty workloads that may exceed backend throughput. Lambda@Edge (Options A and D) is unsuitable for backend order processing because it runs at CloudFront edge locations with strict limitations.

Why Option B is Correct:

Amazon SQS: Ensures no requests are lost, even during traffic spikes.

API Gateway: Handles dynamic traffic patterns efficiently, integrating with SQS for asynchronous processing.

Lambda: Polls the queue and processes requests in a serverless and scalable manner.

Dead-Letter Queue (DLQ): Ensures failed messages are retried or logged for debugging.

Why Other Options Are Not Ideal:

Option A: Elastic Beanstalk cannot handle queue-based decoupling, making it unsuitable for spiky traffic.

Option C: ALB to Lambda does not provide buffering for traffic spikes, risking request loss.

Option D: SNS is better suited for notifications, not reliable for ensuring message durability.

AWS References:

Amazon SQS:AWS Documentation - SQS

A & B. GuardDuty:Designed for threat detection, not for identifying or classifying sensitive data in S3 buckets.

C. Macie with EventBridge + SNS:Automatically identifies sensitive data, triggers alerts, and uses SNS for immediate notification via email.

D. Macie with EventBridge + SQS:Introduces latency due to periodic polling and adds unnecessary complexity.

Amazon EFS is a fully managed, scalable file storage service that can be accessed concurrentlyby thousands of EC2 instances. It supports General Purpose performance mode for latency-sensitive use cases and provides high availability and durability across multiple Availability Zones with minimal changes to application code.

AWS Fargate is a serverless compute engine for containers that works with Amazon EKS. With Fargate, you do not need to provision or manage EC2 instances or clusters. You simply define and deploy your pods, and Fargate automatically launches the required compute resources. This results in the lowest operational overhead because AWS manages the infrastructure. Fargate profiles allow you to specify which pods run on Fargate.

AWS Documentation Extract:

“AWS Fargate is a serverless, pay-as-you-go compute engine that lets you focus on building applications without managing servers. With Amazon EKS and Fargate, you only need to define your application’s pods; Fargate provisions and manages the required compute resources for you.”

(Source: Amazon EKS documentation, AWS Fargate integration)

Other options:

A: Self-managed nodes require you to manage EC2 instances.

B: Managed node groups reduce some overhead, but you are still responsible for patching and managing the EC2 instances.

D: Managed node groups with Karpenter automate scaling but do not remove the need to manage underlying instances.

Option Ais the most automated and cost-efficient solution for exporting data to S3 for analytics.

Option Binvolves manual setup of Streams to S3.

Options C and Dintroduce complexity with EMR.

A. Aurora MySQL:Handles OLTP workloads efficiently with built-in replication and auto-scaling capabilities.

B. EC2 with MySQL:Requires heavy manual maintenance and does not scale seamlessly.

C. RDS for MySQL:Limited in auto-scaling compared to Aurora.

D. Redshift:Primarily for OLAP, not suitable for OLTP workloads.

Amazon Elastic File System (Amazon EFS) is a fully managed, elastic, shared file system that can be mounted concurrently by multiple EC2 instances across multiple Availability Zones. EFS automatically grows and shrinks as files are added or removed, providing seamless scalability for unpredictable and growing storage needs. It supports simultaneous access from multiple compute resources, making it ideal for applications where several EC2 instances must read and write to the same data set concurrently. EBS volumes cannot be shared across multiple Availability Zones, and S3 Glacier is intended for archival and infrequent access, not for active, hourly data analysis.

Reference Extract from AWS Documentation / Study Guide:

"Amazon EFS provides a scalable, fully managed elastic NFS file system for use with AWS Cloud services and on-premises resources. It can be mounted to many EC2 instances across multiple Availability Zones and is ideal for shared data scenarios."

Source: AWS Certified Solutions Architect – Official Study Guide, Storage Solutions section; Amazon EFS User Guide.

Aurora I/O-Optimized: This storage configuration is designed to provide consistent high performance for Aurora databases. It automatically scales IOPS as the workload increases, without needing to provision IOPS separately.

Cost-Effectiveness: With Aurora I/O-Optimized, you only pay for the storage and I/O you use, making it a cost-effective solution for applications with varying and unpredictable I/O demands.

Implementation:

During the creation of the Aurora PostgreSQL Serverless v2 cluster, select the I/O-Optimized storage configuration.

The storage system will automatically handle scaling and performance optimization based on the application load.

Operational Efficiency: This configuration reduces the need for manual tuning and ensures optimal performance without additional administrative overhead.

To ensure that only specific AWS Lambda functions can read from or write to an Amazon SQS queue, useresource-based policiesattached directly to the SQS queue. These policies explicitly grant permissions to the IAM roles used by the Lambda functions. Additionally, the Lambda execution roles must also have IAM policies that permit SQS access. This dual-layer approach follows the AWS security best practice of granting least privilege access and ensures that no other service or entity can interact with the queue.

This is a common and supported pattern documented in theAmazon SQS Developer Guide, where resource-based policies restrict access at the queue level while IAM roles control permissions at the function level.

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that makes it easy to build and run applications that use Apache Kafka to process streaming data. By using Amazon ECS with the AWS Fargate launch type, you can run containers without managing servers or clusters. This combination reduces operational overhead and provides scalability.

AWS Spot best practices recommend diversifying capacity across multiple instance types and Availability Zones and using the capacity-optimized (price-capacity-optimized) allocation strategy to choose pools with the deepest capacity for higher availability. Adding a small On-Demand base in the Auto Scaling group maintains steady, uninterrupted baseline processing while keeping costs low and absorbing Spot interruptions. Option C (lowest price) increases interruption risk. Capacity Reservations (B) target On-Demand capacity guarantees and add cost, not needed for Spot-based elasticity. Option E is redundant; diversification is already achieved with (D). This combination maximizes resiliency of Spot workloads while preserving strong cost efficiency and aligns with AWS guidance for fault-tolerant, stateless applications on Spot.

Amazon EFS requires a mount target in each Availability Zone where EC2 instances access the file system. This is because each mount target provides an elastic network interface in the subnet and AZ, reducing network latency by allowing EC2 instances to communicate locally with the EFS mount target. Creating a mount target in each AZ optimizes file system access performance and availability. Instances mount the EFS file system via the mount target in their respective AZ, which provides the lowest possible latency and avoids cross-AZ traffic.

Option A, with only a single mount target in the VPC, will cause cross-AZ traffic for instances in other AZs, increasing latency and potentially incurring data transfer costs. Option B is incomplete and introduces complexity with sharing directories across instances. Option C is invalid because mount targets are per AZ and per subnet, not per instance.

AWS guidance for global, highly available, low-latency applications using a relational database recommends:

Amazon CloudFront with Amazon S3 for static content to cache data at edge locations globally and minimize latency for static assets.

A regional application tier deployed close to users using managed container services such as Amazon ECS on AWS Fargate, which removes the need to manage servers and scales automatically, reducing operational overhead.

A relational database tier using Amazon Aurora global database, which is purpose-built for globally distributed applications. Aurora global database provides a primary cluster in one Region with low-latency read replicas in secondary Regions and fast cross-Region replication, enabling low-latency reads and high availability for global users.

Option A uses only a single Region, which does not meet the “global users with low latency” requirement.

Option C uses RDS and DMS-based replication, which requires more management and does not provide Aurora’s integrated global database features.

Option D replaces the relational database with DynamoDB, which violates the requirement that the application interacts with a relational database.

The most secure solution for allowing EC2 instances to access an S3 bucket is by usingIAM roles. An IAM role can be created with an access policy that grants the required permissions (e.g., to read and write to the S3 bucket). The IAM role is then associated with the EC2 instances through anIAM instance profile.

By associating the role with the instances, the EC2 instances can securely assume the role and receive temporary credentials via the instance metadata service. This avoids the need to store credentials (such as access keys) on the instances or within the application, enhancing security and reducing the risk of credentials being exposed.

AWS CloudFormation can be used to automate the creation of the entire infrastructure, including EC2 instances, IAM roles, and associated policies.

AWS References:

IAM Roles for EC2 Instancesoutlines the use of IAM roles for secure access to AWS services.

AWS CloudFormation User Guidedetails how to create and manage resources using CloudFormation templates.

Why the other options are incorrect:

A. Save IAM access key in UserData: This is insecure because it involves storing long-term credentials in the instance user data, which can be exposed.

B. Store access keys in S3: This is also insecure, as it involves managing and distributing long-term credentials, which should be avoided.

D. Retrieve access keys via a script: This approach is unnecessarily complex and less secure than using IAM roles, which provide temporary credentials automatically.

AWS VPC endpoints (interface and gateway) allow private connectivity from VPC resources to AWS services without requiring public IP addresses or internet gateways. This ensures applications remain isolated in private subnets while securely accessing AWS services. NAT gateways (B) would allow internet access, which does not meet the security requirement. Internet gateways (C) directly expose traffic to the internet, which violates the isolation requirement. Direct Connect (D) connects on-premises environments to AWS but does not provide service access from private subnets. Therefore, option A — using interface VPC endpoints — is the correct solution.

The SELECT INTO OUTFILE S3 feature allows you to export Amazon Aurora MySQL data directly to Amazon S3 with minimal operational overhead. This method is efficient and cost-effective for archiving historical data.

You can configure S3 Lifecycle rules to transition the exported data to lower-cost storage (e.g., S3 Glacier or S3 Standard-IA) and eventually delete it after 5 years.

No need for additional ETL tools like Glue or DataBrew unless complex transformations are required.

The workload is Windows-based and requires a shared file system with SMB support and very high throughput for 4K video editing.

Amazon FSx for Windows File Server is a fully managed, highly available native Windows file system that supports the SMB protocol, Active Directory integration, and can deliver high throughput and low latency suitable for media workloads.

FSx for Windows File Server supports automatic storage scaling to grow file system capacity as data increases, matching the requirement of +1 TB per week with minimal admin effort.

A Multi-AZ SSD deployment provides high availability and performance.

Why others are not correct:

B: Amazon EFS is NFS, not SMB, and is optimized for Linux clients, not Windows-based environments.

C: FSx for Lustre is a high-performance POSIX file system typically used with Linux-based HPC workloads, not Windows + SMB.

D: S3 File Gateway is not designed for sustained multi-GB/s shared-edit performance and adds additional complexity; it is more suited for backup/archival and limited shared file access.

Export Requirements:

To export data from DynamoDB to Amazon S3, point-in-time recovery (PITR) must be enabled for the tables. This feature creates a snapshot of the data, which is essential for exports.

Incorrect Options Analysis:

Option B: S3 buckets and DynamoDB tables do not need to be in the same region for exports.

Option C: DynamoDB streams are unrelated to the export functionality.

Option D: DAX accelerates reads but has no role in exports.

AmazonTextractcan extract text from the PDFs, andAmazon Comprehendis the most suitable service to analyze the extracted text for sentiment and insights. Comprehend offers a fully managed, low-operational overhead solution for analyzing text data. The results can then be stored in anAmazon S3bucket, ensuring scalability and easy access.

Option A: Athena is for querying structured data and is not suitable for sentiment analysis.

Option B: SageMaker adds complexity and is not necessary when Comprehend can handle sentiment analysis natively.

Option D: QuickSight is used for visualization and analytics, but it does not provide sentiment analysis.

AWS References:

Amazon Comprehend

Amazon Textract

Amazon RDSBlue/Green Deploymentsis the ideal solution for upgrading the database version with minimal operational overhead and no data loss. Blue/Green Deployments allows you to create a separate, fully managed "green" environment with the upgraded database version. You can test the new version in the green environment while the "blue" environment continuesserving production traffic. Once testing is complete, you can seamlessly switch traffic to the green environment without downtime.

This solution provides:

Fast, non-disruptive upgrade: Traffic is only switched to the new environment after testing, ensuring zero data loss.

Minimal operational overhead: AWS handles the infrastructure management, reducing manual intervention.

Option A (Manual snapshot): This requires manual intervention and involves more operational overhead.

Option B (Native backup/restore): This approach is more labor-intensive and slower than Blue/Green Deployments.

Option C (DMS): AWS DMS adds unnecessary complexity for a simple version upgrade when Blue/Green Deployments can handle the task more efficiently.

AWS References:

Amazon RDS Blue/Green Deployments

Amazon Route 53 Resolver endpoints allow you to integrate DNS between AWS and on-premises environments easily. By creating inbound and outbound resolver endpoints, you can configure conditional forwarding rules so that DNS queries for your on-premises AD domain are forwarded to the on-premises DNS servers. This approach is fully managed, scales automatically, and requires the least administrative overhead.

AWS Documentation Extract:

"Route 53 Resolver provides DNS resolution between AWS and on-premises environments, using endpoints and forwarding rules to manage DNS query routing seamlessly."

(Source: Route 53 Resolver documentation)

A, D: Require provisioning, managing, and patching EC2 servers or domain controllers.

B: NS records in a private hosted zone do not provide true DNS forwarding.

AWS Glue Crawler: AWS Glue is a fully managed ETL (Extract, Transform, Load) service that makes it easy to prepare and load data for analytics. A Glue crawler can automatically discover new data and schema in Amazon S3, making it easy to keep the data catalog up-to-date.

Crawling the Data:

Set up an AWS Glue crawler to scan the S3 bucket containing the clickstream data.

The crawler will automatically detect the schema and create/update the tables in the AWS Glue Data Catalog.

Amazon Athena:

Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.

Once the data catalog is updated by the Glue crawler, use Athena to query the clickstream data directly in S3.

Operational Efficiency: This solution leverages fully managed services, reducing operational overhead. Glue crawlers automate data cataloging, and Athena provides a serverless, pay-per-query model for quick data analysis without the need to set up or manage infrastructure.

AWS Secrets Manager is a fully managed service specifically designed to securely store and automatically rotate database credentials, API keys, and other secrets. Secrets Manager provides built-in integration with Amazon RDS for automatic credential rotation on a configurable schedule without requiring downtime. It also manages the secure distribution of the credentials to authorized services, such as your web servers, using IAM policies. Manual solutions (S3, files, cron jobs) do not provide the same level of automation, audit, or security.

Reference Extract from AWS Documentation / Study Guide:

"AWS Secrets Manager enables you to rotate, manage, and retrieve database credentials securely. It supports automatic rotation of secrets for supported AWS databases without requiring application downtime."

Source: AWS Certified Solutions Architect – Official Study Guide, Security and Secrets Management section.

The workload is bursting and must be able to buffer more orders than the printers can process. The best practice for this is a durable, scalable queue: Amazon SQS.

SQS decouples the frontend from the backend and can handle large, spiky volumes of messages.

AWS Lambda is a good fit for processing messages from SQS because it scales automatically with queue depth and requires no server management.

Deploying the frontend in Docker on Amazon ECS with Fargate provides a fully managed, auto-scaling container environment without managing EC2 instances.

Why others are not correct:

A and D: SNS is a pub/sub service, not a queue; it does not provide durable back-pressure buffering when the printers are slower than incoming requests. Lambda@Edge is for CloudFront edge processing, not back-end order processing.

B: Same SNS issue—no durable queue semantics and not ideal for workload that must be buffered and retried.

The correct and secure approach is to use Amazon CloudFront with Origin Access Control (OAC) to protect the S3 origin and attach AWS WAF to the CloudFront distribution to inspect and filter traffic at the edge before reaching the origin.

From AWS Documentation:

“AWS WAF is integrated with Amazon CloudFront, allowing inspection of HTTP(S) requests at the edge location before forwarding to your origin. To restrict direct access to the S3 bucket, use Origin Access Control (OAC).”

(Source: Amazon CloudFront Developer Guide – Serving private content)

Why Option D is correct:

CloudFront is the only service that integrates with AWS WAF for full HTTP layer inspection.

Origin Access Control (OAC) ensures that only CloudFront can access the S3 origin—replacing older Origin Access Identity (OAI) features.

The S3 bucket policy is configured to trust requests only from CloudFront using OAC signed requests.

Why the other options are incorrect:

Option A: WAF ARN is not a principal in S3 bucket policy. IAM does not support bucket policies based on WAF ARNs.

Option B: Incorrect – CloudFront doesn't "forward requests to WAF"; rather, WAF is associated with CloudFront and inspects requests at the edge.

Option C: S3 does not use security groups; they are for EC2/network interfaces. This shows a misunderstanding of how S3 works.

Using an Application Load Balancer (ALB) allows for integration with AWS WAF to inspect all incoming traffic. Running the application on Amazon ECS provides a scalable and managed container orchestration service. Utilizing Amazon Aurora Serverless for the PostgreSQL database offers automatic scaling based on application demand, which is cost-effective for workloads with variable traffic patterns, such as higher traffic on weekdays and lower traffic on weekends.

Amazon Aurora Serverless is a cost-effective, on-demand, autoscaling configuration for Amazon Aurora. It automatically adjusts the database's capacity based on the current demand, which is ideal for workloads with variable and unpredictable usage patterns. Since the application is expected to be read-heavy with occasional writes and steady growth, Aurora Serverless can provide the necessary performance without requiring the management of database instances.

Cost-Optimization: Aurora Serverless only charges for the database capacity you use, making it a more cost-effective solution compared to always running provisioned database instances, especially for workloads with fluctuating demand.

Scalability: It automatically scales database capacity up or down based on actual usage, ensuring that you always have the right amount of resources available.

Performance: Aurora Serverless is built on the same underlying storage as Amazon Aurora, providing high performance and availability.

Why Not Other Options?:

Option A (RDS with Provisioned IOPS SSD): While Provisioned IOPS SSD ensures consistent performance, it is generally more expensive and less flexible compared to the autoscaling nature of Aurora Serverless.

Option C (DynamoDB with On-Demand Capacity): DynamoDB is a NoSQL database and may not be the best fit for applications requiring relational database features.

Option D (RDS with Magnetic Storage and Read Replicas): Magnetic storage is outdated and generally slower. While read replicas help with read-heavy workloads, the overall performance might not be optimal, and magnetic storage doesn’t provide the necessary performance.

AWS References:

Amazon Aurora Serverless- Information on how Aurora Serverless works and its use cases.

Amazon Aurora Pricing- Details on the cost-effectiveness of Aurora Serverless.

AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. By creating a Config rule, you can automatically check whether your Amazon EBS volumes are encrypted and flag those that are not, with minimal cost and configuration effort.

AWS Config Rule: AWS Config provides managed rules that you can use to automatically check the compliance of your resources against predefined or custom criteria. In this case, you wouldcreate a rule to evaluate EBS volumes and determine if they are encrypted. If a volume is not encrypted, the rule will flag it, allowing you to take corrective action.

Operational Overhead: This approach significantly reduces operational overhead because once the rule is in place, it continuously monitors your EBS volumes for compliance, and there’s no need for manual checks or custom scripting.

Why Not Other Options?:

Option A (Lambda with API calls and EventBridge): While this can work, it involves writing and maintaining custom code, which increases operational overhead compared to using a managed AWS Config rule.

Option B (API calls on Fargate): Running API calls on Fargate is more complex and costly compared to using AWS Config, which provides a simpler, managed solution.

Option C (IAM policy with Cost Explorer): This option does not directly enforce encryption compliance and involves manual intervention, making it less efficient and more prone to errors.

AWS References:

AWS Config Rules- Overview of AWS Config rules and how they can be used to evaluate resource configurations.

Amazon EBS Encryption- Information on how to manage and enforce encryption for EBS volumes.

Option B: Pre-signed URLs provide temporary, authenticated access to S3, limiting uploads to the time frame specified. This solution is lightweight, efficient, and easy to implement.

Option Arequires the management of IAM temporary credentials, adding complexity.

Option Cinvolves unnecessary development effort.

Option Dintroduces more complexity with STS and roles than pre-signed URLs.

According to AWS best practices, each tier’s security group must restrict inbound traffic to only the upstream trusted source. For the web tier, the Application Load Balancer must be the only entity allowed to send traffic. AWS documentation specifies: “Restrict the security groups associated with your targets to accept traffic only from the load balancer.” This confirms that the web tier security group should allow inbound HTTPS from the ALB security group (A).

For communication between the web and application tiers, AWS states: “You can specify a security group as the source or destination in a rule” and “Create rules only for the protocols and ports required by your application.” Therefore, the application tier security group must allow inbound HTTPS traffic from the web tier security group (E).

For the database tier, AWS guidance says: “Allow only the necessary ports for database communication.” Microsoft SQL Server listens on port 1433 by default, so the database tier security group must allow inbound SQL Server traffic from the application tier security group (C).

Outbound rules (options B, D, and F) are unnecessary because AWS specifies that “Security groups are stateful. Return traffic is automatically allowed.” This means once inbound rules are defined, the return path is automatically permitted without extra outbound configurations.

This combination (A, C, E) applies the principle of least privilege, ensures end-to-end secure communication across tiers, and follows AWS recommendations for ALB-to-target security group setups.

Detailed Explanation:

A. ECS + API Gateway:Overly complex and costly for an on-demand, intermittent workload.

B. EventBridge + SNS:EventBridge schedules are unnecessary for on-demand generation.

C. EKS + API Gateway:Overkill for this use case, with high operational overhead.

D. Lambda + SES:Most cost-effective and efficient solution for generating and emailing reports on demand.

Durable storage of incoming orders with buffering and ability to handle surges is exactly what Amazon SQS is designed for. SQS provides highly durable, scalable queues that decouple producers from consumers.

Centrally tracking workflow steps is a core use case of AWS Step Functions, which gives a visual workflow and state machine, tracks the state of each order, and can orchestrate calls to multiple microservices (in this case, Lambda functions).

Combining SQS + Step Functions + Lambda gives:

Durable queueing for orders (SQS).

Loose coupling and surge handling (SQS decoupling + auto-scaling Lambda).

Central orchestration and tracking of order-processing steps (Step Functions).

Why the other options are not correct:

A: SNS is a pub/sub service, not a durable work queue, and is not designed for “store-and-retry until processed” workloads in the same way SQS is.

C: SQS + EventBridge provides decoupling but no central, stateful workflow tracking; EventBridge is event routing, not workflow orchestration.

D: SNS + EventBridge still lacks durable order storage and explicit centralized workflow/state tracking.

Amazon Kinesis Data Streams in on-demand mode is purpose-built for real-time ingestion of streaming data and scales automatically with traffic, which is ideal for fluctuating workloads like clickstream data.

Combined with AWS Lambda, which scales concurrently based on incoming events, this setup ensures efficient, real-time processing with minimal configuration and cost overhead.

Option B involves Firehose, which is not optimal for low-latency processing. Option C is incorrect as Kinesis Video Streams is designed for video data, not clickstream. Option D introduces Flink, which adds unnecessary complexity when Lambda suffices.

SSE-KMS (Server-side encryption with AWS Key Management Service) not only encrypts data at rest but also integrates with AWS CloudTrail to provide detailed logs of key usage — meeting the audit requirement.

“SSE-KMS provides the ability to audit key usage to see who used the key and when, via AWS CloudTrail.”

— Amazon S3 Encryption Documentation

Benefits:

Encryption with customer-managed or AWS-managed KMS keys

Audit trails of key usage events

Fine-grained access control

Incorrect Options:

A: SSE-S3 does not support auditing of key usage.

C: SSE-C does not integrate with CloudTrail or KMS.

D: Self-managed keys require external key infrastructure and custom audit logging.

For predictable workloads requiring a relational database, Amazon RDS for MySQL offers a managed, cost-effective solution. Storing product images in Amazon S3 is highly durable and cost-efficient, especially for static assets like images. This combination leverages managed services to reduce operational overhead and costs.

AWS STS issues temporary credentials with automatically expiring permissions based on IAM roles. This eliminates the need to manually manage or deactivate IAM users.

“You can use AWS STS to grant temporary security credentials that automatically expire after a specified duration.”

— Temporary Security Credentials

This is the least operational overhead and follows AWS best practices for short-term access.

AWS Lambda supports encrypting environment variables at rest using AWS KMS. You can use encryption helpers (or Lambda’s built-in support) to encrypt sensitive environment variable values using a KMS key. These encrypted variables are not visible in plaintext to developers, either in the console or when running the code.

AWS Documentation Extract:

"AWS Lambda automatically encrypts environment variables at rest. For additional security, you can use AWS KMS keys and encryption helpers to encrypt environment variables, ensuring they are never exposed in plaintext."

(Source: AWS Lambda documentation, Environment Variables Security)

A: Does not address the issue (and adds more management overhead).

B, C: There is no native support for environment variable encryption via CloudHSM or ACM.

Since the workload is interruptible and can resume after restarts, Amazon EC2 Spot Instances are the most cost-effective option, offering savings of up to 90% compared to On-Demand. Running the batch workload on AWS Batch with Spot Instances allows automatic job queue management, interruption handling, and scheduling. Option A and C reduce cost but still rely on reserved or committed pricing for On-Demand capacity. Option B (Capacity Reservations) increases cost instead of reducing it. Therefore, the most cost-optimized solution is D — AWS Batch with EC2 Spot Instances.

Option Aintroduces complexity and does not scale well.

Option Bcreates a read replica, offloading read traffic from the primary RDS instance without impacting the critical application.

Option Cis manual and inefficient.

Option Dmight help for caching frequently queried data but is not ideal for ad-hoc reporting.Therefore, Option B is the best choice.

S3 One Zone-IA:

Suitable for infrequently accessed data that doesn't require multiple Availability Zone resilience.

Cost-effective for storing user-uploaded images that are only used for AI model training twice a year.

S3 Standard:

Ideal for frequently accessed data with high durability and availability.

Store premium user-generated AI images here to ensure they are readily available for download at any time.

S3 Standard-IA:

Cost-effective storage for data that is accessed less frequently but still requires rapid retrieval.

Store non-premium user-generated AI images here, as these images are only downloaded once every 6 hours, making it a good balance between cost and accessibility.

Cost-Effectiveness: This solution optimizes storage costs by categorizing data based on access patterns and durability requirements, ensuring that each type of data is stored in the most cost-effective manner.

For infrequent or ad hoc querying of log data, Amazon S3 + Amazon Athena provides the most cost-effective, serverless, and scalable analytics solution.

From AWS Documentation:

“Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.”

(Source: Amazon Athena User Guide)

Why A is correct:

Amazon S3 offers durable, scalable, and cost-efficient storage.

Athena allows SQL-based querying on structured or semi-structured data like logs.

No need to provision or manage infrastructure.

Ideal for occasional querying at low cost.

Why the others are not optimal:

Option B: OpenSearch adds cost and is best for frequent, low-latency log querying.

Option C: RDS is not optimized for large-scale write-heavy log ingestion and costs more.

Option D: CloudWatch Logs is suitable for real-time monitoring, not for long-term storage and analytics of large log volumes.

Amazon SQS supports Interface VPC endpoints (AWS PrivateLink), enabling private connectivity from your VPC to SQS without using public IPs, traversing the public Internet, or requiring NAT/IGW. You can restrict access by attaching a queue resource policy that allows only the specific VPC endpoint and denies all other principals/paths, enforcing that all traffic stays on the AWS network. SSE-SQS (B) encrypts data at rest but does not influence network pathing. STS temporary credentials (C) handle authentication/authorization, not routing. VPC Flow Logs (D) are monitoring/visibility and do not prevent public egress. Creating an SQS VPC endpoint and tightening the queue policy satisfies the requirement of no public IP usage while maintaining secure, private access from serverless components in VPC subnets.