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

Amazon S3 is suitable for storing data that needs to be accessed weekly and integrates with AWS Key Management Service (KMS) to provide encryption at rest with server-side encryption using KMS-managed keys (SSE-KMS).

SSE-KMS uses envelope encryption and allows automatic key rotation and logging through AWS CloudTrail, satisfying the requirements for audit trails and compliance.

S3 Glacier Deep Archive is unsuitable due to its high retrieval latency. SSE-C requires customer-side management of encryption keys, with no support for automatic rotation or audit. SSE-S3 does not use customer-managed keys and lacks fine-grained control and auditing.

Express Workflows in AWS Step Functions are optimized for high-throughput, short-duration, and low-cost workflows. They are suitable for applications with large volumes of parallel and interdependent tasks. When paired with HTTP APIs from API Gateway, which are more cost-efficient than REST APIs, this setup offers scalability and cost-effectiveness.

AWS Lambda SnapStart is designed to improve the cold start performance of Java Lambda functions by initializing the function, taking a snapshot of the execution environment, and then reusing that snapshot for subsequent invocations. However, some code (such as code that generates unique IDs or session-specific data) should run during each invocation, not during the snapshot process. By using a pre-snapshot hook and moving the unique ID generation into the handler, you ensure that non-deterministic or per-invocation code is executed correctly, while the rest of the initialization benefits from SnapStart. This delivers the lowest latency and cost, as you do not need to pay for provisioned concurrency.

AWS Documentation Extract:

"Lambda SnapStart is ideal for Java functions with long cold starts due to heavy initialization. Move non-deterministic code such as unique ID generation to the handler, and use pre-snapshot hooks to customize what gets snapshotted. SnapStart works only for published versions, not $LATEST."

(Source: AWS Lambda documentation, Using SnapStart for Java Functions)

Other options:

A: SnapStart is not supported for the $LATEST version; must be a published version.

B & C: Provisioned concurrency removes cold starts but is more expensive than SnapStart for most workloads.

C: You must use SnapStart on published versions, but provisioned concurrency is not required for SnapStart and adds cost.

AWS CloudFormationallows for the automated, repeatable setup of infrastructure, reducing human error and ensuring consistency.AWS Configprovides the ability to track changes in the infrastructure, ensuring that all changes are logged and auditable, which satisfies the requirement for tracking incremental changes.

Option A and C (AWS Organizations): AWS Organizations manage multiple accounts, but they are not designed for infrastructure setup or change tracking.

Option D (Service Catalog): Service Catalog is used for deploying products, not for setting up infrastructure or tracking changes.

AWS References:

AWS Config

AWS CloudFormation

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.

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.

This solution is the most suitable for running cron jobs on AWS with minimal refactoring, while also supporting the possibility of running jobs in response to future events.

Container Image for Cron Jobs: By containerizing the cron jobs, you can package the environment and dependencies required to run the jobs, ensuring consistency and ease of deployment across different environments.

Amazon EventBridge Scheduler: EventBridge Scheduler allows you to create a recurring schedule that can trigger tasks (like running your cron jobs) at specific times or intervals. It provides fine-grained control over scheduling and integrates seamlessly with AWS services.

AWS Fargate: Fargate is a serverless compute engine for containers that removes the need to manage EC2 instances. It allows you to run containers without worrying about the underlying infrastructure. Fargate is ideal for running jobs that can vary in duration, like cron jobs, as it scales automatically based on the task's requirements.

Why Not Other Options?:

Option A (Lambda): While AWS Lambda could handle short-running cron jobs, it has limitations in terms of execution duration (maximum of 15 minutes) and might not be suitable for jobs that run up to 20 minutes.

Option B (AWS Batch on ECS): AWS Batch is more suitable for batch processing and workloads that require complex job dependencies or orchestration, which might be more than what is needed for simple cron jobs.

Option D (Step Functions with Wait State): While Step Functions provide orchestration capabilities, this approach would introduce unnecessary complexity and overhead compared to the straightforward scheduling with EventBridge and running on Fargate.

AWS References:

Amazon EventBridge Scheduler- Details on how to schedule tasks using Amazon EventBridge Scheduler.

AWS Fargate- Information on how to run containers in a serverless manner using AWS Fargate.

Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data in AWS. To scale across Regions and accounts in AWS Organizations, Macie supports delegated administration, automated sensitive data discovery, and multi-account aggregation through a centralized admin account.

Amazon RDS for Oracle is a managed database service, which significantly reduces operational overhead compared to running Oracle on EC2 or on-premises. AWS Secrets Manager natively integrates with RDS and supports automatic, scheduled password rotation with minimal setup. You can configure the rotation schedule (including yearly), and Secrets Manager will handle the secure password storage and rotation workflow for you.

AWS Documentation Extract:

"AWS Secrets Manager helps you protect access to your applications, services, and IT resources without the upfront investment and on-going maintenance costs of operating your own infrastructure. You can configure automatic rotation for supported databases such as Amazon RDS for Oracle."

(Source: AWS Secrets Manager documentation)

A, C, D: These solutions require custom scripting, Lambda, and alarms, leading to more operational overhead.

This question centers on improving scalability and global access performance.

Auto Scaling groups enable EC2 instances to scale dynamically in response to demand, ensuring availability during peak hours without manual intervention. Amazon RDS read replicas offload read traffic, improving read throughput and reducing latency on the primary database instance. Deploying Amazon CloudFront with S3 as origin accelerates delivery of static transaction documents globally by caching content at edge locations, reducing latency for users worldwide.

Option B focuses on vertical scaling (larger instances) and caching with ElastiCache, but it does not address global content delivery optimally. AWS Global Accelerator accelerates network traffic but is better suited for accelerating TCP and UDP traffic; CloudFront is generally preferred for HTTP content delivery.

Option C migrates workloads to Lambda and Aurora global databases, which is an advanced and potentially costly redesign that may not be necessary. Option D suggests moving to ECS and multi-AZ RDS but does not address global content delivery efficiently.

Therefore, option A uses proven scalability and caching best practices aligned with AWS Well-Architected Framework pillars for performance and operational excellence.

EMR managed scaling dynamically resizes the cluster by adding or removing nodes based on the workload. This feature helps minimize idle time and reduces costs by scaling the cluster to meet processing demands efficiently.

Option A:Increasing the number of core nodes might increase idle time further, as it does not address the root cause of underutilization.

Option C:Migrating the job to Lambda is infeasible for large analytics jobs due to resource and runtime constraints.

Option D:Changing to memory-optimized instances may not necessarily reduce idle time or optimize costs.

AWS Documentation References:

EMR Managed Scaling

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.

To ensure ALB access only via CloudFront, AWS prescribes restricting the origin to traffic from CloudFront. For ALB origins, the standard pattern is to allow inbound to the ALB only from CloudFront edge IP ranges using security groups or ALB listener rules. Network ACLs are coarse and stateless and add operational burden. CloudFront does not have a “VPC origin” object; instead, CloudFront references the ALB DNS name. By limiting the ALB’s security group to CloudFront IP ranges, direct client access to the ALB is blocked while CloudFront remains permitted. This aligns with security best practices to “restrict origin access to only CloudFront” and use SGs for instance/ALB layer controls.

To process each form exactly once and ensure no data is lost, using an Amazon SQS FIFO (First-In-First-Out) queue is the most appropriate solution. SQS FIFO queues guarantee that messages are processed in the exact order they are sent and ensure that each message is processed exactly once. This ensures data consistency and reliability, both of which are crucial for processing user-submitted forms without data loss.

SQS acts as a buffer between the web application server and the worker tier, ensuring that submitted forms are stored reliably and forwarded to the worker tier for processing. This also decouples the application, improving its scalability and resilience.

Option B (API Gateway): API Gateway is better suited for API management rather than acting as a message queue for form processing.

Option C (SQS Standard Queue): While SQS Standard queues offer high throughput, they do not guarantee exactly-once processing or the strict ordering needed for this use case.

Option D (Step Functions): Step Functions are useful for orchestrating workflows but add unnecessary complexity for simple message queuing and form processing.

AWS References:

Amazon SQS FIFO Queues

Decoupling Application Tiers Using Amazon SQS

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

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.

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.

Option Ais the simplest solution, using DynamoDB Streams and Lambda for real-time ingestion into S3.

Options B, C, and Dadd unnecessary complexity with Data Firehose or Kinesis.

AWS Site-to-Site VPN is a cost-effective way to securely connect your on-premises data center with AWS resources. In this scenario:

Applications in private subnetsrequire access to the API hosted in the on-premises data center.

ASite-to-Site VPN connectionis a secure and cost-efficient option to route traffic between the VPC and on-premises resources.

Transit GatewayandPrivateLinkare not cost-effective for this use case.

NAT Gatewayonly provides internet access for private subnets, which is not suitable for reaching an on-premises resource.

AWS Documentation Reference:

AWS Site-to-Site VPN

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.

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.

AWS IAM Identity Center:

IAM Identity Centerprovides centralized access management for multiple AWS accounts within an organization and integrates seamlessly with existing identity providers (IdPs) throughSAML 2.0 federation.

It allows users to authenticate using their existing IdP credentials and gain access to AWS resources without the need to create and manage separate IAM users in each account.

IAM Identity Centeralso simplifies provisioning and de-provisioning users, as it can automatically synchronize users and groups from the external IdP to AWS, ensuring secure and managed access.

Integration with Existing IdP:

The solution involves configuringIAM Identity Centerto connect to the company's IdP using SAML. This setup allows employees to log in with their existing credentials, reducing the complexity of managing separate AWS credentials.

Once connected,IAM Identity Centerhandles authentication and authorization, granting users access to the AWS accounts based on their assigned roles and permissions.

Why the Other Options Are Incorrect:

Option A: Creating separateIAM usersfor each employee is not scalable or efficient. Managing thousands of IAM users across multiple AWS accounts introduces unnecessary complexity and operational overhead.

Option B: Using AWSroot userswith synchronized passwords is a security risk and goes against AWS best practices. Root accounts should never be used for day-to-day operations.

Option D:AWS Resource Access Manager (RAM)is used for sharing AWS resources between accounts, not for federating access for users across accounts. It doesn’t provide a solution for authentication via an external IdP.

AWS References:

AWS IAM Identity Center

SAML 2.0 Integration with AWS IAM Identity Center

By setting upIAM Identity Centerand connecting it to the existing IdP, the company can efficiently manage access for thousands of employees across multiple AWS accounts with a high degree of operational efficiency and security. Therefore,Option Cis the best solution.

AWS CloudTrail Lakeis a fully managed service that allows the collection, storage, and querying ofCloudTrail eventsfor both AWS and non-AWS services. CloudTrail Lake can be customized to collect logs from various sources, ensuring a centralized audit solution. It also supports long-term storage, so logs can be retained for 7 years, meeting the compliance requirement.

Option A (Data Lake): Setting up a data lake in S3 introduces unnecessary operational complexity compared to CloudTrail Lake.

Option C (Ingest non-AWS services into CloudTrail): CloudTrail Lake is better suited for this task with less operational overhead.

Option D (CloudWatch Logs): While CloudWatch can store logs, CloudTrail Lake is specifically designed for API auditing and storage.

AWS References:

AWS CloudTrail Lake

Explanation (AWS Docs):

DynamoDB has a built-in feature called Time to Live (TTL) which automatically deletes expired items without manual intervention. This requires adding a timestamp attribute and setting a TTL on the table. This is the lowest operational overhead approach.

“You can use DynamoDB TTL to automatically delete items after a specified time, reducing storage costs and administrative overhead.”

— DynamoDB TTL

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.

Provisioned IOPS SSD (io1 or io2) volumes are designed to deliver predictable, high performance for I/O-intensive workloads such as databases. They offer consistent, low-latency performance and durability, making them ideal for business-critical applications that cannot tolerate latency or data loss.

Amazon RDS Proxy is a fully managed database proxy for RDS that makes applications more scalable, more resilient to database failures, and more secure. RDS Proxy pools and shares database connections, allowing applications to open and close connections as needed without overwhelming the database. This is the recommended solution when the application cannot be modified to use connection pooling itself.

AWS Documentation Extract:

"Amazon RDS Proxy helps manage database connections to improve application scalability and performance. It pools connections and shares them among application clients, which can mitigate issues caused by opening and closing many database connections."

(Source: Amazon RDS Proxy documentation)

A: Upgrading the instance does not solve connection inefficiency and can be cost-ineffective.

B: Increasing IOPS only helps if storage is a bottleneck, not if connections are the issue.

D: Multi-AZ improves availability, not connection management.

Comprehensive and Detailed Explanation:

To ensure that log files are immutable and cannot be deleted or overwritten for a specified retention period, Amazon S3 offers Object Lock in Compliance Mode. When enabled:

Compliance Mode: Ensures that a protected object version can't be overwritten or deleted by any user, including the root user in your AWS account, for the duration of the retention period. AWS Documentation

S3 Versioning: Must be enabled on the bucket to use Object Lock. This allows multiple versions of an object to be stored, ensuring that previous versions are preserved and protected. Amazon Web Services, Inc.

By enabling S3 Versioning and applying Object Lock in Compliance Mode with a 1-year retention period, the company can meet regulatory requirements to retain log data securely and prevent any modifications or deletions during that time.

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.

Elastic Fabric Adapter (EFA) is a network interface for Amazon EC2 instances that enables high throughput, low-latency networking, and OS-bypass capabilities. EFAs are designed for applications that require fast inter-node communications, such as HPC, ML, and real-time analytics. EFA provides significantly lower latency than traditional networking, which is ideal for real-time streaming and processing workloads.

Reference Extract from AWS Documentation / Study Guide:

"Elastic Fabric Adapter (EFA) provides low-latency, high-throughput network communications required by high-performance computing applications, enabling fast, scalable, and tightly-coupled workloads on AWS."

Source: AWS Certified Solutions Architect – Official Study Guide, EC2 Networking section.

The issue in this case is related to the processing tier, where EC2 instances are overwhelmed at peak times, causing delays.Option D, using anAmazon EC2 Auto Scaling target tracking policybased on theApproximateNumberOfMessagesin the SQS queue, is the best solution.

Auto Scaling with Target Tracking:

Target tracking policiesdynamically scale out or in based on a specific metric. For this use case, you can monitor theApproximateNumberOfMessagesin the SQS queue. When the number of messages (orders) in the queue increases, the Auto Scaling group will scale out more EC2 instances to handle the additional load, ensuring that the queue doesn’t build up and cause delays.

This solution is ideal for handling variable and unpredictable peak times, as Auto Scaling can automatically adjust based on real-time load rather than scheduled times.

Why Not the Other Options?:

Option A (Scheduled Scaling): Scheduled scaling works well for predictable peak times, but this company experiencesunpredictable peak usage, making scheduled scaling less effective.

Option B (ElastiCache for Redis): Adding a caching layer would help if DynamoDB were the bottleneck, but in this case, the issue is CPU overload on EC2 instances in the processing tier.

Option C (CloudFront): CloudFront would help cache static content from the web tier, but it wouldn’t resolve the issue of the processing tier’s overloaded EC2 instances.

AWS References:

Amazon EC2 Auto Scaling Target Tracking

Amazon SQS ApproximateNumberOfMessages

Key Requirements:

Serverless architecture.

Handle traffic bursts with high availability.

Process orders asynchronouslyin the order they are received.

Analysis of Options:

Option A:Amazon SNS delivers messages to subscribers. However, SNS does not ensure ordering, making it unsuitable for FIFO (First In, First Out) requirements.

Option B:Amazon SQS FIFO queues support ordering and ensure messages are delivered exactly once. AWS Lambda functions can be triggered by SQS to process messages asynchronously and efficiently. This satisfies all requirements.

Option C:Amazon SQS standard queues do not guarantee message order and have "at-least-once" delivery, making them unsuitable for the FIFO requirement.

Option D:Similar to Option A, SNS does not ensure message ordering, and using AWS Batch adds complexity without directly addressing the requirements.

AWS References:

Amazon SQS FIFO Queues

AWS Lambda and SQS Integration

DynamoDB Accelerator (DAX) is a fully managed, in-memory cache for DynamoDB that delivers up to a 10x performance improvement—even at millions of requests per second. DAX requires minimal changes to existing applications and offloads the read workload from DynamoDB during report generation.

Reference Extract:

"DAX provides in-memory acceleration for DynamoDB tables, reducing response times from milliseconds to microseconds with minimal application changes."

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

Field-level encryption in Amazon CloudFront provides end-to-end encryption for specific data fields (e.g., credit card numbers, social security numbers). It ensures that sensitive fields are encrypted at the edge before being forwarded to the origin, and only the authorized application with the private key can decrypt them.

This adds a layer of protection beyond HTTPS, which encrypts the whole payload but not individual fields. Signed URLs and cookies are for access control, not encryption. Setting HTTPS Only is a good practice but does not satisfy the field-specific encryption requirement.

The most cost-effective and low-maintenance solution to aggregate S3 data from multiple accounts within the same AWS Region is to use Amazon S3 Same-Region Replication (SRR).

From AWS S3 Documentation:

"S3 Same-Region Replication (SRR) automatically replicates new objects between buckets in the same AWS Region. SRR is commonly used to aggregate logs into a single bucket, simplify data aggregation, and meet compliance requirements."

(Source: Amazon S3 User Guide – Replication Overview)

Key benefits of using SRR for this use case:

Zero operational overhead – No Lambda, scripts, or custom code

Fully managed – AWS handles all replication automatically

Secure and efficient – No data leaves the us-west-2 Region

Supports cross-account replication – With proper IAM roles and permissions

Low-cost – Compared to custom ETL pipelines or Lambda solutions

In contrast:

Option A (Lifecycle policies) do not support copying, only expiration, transitions, or deletions.

Option C (scripts) requires custom scheduling and maintenance, increasing operational overhead.

Option D (Lambda) adds complexity and cost and is not as scalable or hands-off as SRR.

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.

Amazon S3 is the most scalable and cost-optimized object storage for large amounts of data. It integrates natively with Amazon SageMaker, which allows direct access to S3 data for machine learning training jobs without the need to copy data elsewhere. The S3 Intelligent-Tiering storage class further optimizes storage costs for data with unknown or changing access patterns, while maintaining immediate availability for ML training.

Copying to FSx for Lustre or EFS adds unnecessary complexity and cost unless ultra-high throughput POSIX file access is specifically required, which is not mentioned here.

AWS Documentation Extract:

"Amazon S3 provides cost-effective storage for ML data sets, and Amazon SageMaker can directly access data in S3. S3 Intelligent-Tiering automatically moves data between frequent and infrequent access tiers, optimizing costs for changing data access patterns."

(Source: AWS S3 and SageMaker documentation)

A, D: FSx for Lustre or EFS may be used in specialized cases but are not cost-optimized for general large-scale ML data storage.

C: SageMaker notebook instance storage is not designed for large-scale data ingestion or long-term storage.

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​.

SAML 2.0-based federation allows organizations to integrate their on-premises Active Directory with AWS, enabling Single Sign-On (SSO) for AWS Management Console and AWS CLI/API access. This lets users continue using their existing AD credentials, removing the need to manage separate identities for AWS and on-premises systems. Mapping roles to AD groups provides granular access control and seamless management.

Reference Extract from AWS Documentation / Study Guide:

"AWS supports SAML 2.0 for federated access, enabling integration with Active Directory or other identity providers to provide single sign-on to AWS resources without creating separate IAM users."

Source: AWS Certified Solutions Architect – Official Study Guide, Identity and Access Management section.

S3 Object Lock in Compliance Mode prevents objects from being deleted or overwritten for a fixed, specified retention period. Compliance Mode is specifically designed to meet regulatory requirements, ensuring that no user, including the root user, can modify or delete the object during the retention period.

Reference Extract:

"S3 Object Lock in compliance mode protects objects from being deleted or overwritten during the specified retention period, helping meet regulatory retention requirements."

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Object Lock and Compliance section.

Edge-optimized API Gateway endpoints utilize the Amazon CloudFront global network to decrease latency for clients globally. This setup ensures that the request is routed to the closest edge location, significantly reducing response time and improving performance for worldwide users.

IAM Identity Center (formerly AWS SSO) is designed for:

Federated access from external directories (e.g., Active Directory, Okta)

Centralized permission management

Support for MFA

Granular control via Attribute-based access control (ABAC)

“IAM Identity Center allows you to manage SSO access to AWS accounts and business applications centrally. You can assign users and groups permissions based on directory attributes such as Region and job role.”

— IAM Identity Center Docs

This option ensures:

Federated, centralized access

Region-specific permissions

MFA and role mapping via existing directory service

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.

Comprehensive and Detailed Step-by-Step Explanation:

The company needsautomatic monitoringandnotificationsforsecurity violationsin Amazon Redshift clusters.

Option A:❌

Create an Amazon EventBridge rule that uses the Redshift clusters as the source. Create an AWS Lambda function to evaluate the Redshift cluster security configuration. Configure the Lambda function to notify the company of any violations of the security policies. Add the Lambda function as a target of the EventBridge rule.

EventBridgecan trigger actionsbased on events.

However, itdoes not track changesin Redshift security configurations.

Why not?AWS Configis bettersuited forcompliance monitoring.

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.

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.

Aurora Replicas: Provide read scalability and high availability. They allow offloading read traffic from the primary database instance.

AWS Global Accelerator: Provides improved availability and performance by routing user requests to the optimal endpoint using AWS’s global network.

“Aurora Replicas can be used to increase read scalability and availability.”

— Aurora Replicas

“AWS Global Accelerator improves the availability and performance of your applications with global users.”

— AWS Global Accelerator

Together, these enhance both the database and network layer resilience.

AmazonKinesis Data Streamsis the best choice for this scenario:

Message throughput: Kinesis Data Streams supports high throughput with enhanced fan-out and dedicated throughput for consumers.

Large message size: Supports message sizes up to 1 MB, meeting the 500 KB requirement.

Message retention: Data streams can retain messages for up to 365 days.

Strict ordering: Guarantees message ordering within shards.

Why Other Options Are Not Ideal:

Option B:

While SQS FIFO supports strict ordering, SNS topics do not. SNS also does not natively support message retention or strict ordering across consumers.Does not meet requirements.

Option C:

EventBridge does not provide strict ordering guarantees or message retention beyond 24 hours.Does not meet requirements.

Option D:

SNS topics with Data Firehose are not designed for use cases requiring strict ordering or long message retention.Does not meet requirements.

AWS References:

Amazon Kinesis Data Streams:AWS Documentation - Kinesis Data Streams

AWS Messaging Services Comparison:AWS Documentation - Messaging Services

The best solution to enforce encryption at rest for Amazon EBS volumes is to use anIAM policyto restrict the creation of unencrypted volumes. To automatically identify and remediate unencryptedvolumes, you can useAWS Configrules, which continuously monitor the compliance of resources, andAWS Systems Managerto automate the remediation by encrypting existing unencrypted volumes. This setup requires minimal administrative overhead while ensuring compliance.

Option B (KMS): KMS is for managing encryption keys, but Config and Systems Manager provide a better solution for automatic detection and enforcement.

Option C (Macie): Macie is for data classification and is not suitable for this use case.

Option D (Inspector): Inspector is used for security vulnerabilities, not encryption compliance.

AWS References:

AWS Config Rules

AWS Systems Manager

The ALB must be in a public subnet to receive internet traffic. The EC2 instances and the RDS database should be in private subnets to prevent direct internet access, minimizing the attack surface. This aligns with AWS security best practices for web application architectures.

Reference Extract:

"Internet-facing ALBs should be placed in public subnets; EC2 instances and RDS databases should be in private subnets to restrict direct internet access."

Source: AWS Certified Solutions Architect – Official Study Guide, Network Security and Design section.

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.

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 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.

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

Option B (Amazon FSx for Lustre): FSx for Lustre is optimized for high-performance file systems required by HPC workloads, scaling to millions of IOPS and supporting parallelized data access.

Option E (Cluster Placement Group with Auto Scaling): A cluster placement group ensures low-latency communication between EC2 instances, critical for HPC workloads. Amazon EMR simplifies running large-scale analytics jobs.

Amazon FSx for Lustre Documentation,AWS Placement Groups Documentation

A warm pool is an Auto Scaling feature that keeps instances in a pre-initialized state so they can quickly join the active group when scaling is required. This reduces the time needed for instance bootstrapping and makes new capacity available almost instantly. Option A only increases capacity limits but does not address slow boot times. Option C merely extends grace periods without solving the delay. Option D forces overprovisioning, which is wasteful and not aligned with cost optimization. Using a warm pool (B) directly addresses the problem by reducing response time to scaling events.

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.

With AWS Shield Advanced, you can add multiple protected resources (like ALBs) to a protection group and choose an aggregation type for mitigation and billing.

Sum aggregation provides combined protection for all resources in the group during blue/green deployment.

“You can add multiple resources to a Shield Advanced protection group and choose an aggregation type. Sum aggregation provides combined protection across all resources.”

— Shield Advanced Protection Groups

This ensures the new ALB inherits protection and avoids additional configuration during deployment.

The requirement is to route users to the nearest AWS Region where the application is deployed. The best solution is to use Amazon Route 53 with a geolocation routing policy, which routes traffic based on the geographic location of the user making the request.

Geolocation Routing: This routing policy ensures that users are directed to the resources (in this case, EC2 instances) that are geographically closest to them, thereby reducing latency and improving the user experience.

Application Load Balancer (ALB): Within each Region, an internet-facing Application Load Balancer (ALB) is used to distribute incoming traffic across multiple EC2 instances in different Availability Zones. ALBs are designed to handle HTTP/HTTPS traffic and provide advanced features like content-based routing, SSL termination, and user authentication.

Why Not Other Options?:

Option B (Geoproximity + NLB): Geoproximity routing is similar but more complex as it requires fine-tuning the proximity settings. A Network Load Balancer (NLB) is better suited for TCP/UDP traffic rather than HTTP/HTTPS.

Option C (Multivalue Answer Routing + ALB): Multivalue answer routing does not direct traffic based on user location but rather returns multiple values and lets the client choose. This does not meet the requirement for geographically routing users.

Option D (Weighted Routing + NLB): Weighted routing splits traffic based on predefined weights and does not consider the user's geographic location. NLB is not ideal for this scenario due to its focus on lower-level protocols.

AWS References:

Amazon Route 53 Routing Policies- Detailed explanation of the various routing policies available in Route 53, including geolocation.

Elastic Load Balancing- Information on the different types of load balancers in AWS and when to use them.

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.

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.

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

Step A:AWS WAF with managed rules protects the API against application-layer attacks, such as SQL injection and cross-site scripting (XSS).

Step C:Amazon Cognito provides secure authentication and supports federation with social IdPs using OIDC or SAML. It integrates seamlessly with API Gateway.

Option B:AWS Shield Advanced provides DDoS protection, which is not explicitly required in this scenario.

Option D:API keys provide identification, not authentication, and are insufficient for this use case.

Option E:IP filters in WAF are overly restrictive for federated authentication scenarios.

AWS Documentation References:

Amazon Cognito Federation

AWS WAF Managed Rules

Use SQS FIFO to durably persist orders, guarantee order processing semantics, and decouple producers/consumers. FIFO queues provide “exactly-once processing” with message deduplication and “preserve message order.” Visibility timeouts and retries ensure messages are “processed eventually” without being lost; failed messages go to a DLQ for later reprocessing. This pattern aligns with Well-Architected reliability guidance to “queue work to protect against overload and failures” and to ensure “durable, idempotent processing” with retry and backoff. ALB/RDS Proxy/read replicas (A, B) improve availability/connection management but do not guarantee durable handoff or exactly-once processing. SNS (D) is pub/sub and does not provide FIFO semantics in this option, nor a DLQ per subscription for exactly-once. Therefore, frontends write orders to an SQS FIFO queue; backend workers in an Auto Scaling group consume, process idempotently, and use a DLQ for poison messages to meet “no lost orders,” “eventual processing,” and “exactly-once” requirements.

Using a Lambda function as part of the Kinesis Data Firehose pipeline allows for real-time detection and masking of PII before data is written to S3. This ensures that PII is never stored in its raw form in the data lake.

Option B:Amazon Macie can scan and classify data but does not provide in-line PII masking for data ingestion.

Option C:Server-side encryption secures data but does not mask PII.

Option D:CloudHSM is unnecessary for PII masking and adds complexity without addressing the requirements.

AWS Documentation References:

Using Lambda with Kinesis Data Firehose

A. ElastiCache:Provides in-memory caching, not suitable for persistent, scalable databases.

B. DynamoDB Streams + Lambda:Manages replication manually, increasing latency and operational complexity.

C. Aurora Global Database:Provides high availability but does not support active-active configuration.

D. DynamoDB Global Tables:Provides active-active configuration and sub-second RPO.

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.

To achieve a 60-second RTO, pre-warming the DR environment (including running EC2 instances and Route 53 health checks) is essential. Active/passive failover using Route 53 with health checks ensures fast redirection when the primary Region becomes unavailable. S3 cross-region replication ensures document availability.

Edge-Optimized API: Designed for global users by routing requests through CloudFront's edge locations, reducing latency.

Content Encoding: Enabling content encoding compresses data, further optimizing performance by decreasing payload size.

Caching: Adding API Gateway caching reduces the number of calls to Lambda and database backends, improving latency.

Reserved Concurrency: Although useful, this does not directly affect latency for transferring static and dynamic content.

AWS API Gateway Edge-Optimized APIs Documentation

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. On-premises tool via internet:Incurs high costs due to large data transfers over the internet.

B. AWS Region tool via internet:Does not utilize Direct Connect, leading to potential latency and higher costs.

C. On-premises tool via Direct Connect:Adds latency for querying and visualization.

D. AWS Region tool via Direct Connect:Reduces latency and leverages Direct Connect for optimized data transfer costs.

The issue described in the question, where incoming traffic seems to favor one EC2 instance, is often caused by session affinity (also known as sticky sessions) being enabled on the Application Load Balancer (ALB). When session affinity is enabled, the ALB routes requests from the same client to the same EC2 instance. This can cause an imbalance in traffic distribution, leading to performance bottlenecks on certain instances while others remain underutilized.

To resolve this issue, disabling session affinity ensures that the ALB distributes incoming traffic evenly across all EC2 instances, allowing better load distribution and reducing latency. The ALB will rely on its round-robin or least outstanding requests algorithm (depending on the configuration) to distribute traffic more evenly across instances.

Option B (Network Load Balancer): The NLB is designed for Layer 4 (TCP) traffic and low latency use cases, but it is not needed here as the problem is with load balancing logic at the application layer (Layer 7). The ALB is more appropriate for HTTP/HTTPS traffic.

Option C (Increase EC2 Instances): Adding more EC2 instances does not solve the root issue of uneven traffic distribution.

Option D (Health Check Frequency): Adjusting health check frequency won't address the imbalance caused by session affinity.

AWS References:

Application Load Balancer Sticky Sessions

When operating in disconnected or limited-connectivity environments, such as ships at sea, AWS recommends using AWS Snowball Edge devices to host local compute and storage workloads. Snowball Edge supports Amazon EC2 instances and AWS IoT Greengrass, and can run Amazon EKS Anywhere for local Kubernetes cluster deployments.

From AWS Documentation:

“You can use AWS Snowball Edge devices to run compute-intensive applications in remote or disconnected locations. Snowball Edge devices support running Amazon EC2 instances and Amazon EKS Anywhere clusters.”

(Source: AWS Snow Family – Developer Guide)

Why A is correct:

Snowball Edge provides local compute and storage even without internet connectivity.

Multiple Snowball Edge devices can be clustered together for high availability and failover.

Fully self-contained environment suitable for ships or field operations.

Why the others are incorrect:

B, C, D require network connectivity to AWS Regions or Zones (Local Zone, Outposts, Wavelength), which is not available at sea. These options cannot operate fully disconnected.

Using S3 gateway endpoints allows private and cost-free access to S3 without routing traffic through a NAT gateway. NAT gateway traffic incurs charges, especially when used across multiple Availability Zones.

By using an S3 gateway endpoint, EC2 instances in private subnets can access S3 directly without needing internet access, reducing both data transfer and NAT gateway costs.

Interface endpoints are more expensive and typically used for services like API Gateway or Systems Manager.

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

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.

Comprehensive and Detailed Explanation:

To securely migrate an on-premises Oracle database to Amazon Aurora, the following steps are recommended:

Use AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (AWS DMS): AWS SCT helps convert the source database schema to a format compatible with the target database (Aurora). AWS DMS facilitates the actual data migration, ensuring minimal downtime and data integrity.

Use AWS Site-to-Site VPN: Establishing a Site-to-Site VPN connection provides a secure and encrypted tunnel between the on-premises environment and AWS. This ensures that data transferred during the migration is protected against interception and unauthorized access.

Purchasing a Compute Savings Plan for the minimum baseline usage ensures cost savings. Using On-Demand Instances for peak times ensures flexibility without over-provisioning. S3 Lifecycle policies enable automatic transition of objects to lower-cost storage classes such as S3 Glacier orS3 Intelligent-Tiering, further reducing storage costs.

The requirement is to monitor network metrics such as total packet loss and round-trip time (RTT) between on-premises and AWS over Site-to-Site VPN with minimal operational overhead. AWS CloudWatch Network Monitor (formerly known as VPC Network Manager) provides a managed solution to monitor connectivity, including packet loss and latency, between AWS and on-premises networks. This solution does not require managing any additional infrastructure like EC2 instances or Lambda functions and thus reduces operational overhead significantly.

CloudWatch Network Monitor leverages AWS-managed probes and integrates natively with CloudWatch dashboards and alarms, enabling automated, centralized monitoring of network health. This aligns with the AWS Well-Architected Framework’s operational excellence pillar by minimizing manual intervention and enabling proactive detection of network issues.

Option B, C, and D involve creating custom probes with EC2, Lambda, or Batch jobs, which increases complexity, cost, and maintenance effort. They also require scheduling, script management, and additional monitoring infrastructure.

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.

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.

When using imported key material with AWS KMS, you maintain control over the key lifecycle. AWS KMS allows you to import new key material into an existing KMS key (of type "external" or "imported"), thus rotating the key material without changing the key ID or ARNs. This enables applications to continue using the same key for cryptographic operations without disruption.

You can also set an expiration time for the old key material, after which AWS KMS deletes the old material and requires new key material to be imported, enforcing regular rotation per your compliance requirements.

AWS Documentation Extract:

"To rotate imported key material, you can re-import new key material into the same KMS key. This retains the same key ID and ARNs so applications are unaffected. You can set an expiration time for imported key material and replace it as needed, ensuring compliance with your rotation policy."

(Source: AWS Key Management Service Developer Guide, Importing Key Material, Rotating Key Material)

Other options:

A: You cannot create a new KMS key with the same key ID as an existing one.

B: Deleting and recreating the key disrupts application access because the key ID changes.

D: Automatic rotation is only available for AWS-managed keys, not for imported key material.

Why Option B is Correct:

HTTP Proxy Integration: Passes XML requests directly to the application, which already supports XML.

JSON Conversion: An AWS Lambda function converts JSON requests to XML and calls the application.

API Gateway: Acts as a front end to handle JSON requests and integrates seamlessly with Lambda for the transformation process.

Why Other Options Are Not Ideal:

Option A: Suggests using API Gateway mappings to convert JSON to XML. API Gateway mapping templates are limited in functionality and are not ideal for complex transformations.

Option C and D: Use HTTP custom integration unnecessarily, which adds complexity without additional benefits.

AWS References:

Amazon API Gateway Integration:AWS Documentation - API Gateway Integration

AWS Lambda:AWS Documentation - Lambda

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.

For an application requiring TCP communication and high availability:

Network Load Balancer (NLB)is the best choice for load balancing TCP traffic because it is designed for handling high-throughput, low-latency connections.

Auto Scaling groupensures that the application can automatically scale based on demand, adding or removing EC2 instances as needed, which is crucial for handling user growth.

Option C (Application Load Balancer): ALB is primarily for HTTP/HTTPS traffic, not ideal for TCP.

Option D (Manual scaling): Manually adding instances does not provide the automation or scalability required.

Option E (Gateway Load Balancer): GLB is used for third-party virtual appliances, not for direct application load balancing.

AWS References:

Network Load Balancer

Auto Scaling Group

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.

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

Amazon Timestreamis purpose-built for storing and analyzing time-series data like telemetry.

Option Aleverages Timestream, SageMaker for ML, and QuickSight for visualization, meeting all requirements with minimal complexity.

Option Binvolves more complex DynamoDB-EMR integration.

Option Cuses Neptune, which is designed for graph databases, not telemetry data.

Option Dincorrectly uses Athena for visualization instead of QuickSight.

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.

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.

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

Amazon RDS for MySQL Reserved Instances provide significant savings over on-demand pricing when you plan to run the database for long periods. This is the most cost-effective option for non-production, long-running managed MySQL workloads.

Reference Extract:

"Reserved Instances provide a significant discount compared to On-Demand pricing and are recommended for steady-state workloads that run for an extended period."

Source: AWS Certified Solutions Architect – Official Study Guide, RDS Cost Optimization section.

Amazon FSx for NetApp ONTAP fully supports native NetApp SnapMirror replication, making it the most efficient and reliable option for migrating NetApp data from on-premises to AWS.

From AWS Documentation:

“You can use SnapMirror to replicate data from your on-premises NetApp systems to FSx for ONTAP for seamless, block-level, incremental transfers. This provides a highly efficient and performant method for migration, especially for large datasets.”

(Source: Amazon FSx for NetApp ONTAP – Migration Guide)

Why Option C is correct:

SnapMirror offers block-level replication, making it highly efficient for millions of small files.

It supports NFS and SMB file shares, preserving directory structures and permissions.

Reduces cutover time and allows for incremental syncs.

Uses the existing 10 Gbps network for fast transfers.

Why the other options are incorrect:

Option A (DataSync): Suitable for many file-based migrations but less efficient for very large datasets with millions of small files compared to SnapMirror.

Option B (Storage Gateway): Volume Gateway is not used for full-scale file migrations; it's for hybrid cloud access.

Option D (Snowball Edge): Useful for offline migrations, but online SnapMirror is more efficient and avoids shipping delays.

Amazon EFS allows automatic transitions to Infrequent Access (IA) and back to Standard when accessed again using the lifecycle policy.

By specifying the bursting throughput mode, throughput scales automatically with file system size.

“With EFS Lifecycle Management, you can automatically move files to EFS Infrequent Access storage and automatically return them to Standard storage when accessed.”

“The Bursting Throughput mode scales with your file system size.”

— Amazon EFS Lifecycle Management

This option matches all requirements:

Auto transition to IA after 30 days.

Auto transition back to Standard on access.

Bursting throughput for scaling with file system size.

The best practice for granting AWS service access to EC2 instances is to assign IAM roles with the least privilege necessary. IAM roles attached to EC2 instances provide temporary credentials automatically rotated by AWS, avoiding hard-coded credentials in the application code.

Option C adheres to security best practices by assigning an IAM role scoped with the minimum permissions needed to access the S3 bucket.

Option A grants administrative access, which violates least privilege principles. Options B and D use IAM users with static credentials, increasing the risk of credential exposure and requiring manual rotation, which is not recommended.

Amazon Macieis purpose-built for detecting PII in S3.

Option Auses EventBridge to filter SensitiveData findings and notify via SNS, meeting the requirements.

Options B and Dinvolve GuardDuty, which is not designed for PII detection.

Option Cuses SQS, which is less suitable for immediate notifications.

To protect against a Regional failure, the application must be deployed in multiple Regions. Amazon Route 53 DNS failover with health checks allows traffic to be automatically routed to a healthy Region when the primary becomes unavailable, meeting high availability and disaster recovery requirements.

When an AWS Lambda function is invoked, especially after periods of inactivity, it may experience cold starts that delay execution. As demand increases, the scaling behavior and latency of Lambda can affect performance. Provisioned Concurrency is an AWS feature designed specifically to solve this issue.

From AWS Documentation:

“Provisioned Concurrency keeps functions initialized and hyper-ready to respond in double-digit milliseconds.”

(Source: AWS Lambda Developer Guide – Managing Concurrency)

Why Option D is correct:

Provisioned Concurrency ensures that a specified number of Lambda function instances are always warm and ready to serve requests, eliminating cold start latency.

It's cost-effective for workloads with consistent usage patterns, like real-time simulations for a small user group.

Maintains scalability and low overhead of Lambda without moving to managed container or EC2 platforms.

Why the other options are less optimal:

Option A (Fargate) and Option B (EC2): Introduce more infrastructure and higher ongoing costs for a small team with likely intermittent usage.

Option C (AWS Batch): Ideal for batch jobs, not real-time simulations; also incurs higher latency due to job queuing.

The requirements include:

Scalability: The solution must scale as video files are uploaded.

Long-running tasks: Processing tasks can take up to 30 minutes. AWS Lambda has a maximum execution time of 15 minutes, which rules out options that involve Lambda performing all the processing.

Serverless and event-driven architecture: Ensures cost-effectiveness and high availability.

Analysis of Options:

Option A:

AWS Lambda has a 15-minute timeout, which cannot support tasks that take up to 30 minutes.

EventBridge Scheduler is unnecessary for monitoring files when native event notifications are available.Not a valid choice.

Option B:

AWS Step Functions and AWS Fargate can handle long-running processes, but Amazon EFS is not the ideal storage for uploaded video files in a serverless architecture.

Processing tasks triggered by EFS events are not a common pattern and may introduce complexities.Not the best practice.

Option C:

Amazon S3 is used for storing uploaded files, which integrates natively with event-driven services like EventBridge and Step Functions.

Amazon S3 event notifications trigger a Step Functions workflow, which can orchestrate Fargate tasks to process large video files, meeting the scalability and execution time requirements.Correct choice.

Option D:

Similar to Option A, AWS Lambda cannot handle long-running processes due to its 15-minute timeout.

Invoking Lambda for processing directly is not feasible for tasks that take up to 30 minutes.Not a valid choice.

AWS References:

Amazon S3 Event Notifications:AWS Documentation - S3 Event Notifications

AWS Step Functions:AWS Documentation - Step Functions

AWS Fargate:AWS Documentation - Fargate

Comparison of Storage Services:AWS Storage Options

By leveragingAmazon S3,Step Functions, andFargate, this solution provides a scalable, efficient, and serverless approach to handling video processing tasks.

The first step is to configure a delegated administrator account for IAM Access Analyzer at the organization level. Only after delegating the administrator account can you aggregate Access Analyzer findings from all member accounts into a designated audit account. This must be set up in the AWS Organizations management account.

AWS Documentation Extract:

“You must designate a delegated administrator for IAM Access Analyzer at the organization level. The delegated administrator account aggregates findings from all member accounts.”

(Source: IAM Access Analyzer documentation)

A, C, D: These steps do not establish the organization-wide aggregation required for Access Analyzer.

Creating a listener rule on theApplication Load Balancer (ALB)to return a maintenance response during the maintenance window is the most straightforward solution with the least operational overhead. The rule can be configured to match all incoming requests and return a custom response, and it can be easily removed once maintenance is complete.

Option A (Aurora table flag): This adds unnecessary complexity for a temporary maintenance response.

Option B and D (SQS or SNS): These options introduce more components than needed for a simple maintenance message.

AWS References:

ALB Listener Rules

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.

AWS Elastic Disaster Recovery (AWS DRS) enables fast, reliable, and cost-effective disaster recovery. It replicates on-premises machines to AWS using continuous block-level replication. It supports automated testing and failover, meeting aggressive RTO targets such as 15 minutes.

AWS PrivateLink enables private connectivity between VPCs and supported AWS or third-party services without exposing traffic to the public internet. It ensures secure and private communications, making it ideal for connecting internal services to SaaS applications hosted in AWS.

Amazon CloudWatch Container Insightsis designed for monitoring containerized environments like EKS. It provides native support for collecting and visualizing metrics and logs in a centralized location through CloudWatch dashboards, offering the most operationally efficient solution.

Option A:Using the CloudWatch agent provides basic metrics but lacks the specific insights required for containerized applications.

Option B:Kinesis Data Streams and Firehose add unnecessary complexity for this use case.

Option C:CloudTrail is for auditing API activity and is not designed for application metrics and log aggregation.

AWS Documentation References:

Amazon CloudWatch Container Insights

AWS PrivateLinkis the most suitable solution for providing fine-grained access control while allowing multiple VPCs, potentially across multiple accounts, to access the new application. This approach offers the following advantages:

Fine-grained control: Endpoint policies can restrict access to specific services or principals.

No need for route table updates: Unlike VPC peering or transit gateways, AWS PrivateLink does not require complex route table management.

Scalable architecture: PrivateLink scales to support traffic from multiple VPCs.

Secure connectivity: Ensures private connectivity over the AWS network, without exposing resources to the internet.

Why Other Options Are Not Ideal:

Option A:

VPC peering is not scalable when connecting multiple VPCs or accounts.

Route table management becomes complex as the number of VPCs increases.Not scalable.

Option B:

While transit gateways provide scalable VPC connectivity, they are not ideal for fine-grained access control.

Transit gateways allow connectivity but do not inherently restrict access to specific applications.Not ideal for fine-grained access control.

Option D:

Exposing the application through an ALB over the internet is not secure and does not align with the requirement to use private network resources.Security risk.

AWS References:

AWS PrivateLink:AWS Documentation - PrivateLink

AWS Networking Services Comparison:AWS Whitepaper - Networking Services

This solution allows for secure and least-privilege access with minimal operational overhead.

IAM Identity Center: AWS IAM Identity Center (formerly AWS SSO) enables you to centrally manage access to multiple AWS accounts and applications. By using IAM Identity Center, you can assign permission sets that define what users or groups can access, ensuring that only necessary permissions are granted.

Permission Sets: Permission sets in IAM Identity Center allow you to define granular access controls for specific services, such as Amazon RDS and S3. You can tailor these permissions to meet the needs of different teams, adhering to the principle of least privilege.

Group Management: By assigning users to groups and associating those groups with specific permission sets, you reduce the complexity and overhead of managing individual IAM roles and policies. This method also simplifies compliance and audit processes.

Why Not Other Options?:

Option A (IAM roles): While IAM roles can provide least-privilege access, managing multiple roles and policies across teams increases operational overhead compared to using IAM Identity Center.

Option C (Individual IAM users): Managing individual IAM users and roles can be cumbersome and does not scale well compared to group-based management in IAM Identity Center.

Option D (AWS Organizations with cross-account roles): Creating separate accounts and cross-account roles adds unnecessary complexity and overhead for this use case, where IAM Identity Center provides a more straightforward solution.

AWS References:

AWS IAM Identity Center- Overview and best practices for using IAM Identity Center.

Managing Access Permissions Using IAM Identity Center- Guide on creating and managing permission sets for secure access.

Comprehensive and Detailed Step-by-Step Explanation:

To meet the requirements of copying only relevant data as soon as possible and receiving notifications upon completion, the following steps are recommended:

Generate a Manifest File (Option A):

Action:Modify the on-premises data generation process to create a manifest file at the end of each data generation cycle. This manifest should list the names of the objects that need to be copied to Amazon S3.

Implementation:Develop a custom script that runs after data generation. This script compiles the list of relevant data files into a manifest file and uploads it to a designated S3 bucket.

Justification:Using a manifest allows AWS DataSync to transfer only the specified files, reducing unnecessary data transfer and associated costs.

docs.aws.amazon.com

Automate DataSync Task Execution (Option D):

Action:Set up an S3 Event Notification to trigger an AWS Lambda function whenever a new manifest file is uploaded to the S3 bucket.

Implementation:Configure the Lambda function to invoke the DataSync task by calling the StartTaskExecution API action, specifying the manifest file. This ensures that only the files listed in the manifest are copied from on-premises storage to Amazon S3.

Justification:This automation ensures timely data transfer as soon as relevant data is available, minimizing delays and manual intervention.

docs.aws.amazon.com

Set Up Completion Notifications (Option E):

Action:Create an Amazon SNS topic to handle notifications. Then, establish an Amazon EventBridge rule that monitors the DataSync task execution status and sends an email notification to the SNS topic when the status changes to SUCCESS or ERROR.

Implementation:Configure EventBridge to capture state changes of the DataSync task. When a task completes successfully or encounters an error, EventBridge triggers a notification to the SNS topic, which then sends an email to the subscribed recipients.

Justification:This setup provides immediate feedback on the data transfer process, allowing the analytics team to act promptly based on the success or failure of the data copy operation.

docs.aws.amazon.com

Compute Savings Plansoffer the most flexible and cost-effective solution for the production instances, as they provide significant savings (up to 66%) for both EC2 and AWS Lambda usage, while allowing flexibility in the type of instance family, size, and even region. For nonproduction instances, usingOn-Demand Instancesensures you only pay for the instances when they are running, and shutting them down during off-hours further optimizes cost.

Key AWS features:

Compute Savings Plans: Provide savings based on consistent usage, making it ideal for production environments with steady load.

On-Demand Instances: Suitable for nonproduction environments that are used intermittently. Shutting them down when not in use avoids unnecessary costs.

AWS Documentation: According to AWS's cost optimization best practices, using a combination of Savings Plans for production and On-Demand Instances for nonproduction environments that are used sparingly results in optimal cost savings​​.

AWS Transfer Family: This service provides fully managed support for file transfers directly into and out of Amazon S3 using the SFTP, FTPS, and FTP protocols.

SFTP Endpoints:

Set up an AWS Transfer Family server and configure SFTP endpoints to handle the file transfers.

This service is scalable and managed, reducing operational overhead compared to running an SFTP solution on EC2 instances.

Integration with Active Directory:

Choose the AWS Directory Service option as the identity provider for the Transfer Family server.

Use AD Connector to link the on-premises Active Directory with AWS, allowing employees to use their existing AD credentials to access the SFTP service.

Operational Efficiency: This solution leverages managed services for both file transfer and identity management, ensuring minimal changes to the current authentication mechanisms and reducing operational overhead.

AWS Outposts brings native AWS services (including Amazon EMR) on-premises, ideal when data residency or latency constraints require local processing.

You benefit from AWS’s managed services while meeting the requirement to keep data processing local.

Using Amazon CloudFront in front of an ALB in a single region is a cost-effective way to deliver content with low latency across the globe. CloudFront caches content closer to the users, reducing the load on backend servers and minimizing data transfer costs by serving cached content from edge locations.

Compared to Global Accelerator, CloudFront is significantly more cost-optimized for static and dynamic content delivery. Multi-region deployments increase infrastructure and transfer costs, which violates the optimization goal. Therefore, option A provides the best mix of performance and cost efficiency.

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

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.

Using API Gateway with API keys provides secure access control. Amazon SQS allows asynchronous decoupling between frontend and backend, ensuring that backend processing can scale independently. AWS Lambda reading from SQS ensures scalable, event-driven processing with minimal operational management. This architecture is resilient and decoupled.

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

Amazon EFS offers an Infrequent Access (IA) storage class, which can be managed via EFS lifecycle policies. Frequently accessed files remain in the Standard storage class, while infrequently accessed files are automatically moved to the IA class, significantly reducing storage costs with minimal effort and no application changes.

Reference Extract:

"EFS lifecycle management automatically transitions files that are not accessed for a set period to the EFS Infrequent Access (IA) storage class, reducing storage costs."

Source: AWS Certified Solutions Architect – Official Study Guide, EFS and Lifecycle Management section.

The best practice for secure access control in AWS is to use IAM roles with least-privilege policies, granting only the permissions necessary to perform required tasks. Assigning roles individually ensures that developers cannot overstep their intended access boundaries.

Sharing credentials or using permanent access keys increases the risk of security breaches. Cognito is primarily intended for managing user access to applications, not AWS infrastructure. Thus, Option B best meets security and access control requirements.

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.

Amazon GuardDuty detects cryptocurrency mining and sends findings to Amazon EventBridge. You can use EventBridge to trigger an automated Lambda function to isolate EC2 instances (such as by removing security group access or stopping/isolating the instance).

AWS Documentation Extract:

"Amazon GuardDuty findings can be sent to Amazon EventBridge, which enables you to trigger an automated response using AWS Lambda."

(Source: AWS GuardDuty documentation)

B, C, D: Security Hub, Inspector, and Config are not directly used for this detection-to-isolation workflow.

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.

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.

AWS App2Container is a command-line tool that helps containerize existing Java and .NET applications running on-premises or on virtual machines, with minimal refactoring. By using Amazon EKS, the company benefits from a managed Kubernetes service, which significantly reduces the operational overhead compared to managing Kubernetes on EC2.

Amazon RDS for SQL Server provides a fully managed SQL Server database engine with automated backups, patching, and high availability through Multi-AZ deployments. This eliminates the need for the company to manage database infrastructure and software manually.

Overall, option A provides the most streamlined and managed approach for both the application and database layers with the least operational effort.

EC2 instances in private subnets cannot access the internet unless there is a NAT gateway or a NAT instance configured.

“To enable instances in a private subnet to connect to the internet or other AWS services, you can use a NAT gateway or NAT instance.”

— NAT Gateways – Amazon VPC

In this use case:

EC2 instances are in private subnets

They need to call external APIs (internet access)

The most operationally efficient and secure method is to place a NAT Gateway in a public subnet and update the route table for private subnets to route internet-bound traffic through it.

Incorrect Options:

A: Private subnets don’t support public IPs.

C: VPC peering doesn’t help reach the public internet.

D: Interface endpoints are for private connectivity to AWS services, not external APIs.

Amazon Aurora Serverless v2 is ideal for applications with unpredictable or intermittent workloads. It automatically scales capacity up or down based on demand, significantly reducing costs. It supports automated backups to Amazon S3, making it suitable and cost-effective for new applications with variable traffic.

The question focuses on minimizing costs while serving static content to users in the US and Europe.

Option Auses a single S3 bucket and configures CloudFront to limit edge locations, reducing costs by using fewer edge locations while still improving performance.

Option Bmaximizes edge locations, which increases costs unnecessarily.

Options C and Dinvolve storing data in multiple regions, which increases storage and operational costs.Thus, Option A is the most cost-effective solution.

IAM Access Analyzer analyzes permissions granted using policies to determine what resources are shared with an external entity, and helps identify excessive permissions or least privilege violations across all accounts in an AWS Organization. It is specifically designed for reviewing and refining IAM permissions with minimal administrative effort.

AWS Documentation Extract:

“IAM Access Analyzer helps you identify the resources in your organization and accounts, such as Amazon S3 buckets or IAM roles, that are shared with an external entity. You can also use Access Analyzer policy checks to refine permissions and implement least privilege.”

(Source: IAM Access Analyzer documentation)

A: Network Access Analyzer is for VPC network access analysis, not IAM permissions.

B: CloudWatch alarms are not suitable for detailed permission analysis.

D: Amazon Inspector is for security vulnerability assessment, not IAM policy review.

Amazon RDS Performance Insights is a “database performance tuning and monitoring feature” that can be enabled with a few clicks and “helps you quickly assess the load on your database” and identify bottlenecks. It surfaces key metrics, including DB load, top SQL, waits, and host metrics such as CPU utilization, which are commonly used indicators for right-sizing (up or down). This provides the lowest operational effort compared to building log pipelines or querying CloudTrail (which does not capture SQL workload characteristics). AWS X-Ray traces application requests, not database internals, and CloudWatch Logs aggregation requires custom ingestion and analysis. Enabling Performance Insights directly on the RDS instance provides actionable utilization data to right-size with minimal setup.

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).

To make the application highly available across regions:

Deploy the application in a different region using a newECS clusterandALBto ensure regional redundancy.

UseRoute 53 failover routingto automatically direct traffic to the healthy region in case of failure.

UseDynamoDB Global Tablesto ensure the database is replicated and available across multiple regions, supporting read and write operations in each region.

Option D (EKS cluster in the same region): This does not provide regional redundancy.

Option E (Global Secondary Indexes): GSIs improve query performance but do not provide multi-region availability.

Option F (PrivateLink): PrivateLink is for secure communication, not for cross-region high availability.

AWS References:

DynamoDB Global Tables

Amazon ECS with ALB

UsingAD ConnectorwithAWS IAM Identity Center(formerly AWS Single Sign-On) allows the company to leverage its existingon-premises Active Directoryfor centralized identity management and access control. AD Connector acts as a proxy to the on-premises AD withoutrequiring additional infrastructure or complex setup. This solution integrates seamlessly with AWS, allowing the development team to use their existing AD credentials to access AWS resources across multiple accounts managed by AWS Organizations. The permissions for AWS resources can be managed centrally through IAM Identity Center by configuring permission sets.

This solution provides:

Least operational overhead: AD Connector is fully managed, and IAM Identity Center allows centralized management of permissions across accounts.

Secure access: The solution complies with security policies by using existing AD authentication mechanisms.

Option A (AWS Managed AD): Setting up a fully managed AWS AD and establishing a trust is more complex and involves additional operational overhead.

Option B (IAM Users): Manually managing IAM users and permissions is less scalable and increases operational complexity.

Option D (Cognito): Amazon Cognito is more suited for user-facing applications rather than internal identity management for AWS resources.

AWS References:

AD Connector with IAM Identity Center

AWS IAM Identity Center

Using Amazon SQS decouples the API from the database, allowing the system to handle write bursts without data loss. The queue buffers incoming requests, and AWS Lambda processes them asynchronously, writing to the RDS instance at a sustainable rate.

From AWS Documentation:

“Amazon SQS acts as a buffer between components that produce and consume data, allowing each to operate independently. You can use AWS Lambda to process messages from SQS and write them to other services such as Amazon RDS.”

(Source: Amazon SQS Developer Guide)

Why C is correct:

SQS absorbs write surges and ensures durable message retention.

Lambda scales automatically with message volume and reduces DB connections.

Prevents timeout errors by smoothing traffic spikes.

Why others are incorrect:

A: Scaling RDS vertically doesn’t address connection spikes.

B: Multi-AZ is for availability, not write throughput.

D: SNS is for fan-out message delivery, not queue-based buffering.

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

AWS Lambda is not suitable for long-running jobs that can take up to 20 minutes, as Lambda has a maximum execution duration of 15 minutes.

Amazon ECS with AWS Fargate allows you to run containers without managing EC2 instances, providing a scalable and highly available environment. You can scale Fargate tasks to handle large and parallel video processing jobs. Amazon Aurora is a highly available, managed relational database. S3 Intelligent-Tiering is cost-effective for storing large video files with variable access patterns.

AWS Documentation Extract:

"AWS Fargate lets you run containers without managing servers or clusters, providing a highly available and scalable compute environment. Fargate is suitable for running data processing workloads that require long-running compute tasks."

(Source: AWS Fargate documentation, Use Cases)

A: Lambda is not suitable for long-running (over 15 min) or heavy compute jobs.

C: Amazon EMR is optimized for big data analytics, not specific video processing. FSx for Lustre and Kinesis are not best fit for this use case.

D: EKS with EC2 adds operational overhead and RDS in a single AZ is not highly available; Glacier Deep Archive is not suitable for frequently accessed video files.

This solution provides encryption at rest and in transit with the least operational overhead while adhering to the company’s security policies.

Encryption at Rest: Amazon RDS for MySQL can be configured to encrypt data at rest by using AWS Key Management Service (KMS) managed keys. This encryption is applied automatically to all data stored on disk, including backups, read replicas, and snapshots. This solution requires minimal operational overhead because AWS manages the encryption and key management process.

Encryption in Transit: AWS Certificate Manager (ACM) allows you to provision, manage, and deploy SSL/TLS certificates seamlessly. These certificates can be used to encrypt data in transit by configuring the MySQL instance to use SSL/TLS for connections. This setup ensures thatdata is encrypted between the application and the database, protecting it from interception during transmission.

Why Not Other Options?:

Option B (IPsec tunnels): While IPsec tunnels encrypt data in transit, they are more complex to manage and require additional configuration and maintenance, leading to higher operational overhead.

Option C (Third-party application-level encryption): Implementing application-level encryption adds complexity, requires code changes, and increases operational overhead.

Option D (VPN for encryption): A VPN solution for encrypting data in transit is unnecessary and adds additional complexity without providing any benefit over SSL/TLS, which is simpler to implement and manage.

AWS References:

Amazon RDS Encryption- Information on how to configure and use encryption for Amazon RDS.

AWS Certificate Manager (ACM)- Details on using ACM to manage SSL/TLS certificates for securing data in transit.

AWS Outposts extends AWS infrastructure and services to on-premises locations. Running Amazon EMR on Outposts allows for processing data that resides locally while benefiting from the managed services of EMR. This enables compliance with data residency requirements and provides scalability and manageability for analytics.

For improving availability and performance of the hybrid application, the following solutions are optimal:

AWS Global Accelerator (Option A): Global Accelerator provides high availability and improves performance by using the AWS global network to route user traffic to the nearest healthy endpoint (across AWS Regions). By adding the Network Load Balancers as endpoints, Global Accelerator ensures that traffic is routed efficiently to the closest endpoint, improving both availability and performance.

Network Load Balancer (Option D): Thestateful TCP-based workloadhosted on Amazon EC2 instances and thestateless UDP-based workloadhosted on-premises are best served by Network Load Balancers (NLBs). NLBs are designed to handle TCP and UDP traffic with ultra-low latency and can route traffic to both EC2 and on-premises endpoints.

Option B (CloudFront and Route 53): CloudFront is better suited for HTTP/HTTPS workloads, not for TCP/UDP-based applications.

Option C (ALB): Application Load Balancers do not support the stateless UDP-based workload, making NLBs the better choice for both TCP and UDP.

AWS References:

AWS Global Accelerator

Network Load Balancer

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.

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.

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.

Explanation (AWS Docs):

DynamoDB Accelerator (DAX) is a fully managed, in-memory cache specifically for DynamoDB. It reduces read load and latency without requiring code changes (only SDK config). This is the least development effort.

“Amazon DynamoDB Accelerator (DAX) is a fully managed, highly available in-memory cache for DynamoDB that delivers microsecond read performance and requires minimal application changes.”

— Amazon DAX

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

Amazon API Gateway supports multiple API types: REST, HTTP, WebSocket, and gRPC. HTTP APIs are a newer, lightweight option that support JWT authorizers natively, enabling secure, scalable authentication for APIs with JSON Web Tokens.

HTTP APIs can integrate with ALBs as a backend target, providing direct connectivity and simplified API management with JWT authentication built in.

REST APIs support JWT but are more feature-rich and complex, often used for legacy or more complex use cases, and have higher costs and latency. WebSocket APIs are for real-time, bidirectional communication, which is not requested here. gRPC APIs support RPC calls but are less common for public HTTP-based APIs with JWT auth.

Therefore, HTTP API with JWT authorizers is the best fit for this use case.

To automatically identify sensitive data in Amazon S3 and monitor access patterns for suspicious activities:

Amazon Macie uses machine learning and pattern matching to discover and protect sensitive data in S3. It provides visibility into data security risks and enables automated protection against those risks.

Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior to protect AWS accounts and workloads. It analyzes events from AWS CloudTrail, VPC Flow Logs, and DNS logs.

By enabling both services, the company can automate the discovery of sensitive data and continuously monitor access patterns for potential security threats.

AWS Backup is the most appropriate solution for managing backups with minimal operational overhead while meeting the regulatory requirement to retain data for 90 days and enabling point-in-time restore for up to 14 days.

AWS Backup: AWS Backup provides a centralized backup management solution that supports automated backup scheduling, retention management, and compliance reporting across AWS services, including Amazon RDS. By creating a backup plan, you can define a retention period (in this case, 90 days) and automate the backup process.

Point-in-Time Restore (PITR): Amazon RDS supports point-in-time restore for up to 35 days with automated backups. By using AWS Backup in conjunction with RDS, you ensure that your backup strategy meets the requirement for restoring data to a specific point in time within the last 14 days.

Why Not Other Options?:

Option A (RDS Automated Backups): While RDS automated backups support PITR, they do not directly support retention beyond 35 days without manual intervention.

Option B (Manual Snapshots): Manually creating and managing snapshots is operationally intensive and less automated compared to AWS Backup.

Option C (Aurora Clones): Aurora Clone is a feature specific to Amazon Aurora and is not applicable to Amazon RDS for Oracle.

AWS References:

AWS Backup- Overview of AWS Backup and its capabilities.

Amazon RDS Automated Backups- Information on how RDS automated backups work and their limitations.

When using an SQS queue as an event source for AWS Lambda, the visibility timeout of the SQS queue plays a critical role in preventing duplicate message processing.

"If your Lambda function doesn't process the message and delete it from the queue within the visibility timeout period, the message becomes visible again and can be processed again by the same or another function instance."

— AWS Lambda with SQS

In this scenario, the third-party API may take up to 60 seconds to respond. Since the Lambda function is configured with a 65-second timeout, the visibility timeout of the queue must be greater than or equal to the maximum function execution time to avoid the same message being reprocessed.

Incorrect Options:

A: Memory allocation doesn’t impact duplicate message handling.

B: Timeout is already sufficient; increasing it further does not solve the core issue.

C: Delivery delay controls initial delivery delay, not reprocessing logic.

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.

The Amazon CloudWatch agent is required to collect memory utilization metrics (as memory metrics are not reported by default). A target tracking policy is the simplest and most effective way to scale based on a custom metric such as mem_used_percent.

Reference Extract:

"Install the CloudWatch agent to collect memory metrics, and create a target tracking scaling policy using these custom metrics."

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

AWS Secrets Manager is the recommended service for managing and automatically rotating database credentials. It integrates natively with Amazon RDS (including PostgreSQL), supports built-in rotation functionality, and requires minimal setup.

Secrets Manager also supports versioning and auditing, which enhances operational excellence and security. Parameter Store does not natively support credential rotation. AWS KMS manages key encryption—not application secrets—so it is not applicable here.

TheAWS_PROXY integration with Amazon API Gatewayallows the API to invoke a Lambda function synchronously, making it a suitable solution for the custom authorization mechanism and text translation use case.

Synchronous Invocation: The API Gateway REST API with AWS_PROXY integration enables synchronous processing of HTTP requests and responses, which is required for document translation.

Custom Authorization: API Gateway supports custom authorizers for fine-grained access control.

Lambda Function Execution: Although Lambda's execution time limit is 15 minutes, this is sufficient for the 6-minute document translation requirement.

Why Other Options Are Not Ideal:

Option B:

Introducing a Lambda function URL to invoke another Lambda function unnecessarily complicates the architecture.Not efficient.

Option C:

Asynchronous invocation cannot guarantee real-time response delivery for document translation tasks.Not suitable.

Option D:

Hosting the API on an EC2 instance increases operational overhead. HTTP PROXY integration does not add significant benefits here.Not cost-effective or efficient.

AWS References:

API Gateway Lambda Proxy Integration:AWS Documentation - Proxy Integration

Custom Authorization in API Gateway:AWS Documentation - Custom Authorization

To provide unique, secure URLs efficiently:

A: Create a wildcard custom domain in Route 53 as an alias for the API Gateway endpoint.

D: Request a wildcard certificate in ACM in the same Region as API Gateway (certificates must be in the same Region as the API).

F: Create a custom domain name in API Gateway and attach the certificate.

“You can configure a custom domain name for your API Gateway API. To use a wildcard certificate, request it from ACM in the same Region as your API.”

— API Gateway Custom Domain Names

This combination provides secure wildcard URLs without creating separate endpoints or hosted zones per user.

Amazon ECS with AWS Fargate provides serverless container compute that can scale tasks dynamically in response to demand. This matches the unpredictable scaling requirements of a gaming workload. Aurora Serverless v2 provides on-demand, autoscaling relational database capacity while supporting complex SQL queries. EC2 with Auto Scaling (A) works but requires significant management overhead. Lambda with DynamoDB (B) is not suitable because the workload requires a relational database and long-running processes. ParallelCluster with HPC (D) is designed for batch scientific workloads, not dynamic, interactive gaming. Option C provides the correct balance of elasticity, performance, and managed services for both compute and relational database needs.

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 Storage Gateway file gateway presents a file interface backed by Amazon S3 and supports NFS. This allows local applications to access data via NFS while also enabling cloud applications to use the data stored in S3 for analytics and processing, fulfilling both hybrid and cloud-native requirements.

Reference Extract:

"AWS Storage Gateway file gateway offers NFS and SMB access to data stored in Amazon S3, supporting hybrid workloads for local and cloud access."

Source: AWS Certified Solutions Architect – Official Study Guide, Hybrid and Storage Gateway section.

A spread placement group is designed to deploy each instance on distinct underlying hardware, reducing the risk of simultaneous failures. By spreading instances across multiple Availability Zones, you achieve high availability and fault tolerance, meeting the 99.99% uptime requirement. Spread placement groups are ideal for critical applications that require maximum resilience to single hardware failures. Neither cluster nor partition strategies offer the same guarantee of separation combined with cross-AZ distribution.

Reference Extract from AWS Documentation / Study Guide:

"Spread placement groups are recommended for applications that have a small number of critical instances that should be kept separate from each other. Instances in a spread placement group are placed on distinct underlying hardware, and when deployed across multiple Availability Zones, provide high availability."

Source: AWS Certified Solutions Architect – Official Study Guide, Compute and High Availability section; Amazon EC2 Placement Groups Documentation.

Amazon RDS for MySQL supports read replicas that offload read-intensive workloads such as reporting, leaving the primary instance free for write operations.

“You can use Amazon RDS read replicas to elastically scale out beyond the capacity constraints of a single DB instance for read-heavy database workloads.”

— Working with Read Replicas

This is the recommended approach for minimizing performance impact on the primary DB instance.