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

Memory Optimized Instances: These instances are designed to deliver fast performance for workloads that process large data sets in memory. They are ideal for high-performance databases like SAP and applications with high memory utilization.

High Memory Utilization: Both the SAP application and the SQL Server database have high memory demands as per the on-premises performance data. Memory optimized instances provide the necessary memory capacity and performance.

Instance Types:

For the SAP application, using a memory optimized instance ensures the application has sufficient memory to handle the high workload efficiently.

For the SQL Server database, memory optimized instances ensure optimal database performance with high memory throughput.

Operational Efficiency: Using the same instance family for both the application and the database simplifies management and ensures both components meet performance requirements.

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.

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

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

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

AWS Documentation Extract:

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

(Source: AWS Macie and S3 Object Lock documentation)

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

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

ECS with Fargate: Allows containerized workloads to scale rapidly without managing underlying servers, handling unpredictable growth effectively.

RDS for Relational Data: Manages large relational datasets efficiently while supporting high availability.

SQS for Decoupling: Ensures message processing occurs in a specific order, decoupling application components and allowing independent evolution.

AWS ECS with Fargate Documentation,AWS SQS Documentation

Application Load Balancer (ALB): ALB is suitable for routing HTTP/HTTPS traffic to the application servers. It provides advanced routing features and integrates well with Auto Scaling groups.

Target Group Configuration:

Create a target group for the application servers and register the Auto Scaling group with this target group.

Configure the ALB to forward requests from the web servers to the application servers.

Security Group Setup:

Configure the security group of the application servers to only allow traffic from the web servers' security group.

This ensures that only the web servers can access the application servers, meeting the requirement to limit access.

Benefits:

Security: Using security groups to restrict access ensures a secure environment where only intended traffic is allowed.

Scalability: ALB works seamlessly with Auto Scaling groups, ensuring the application can handle varying loads efficiently.

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.

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

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

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

Why Not Other Options?:

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

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

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

AWS References:

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

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

Amazon DocumentDB global clusters support managed cross-region failover, allowing you to promote a secondary region to become the new primary with minimal downtime. This ensures business continuity during maintenance or regional disruptions.

Reference Extract:

"Amazon DocumentDB global clusters support managed cross-Region failover, allowing you to recover quickly from regional disruptions with minimal downtime."

Source: AWS Certified Solutions Architect – Official Study Guide, DocumentDB and Resiliency section.

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

Key AWS features:

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

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

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

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.

This solution usesCloudFrontto serve the website securely over HTTPS usingAWS Certificate Manager (ACM)for SSL certificates.Origin Access Control (OAC)ensures that only CloudFront can access the S3 bucket directly.AWS WAFwith an IP set rule restricts access to the website, allowing only the on-premises IP address.Route 53is used to create an alias record pointing to the CloudFront distribution. This setup ensures secure, private access to the website with low administrative overhead.

Option A and B: S3 bucket policies and access points do not provide HTTPS support, nor do they offer the same level of security as CloudFront with WAF.

Option D: Signed URLs are more suitable for temporary, expiring access rather than a permanent solution for on-premises employees.

AWS References:

Amazon CloudFront with Origin Access Control

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.

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

Key aspects:

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

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

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

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

AWS Fargate with Amazon ECS is a serverless, fully managed compute engine for containers. Fargate eliminates the need to provision or manage servers, and is ideal for periodic, long-running batch jobs. You only pay for the resources consumed during job execution, making it cost-effective for jobs that run infrequently. Lambda is not suitable because the maximum execution time is 15 minutes, but the job can take up to 2 hours.

AWS Documentation Extract:

“AWS Fargate lets you run containers without managing servers or clusters. Fargate is ideal for batch jobs, event-driven processing, and scheduled tasks that require hours of compute.”

(Source: AWS Fargate documentation)

A: Reserved Instances are cost-inefficient for infrequent workloads and require server management.

B: Lambda has a hard limit of 15 minutes per execution, insufficient for this job.

D: ECS on EC2 requires you to manage and provision EC2 instances, increasing cost and management overhead.

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

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

DynamoDB Streams: Captures real-time changes in DynamoDB tables and allows integration with Lambda for processing the changes.

Minimal Operational Overhead: Using a Lambda function directly to write data to S3 ensures simplicity and reduces the complexity of the pipeline.

Amazon DynamoDB Streams Documentation

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.

Comprehensive and Detailed Explanation:

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

This architecture uses ALB for routing, Auto Scaling for elasticity, SQS to buffer unprocessed orders and decouple services, and RDS Multi-AZ for high availability and durability of transactional data. This ensures resilience and fault tolerance even during traffic spikes.

Amazon EBS io2 Block Express volumes are designed to deliver sub-millisecond latency and up to 256,000 IOPS per volume, with durability and high availability. This makes io2 Block Express the recommended choice for workloads requiring very high and predictable IOPS, such as enterprise databases and high-transaction-rate applications.

Reference Extract:

"EBS io2 Block Express volumes deliver up to 256,000 IOPS and sub-millisecond latency, supporting high-performance, high-durability workloads."

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

Amazon S3 is designed for durability, scalability, and millisecond access. For small monthly data volumes (300 MB), S3 Standard is cost-effective and provides immediate access. To meet 30-day retention, Lifecycle policies can automatically expire objects after the required time. For disaster recovery, S3 Cross-Region Replication (CRR) copies objects across Regions to a backup bucket, ensuring data resiliency. OpenSearch (A) is not needed because the requirement is storage and retrieval, not indexing. Aurora or RDS options (C, D) add unnecessary complexity and cost, as a relational database is not required for JSON storage and millisecond retrieval. Therefore, option B provides the simplest, most resilient, and cost-optimized solution.

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

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

Detailed Explanation:

A. IAM identity provider:Does not support centralized management across multiple accounts.

B. AWS Managed AD:Unnecessary if an on-premises Active Directory already exists.

C. IAM Identity Center + Existing AD:Best approach to integrate existing Active Directory for SSO, with MFA and centralized permissions.

D. Lambda for synchronization:Adds complexity and does not leverage IAM Identity Center capabilities.

Option Ais the best solution as it leveragesAWS Lambdafor serverless, scalable, and highly available processing and enrichment of clickstream data. Lambda can process the data in real-time, join it with the Aurora database data, and write the enriched results to Amazon S3. FromS3,Amazon Redshift Spectrumcan directly query the enriched data without needing to load the data into Redshift, enabling cost efficiency and high availability.

Why Other Options Are Incorrect:

Option B:EC2 Spot Instances are not guaranteed to be highly available, as Spot Instances can be interrupted at any time. This does not align with the requirement for high availability.

Option C:While ECS with AWS Fargate provides scalability, using EC2 for the COPY command introduces operational overhead and compromises high availability.

Option D:Kinesis Data Firehose and Athena are suitable for querying raw data, but they do not directly support enriching the data by joining with Aurora. This solution fails to meet the requirement for data enrichment.

Key AWS Features Used:

AWS Lambda:Real-time serverless processing with integration capabilities for Aurora and S3.

Amazon S3:Cost-effective storage for enriched data.

Amazon Redshift Spectrum:Direct querying of data stored in S3 without loading it into Redshift.

AWS Documentation References:

AWS Lambda Function Overview

Amazon Redshift Spectrum

Processing Streaming Data with Kinesis Data Streams

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

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

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

Why Not Other Options?:

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

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

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

AWS References:

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

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

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

AWS Documentation Extract:

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

(Source: AWS Backup documentation)

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

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

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.

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.

Edge-optimized API endpointsroute requests through CloudFront, reducing latency for global users.

Option Acorrectly implements edge-optimization, caching, and compression to minimize latency.

Options B and Ddo not use edge optimization, leading to higher latency for global users.

Reserved concurrency inOptions C and Dimproves backend scaling but does not address global latency directly.

Amazon S3 Glacier Flexible Retrieval is a low-cost archival storage class that supports retrieval of data within minutes (expedited access ~1–5 minutes), making it ideal for audit scenarios where occasional, quick access to archived data is required. In contrast, Glacier Deep Archive takes hours to retrieve.

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

Reference Extract:

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

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

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

Amazon RDS Blue/Green Deployments provide a safe and straightforward way to make database changes with minimal downtime and risk. Blue/Green deployments create an exact copy ("green") of your production environment ("blue") where you can make schema changes and run tests. After validation, you can promote the green environment to production with a single click or API call, achieving near-zero downtime and maximum availability. This is the AWS-recommended method for deploying major database changes in a way that minimizes impact to users and maximizes uptime.

Reference Extract from AWS Documentation / Study Guide:

"Amazon RDS Blue/Green Deployments enable you to make changes to your database environment safely. You can perform schema updates and feature testing in a fully managed staging environment and switch over with minimal downtime, ensuring the highest availability."

Source: AWS Certified Solutions Architect – Official Study Guide, Database and Migration section; Amazon RDS Blue/Green Deployments Documentation.

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.

Comprehensive and Detailed Explanation:

To track costs associated with different teams within a single AWS account, the company should implement user-defined cost allocation tags.

Tagging Resources with CostCenter: Assign the CostCenter tag to all resources, specifying the appropriate value for each team. This practice enables the organization to categorize and track AWS costs effectively.

Activating the CostCenter Tag: After tagging resources, activate the CostCenter tag in the AWS Billing and Cost Management console. Activation is necessary for the tags to appear in cost allocation reports and AWS Cost Explorer.

By following these steps, the company can generate detailed billing reports that break down costs by team, facilitating better cost management and accountability.

Amazon CloudFront is a highly cost-effective CDN that caches content like images and videos at edge locations globally. This reduces latency and the load on the origin S3 bucket. It is ideal for static content that is accessed by many users.

Why Option C is Correct:

NAT Gateway: Allows private subnets to access the internet for outbound requests while preventing inbound connections.

High Availability: Deploying NAT gateways in both AZs ensures fault tolerance.

Shared Route Table: Simplifies routing configuration for private subnets.

Why Other Options Are Not Ideal:

Option A: Creating separate route tables for each subnet adds unnecessary complexity.

Option B: Internet gateways allow inbound access, violating the requirement to block public IPv4 access.

Option D: Egress-only internet gateways are designed for IPv6, not IPv4.

AWS References:

Amazon VPC NAT Gateway:AWS Documentation - NAT Gateway

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

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

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

AWS Global Accelerator reduces latency by directing users to the optimal Regional endpoint based on global network health and proximity. Amazon CloudFront caches static and dynamic content at edge locations for ultra-low latency access worldwide, improving performance and reducing server load.

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.

The company needs a cloud-based storage solution for frequently accessed data with low latency, while retaining their current on-premises infrastructure for some data storage. AWS Storage Gateway'sVolume Gateway with cached volumesis the most appropriate solution for this scenario.

AWS Storage Gateway - Volume Gateway (Cached Volumes):

Volume Gateway with cached volumesallows you to store frequently accessed data in the AWS Cloud while keeping the most recently accessed data cached locally on-premises. This ensures low-latency access to active data while providing scalability for the rest of the data in the cloud.

The cached volume option stores the primary data in Amazon S3 but caches frequently accessed data locally, ensuring fast access. This configuration is well-suited for applications that require fast access to frequently used data but can tolerate cloud-based storage for the rest.

Since the company is facing limited on-premises storage, cached volumes provide an ideal solution, as they reduce the need for additional on-premises storage infrastructure.

Why Not the Other Options?:

Option A (S3 File Gateway): S3 File Gateway provides a file-based interface (SMB/NFS) for storing data directly in S3. While it is great for file storage, the company’s need for block-level storage with iSCSI targets makes Volume Gateway a better fit.

Option C (Volume Gateway - Stored Volumes): Stored volumes keep all the data on-premises and asynchronously back up to AWS. This would not address the company's storage limitations since they would still need substantial on-premises storage.

Option D (Tape Gateway): Tape Gateway is designed for archiving and backup, not for frequently accessed low-latency data.

AWS References:

AWS Storage Gateway - Volume Gateway

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

Web ACL (Access Control List):

Create a web ACL and associate it with the ALB.

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

Security and Flexibility:

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

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

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

When designing a microservice architecture where each microservice interacts with different AWS services, it's essential to follow the principle of least privilege. This means granting each microservice only the permissions it needs to perform its tasks, reducing the risk of unauthorized access or accidental actions.

The recommended approach is to create individualIAM roleswith policies that grant each microservice the specific permissions it requires. Then, these roles should be associated with the EC2 instances that run the corresponding microservice. By doing so, each EC2 instance will assume its specific IAM role, and permissions will be automatically managed by AWS.

IAM roles provide temporary credentials via the instance metadata service, eliminating the need to hard-code credentials in your application code, which enhances security.

AWS References:

IAM Roles for Amazon EC2explains how EC2 instances can use IAM roles to securely access AWS services without managing long-term credentials.

Best Practices for IAMincludes recommendations for implementing the least privilege principle and using IAM roles effectively.

Why the other options are incorrect:

A. Assign an IAM user to each microservice: This requires managing long-term credentials (access keys), which should be avoided. Storing keys in application code is insecure and creates a maintenance burden.

B. Create a single IAM role: This violates the principle of least privilege, as a single role with broad permissions across all services is less secure.

C. Use AWS Organizations: This approach adds unnecessary complexity. Managing permissions at the account level for each microservice is excessive for this use case and doesn't adhere to the principle of least privilege.

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

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

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

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

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

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

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

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

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

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

AWS References:

Amazon EMR Cluster Management

Using Spot Instances in EMR

Amazon RDS Multi-AZ deployment provides automated failover and increased availability for MySQL databases. In case of infrastructure failure or power outage in one Availability Zone, RDS automatically fails over to a standby replica in another AZ, minimizing downtime. This is the AWS-recommended way to achieve high availability for relational databases.

AWS Documentation Extract:

"Amazon RDS Multi-AZ deployments provide high availability, failover support, and data redundancy for database instances. In the event of infrastructure failure, Amazon RDS performs an automatic failover to the standby."

(Source: Amazon RDS documentation, High Availability)

A: An ALB does not increase availability for a single database instance.

B: EC2 instance recovery does not move the instance across Availability Zones.

D: Termination protection prevents accidental deletion, not availability issues.

Amazon Aurora Serverless v2 provides cost-effective, on-demand, and auto-scaling database capacity. You pay only for actual usage, making it ideal for development and test environments that are not used continuously. It supports PostgreSQL and is suitable for variable and short-duration workloads, minimizing costs when databases are idle.

AWS Documentation Extract:

“Amazon Aurora Serverless v2 automatically adjusts database capacity based on application needs, ideal for development and test environments with intermittent usage.”

(Source: Aurora Serverless v2 documentation)

B: RDS instances run and accrue charges even when idle.

C, D: Aurora clusters/global databases are overkill and incur higher costs for dev/test.

AWS WAF allows web applications to protect themselves from common web exploits and vulnerabilities. Using AWS managed rule groups ensures protection against known attack patterns, such as SQL injection and cross-site scripting. Associating AWS WAF with the ALB provides application-layer security and real-time threat mitigation.

Comprehensive and Detailed Explanation:

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

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

Amazon S3 Bucket Keys reduce the cost of AWS KMS API requests by generating a data key at the bucket level instead of individually calling KMS for every object read or written. This approach is particularly effective when workloads, such as ML pipelines, involve reading large numbers of encrypted objects. Switching to AWS managed keys (A) does not reduce the frequency of API calls. Removing encryption (B) would violate compliance/security requirements. Using CloudHSM (C) adds cost and operational burden. Therefore, the correct solution is D — enabling S3 Bucket Keys with SSE-KMS, which significantly reduces decryption costs while maintaining secure encryption.

Amazon OpenSearch Service is the AWS-native service for real-time search, analytics, and visualization. Ingesting streaming data with Amazon Kinesis Data Streams ensures scalable and reliable data ingestion. OpenSearch supports indexing and querying the data in near real time. QuickSight can then be integrated for visualization and reporting. Options A and B involve batch processing and are not optimized for real-time search. Option C (EKS + DynamoDB) is not a fit for search workloads, as DynamoDB does not support advanced text search. Option D provides the closest AWS-native equivalent to the company’s existing search-and-visualization architecture.

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

AWS Systems Manager Session Manager allows secure, auditable SSH-like access to EC2 instances without the need to open SSH ports or manage bastion hosts. For this to work in a private subnet, an interface VPC endpoint is required (not a gateway endpoint).

The EC2 instances must have the AmazonSSMManagedInstanceCore policy attached to their IAM roles to allow Systems Manager operations.

With Session Manager, all session activity can be logged centrally to Amazon CloudWatch Logs or S3, satisfying the audit requirement and improving operational efficiency over manual SSH and bastion configurations.

AWS Config allows for creation of custom rules to monitor EBS configurations. Combined with CloudWatch metrics and Amazon SNS, custom rules can track over-provisioned IOPS and send alerts when thresholds are breached, allowing proactive cost and performance management.

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

For a large-scale multi-account AWS environment with many VPCs and centralized Direct Connect, AWS recommends using a Transit Gateway (TGW) architecture combined with a Direct Connect gateway (DXGW). This setup allows scalable, centralized connectivity between on-premises and multiple VPCs across accounts.

Step B: Creating a Direct Connect gateway and Transit Gateway in a central network account and connecting them via a transit VIF enables the on-premises network to access all connected VPCs.

Step D: Sharing the transit gateway with other accounts via AWS Resource Access Manager (RAM) allows the central TGW to attach VPCs in multiple accounts, simplifying multi-account connectivity.

Step F: To route cloud resources’ internet traffic back through the on-premises data center (for centralized egress), provisioning only private subnets and routing outbound internet traffic through NAT or firewall services in the data center is necessary. This requires configuring transit gateway and customer gateway routes appropriately.

Option A is partially correct in the use of Direct Connect gateway but association proposals are not scalable for hundreds of VPCs and accounts compared to transit gateway. Option C (internet gateway) is irrelevant here as traffic egress is required via on-premises data center, not directly to the internet. Option E (VPC peering) is not scalable for hundreds of VPCs.

Amazon QuickSight includes built-in ML-powered forecasting capabilities, allowing you to create, visualize, and interact with time series forecasts directly within the BI dashboard, with no ML experience or infrastructure management required. This is the lowest management overhead solution.

AWS Documentation Extract:

"Amazon QuickSight provides built-in ML-powered forecasting, allowing business users to forecast future trends with a few clicks and no machine learning expertise required."

(Source: Amazon QuickSight documentation)

A, C, D: Require additional setup, management, or ML knowledge.

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.

Amazon RDS Proxy: RDS Proxy is a fully managed, highly available database proxy that makes applications more resilient to database failures by pooling and sharing connections, and it can automatically handle database failovers.

Reduced Failover Time: By using RDS Proxy, the connection management between the application and the database is improved, reducing failover times significantly. RDS Proxy maintains connections in a connection pool and reduces the time required to re-establish connections during a failover.

Configuration:

Create an RDS Proxy instance.

Configure the proxy to connect to the RDS for PostgreSQL DB instance.

Modify the application configuration to use the RDS Proxy endpoint instead of the direct database endpoint.

Operational Benefits: This solution provides high availability and reduces application timeouts during failovers with minimal changes to the application code.

Amazon SQS with a 2-day retention ensures the data lives just as long as needed. EventBridge Pipes allow direct integration between event producers and consumers, with optional filtering and transformation. AWS Step Functions supports manual approval steps, which fits the workflow requirement perfectly.

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

Background Jobs with Event Invocation Type:

The Event invocation type is asynchronous, meaning the Lambda function does not send an immediate result to the API Gateway and processes the request in the background. This is ideal for video encoding tasks that take time.

REST API vs. HTTP API:

REST APIs support advanced features like API keys, request validation, and throttling that HTTP APIs do not support fully.

Since the developer needs API keys and request validations, a REST API is the correct choice.

Integration with Lambda:

AWS Lambda integration is seamless with REST APIs, and using the Event invocation ensures asynchronous processing.

Incorrect Options Analysis:

Option A: HTTP API lacks full support for API keys and validation.

Option CandD: RequestResponse invocation type requires immediate responses, unsuitable for background jobs.

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

An S3 Bucket Key reduces the cost of using AWS KMS for server-side encryption by decreasing the number of requests made to KMS. By enabling S3 Bucket Key, the company can meet its encryption requirements with KMS keys while optimizing costs by reducing the number of KMS API requests.

Key AWS features:

Cost Optimization: S3 Bucket Keys reduce the frequency of KMS calls, optimizing the cost associated with encryption while still using AWS KMS for key management.

Compliance with KMS Encryption: This solution continues to meet the strict encryption requirements of the company by using KMS managed keys.

AWS Documentation: Using an S3 Bucket Key is recommended for organizations looking to optimize encryption costs without compromising security​​.

AWS Global Accelerator: This service provides a global fixed entry point to your applications and optimizes the path to your application through the AWS global network, reducing latency and improving performance.

Accelerated TCP Connections:

Global Accelerator uses the AWS global network to route traffic to the nearest edge location, improving the performance and reliability of your live streams.

It provides static IP addresses that act as a fixed entry point to your application, simplifying DNS management.

High-Quality Streams:

By leveraging Global Accelerator, reporters can send live streams with the highest quality and low latency.

This service automatically reroutes traffic to the nearest available AWS Region, ensuring consistent performance even during traffic spikes or failures.

Operational Efficiency: Using Global Accelerator simplifies the network setup and provides an optimized path for live streams without the need for complex configurations, making it an efficient solution for real-time streaming applications.

SSE-KMS: Server-side encryption with AWS Key Management Service (SSE-KMS) provides robust encryption of data at rest, integrated with AWS KMS for key management and auditing.

Automatic Key Rotation: By enabling automatic rotation for the KMS keys, the system ensures that keys are rotated annually without manual intervention, meeting compliance requirements.

Logging and Auditing: AWS KMS automatically logs all key usage and management actions in AWS CloudTrail, providing the necessary audit logs.

Implementation:

Create a KMS key with automatic rotation enabled.

Configure the S3 bucket to use SSE-KMS with the created KMS key.

Ensure CloudTrail is enabled for logging KMS key usage.

Operational Efficiency: This solution provides encryption, automatic key management, and auditing in a seamless, fully managed way, reducing operational overhead.

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.

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.

AWSSecrets Manageris the best solution for securely storing and managing sensitive information, such as usernames and passwords. Secrets Manager provides automatic rotation, fine-grained access control, and encryption of credentials. It is designed to integrate easily with other AWS services, such as CloudFormation, to automate the retrieval of secrets via theBatchGetSecretValue API.

Secrets Manager has a lower operational overhead than manually managing credentials, and it offers features like automatic secret rotation that reduce the need for human intervention.

Option A and C (Parameter Store): While Systems Manager Parameter Store can store secrets, Secrets Manager provides more specialized capabilities for securely managing and rotating credentials with less operational overhead.

Option B and C (AWS Batch): Introducing AWS Batch unnecessarily complicates the solution. Secrets Manager already provides simple API calls for retrieving secrets without needing an additional service.

AWS References:

AWS Secrets Manager

Secrets Manager with CloudFormation

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.

To meet the requirements for tagging and preventing instance creation or deletion without proper tags, the company can use a combination of AWS Organizations tag policies and service control policies (SCPs).

Tag Policies: These enforce specific tag values across resources. Creating a tag policy with required values (e.g., sensitive, non-sensitive) and attaching it to the appropriate organizational unit (OU) ensures consistency in tagging.

SCPs: SCPs can be used to enforce compliance by preventing instance creation without a tag and preventing tag deletion. These policies control actions at the account level across the organization.

Key AWS features:

Tag Policieshelp standardize tags across accounts, andSCPsenforce governance by restricting actions that violate the policies.

AWS Documentation: AWS best practices recommend using tag policies and SCPs to enforce compliance across multiple accounts within AWS Organizations​.

Amazon Aurora PostgreSQL with Babelfish allows SQL Server applications to run directly on Aurora PostgreSQL with minimal code changes. Babelfish adds a SQL Server-compatible endpoint, significantly lowering costs compared to running licensed SQL Server instances on RDS or EC2.

AWS Documentation Extract:

“Babelfish for Aurora PostgreSQL enables Aurora to understand T-SQL and SQL Server wire protocol, allowing you to run SQL Server applications on Amazon Aurora PostgreSQL with lower costs.”

(Source: Babelfish for Aurora PostgreSQL documentation)

A, D: SQL Server on EC2 or RDS incurs high Microsoft licensing costs.

C: Plain PostgreSQL would require more code refactoring than Babelfish.

Dead-letter queues (DLQs) with Amazon SQS allow Lambda functions to offload failed events for later inspection or retry. Using retry logic with exponential backoff ensures resilience and compliance with best practices for fault-tolerant serverless architectures. This guarantees no data is lost due to transient errors.

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 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.

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

Here is why FSx for Lustre is the best fit:

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

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

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

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

In contrast:

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

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

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

AWS References:

Amazon FSx for Lustre

Best Practices for High Performance Computing (HPC)

Amazon FSx and Amazon S3 Integration

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

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

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

Implementation:

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

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

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

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

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

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

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

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

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

AWS Organizations: AWS Organizations allows you to create multiple AWS accounts and manage them centrally. You can organize accounts into organizational units (OUs) and apply policies to these units.

Organizational Units (OUs):

Create separate OUs for each department: finance, data analytics, and development.

Place the respective AWS accounts for each department into their corresponding OUs.

Service Control Policies (SCPs):

SCPs are policies that can restrict which AWS services and actions are available to accounts in an OU.

Create SCPs to define which services each department can use and attach these policies to the appropriate OUs.

SCPs apply to all IAM users, groups, and roles within the accounts in the OU, providing centralized control over service usage.

Operational Efficiency: Using AWS Organizations and SCPs provides a scalable and centralized way to manage permissions across multiple accounts with minimal operational overhead.

Incremental Exports: Exporting DynamoDB data directly to Amazon S3 provides an automated, serverless way to make data available for analytics without operational overhead.

Analytics-Friendly Storage: Amazon S3 supports long-term analytics workloads and can integrate with tools like Athena or QuickSight.

DynamoDB Export to S3 Documentation

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.

AWS Control Tower: Provides a managed service to set up and govern a secure, multi-account AWS environment based on AWS best practices. It automates the setup of AWS Organizations and applies security controls (guardrails).

Networking Account:

Create a centralized networking account that includes a VPC with both private and public subnets.

This centralized VPC will manage and control the networking resources.

AWS Resource Access Manager (AWS RAM):

Use AWS RAM to share the subnets from the networking account with the other workload accounts.

This allows different workload accounts to utilize the shared networking resources without the need to manage their own VPCs.

Operational Efficiency: Using AWS Control Tower simplifies the setup and governance of multiple AWS accounts, while AWS RAM facilitates centralized management of networking resources, reducing operational overhead and ensuring consistent security and compliance.

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

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

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

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

Amazon CloudFront is a global content delivery network (CDN) that caches and distributes content to users with low latency. By setting the existing ALB as the origin, CloudFront can cache dynamic and static content closer to users worldwide, improving performance and reducing the load on the application servers. This is the most cost-optimized and AWS-recommended way to globally serve dynamic content for web applications.

Reference Extract:

"CloudFront accelerates delivery of both static and dynamic content, reducing latency and offloading origin resources by caching content at edge locations."

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

Amazon Rekognition provides pretrained models that can detect inappropriate or unsafe content (such as nudity or violence) without requiring users to build their own ML models. Using a Lambda function to call Rekognition when new photos are uploaded is a serverless and scalable solution.

AWS Secrets Manager is designed specifically to securely store and manage sensitive information such as database credentials. It integrates seamlessly with AWS services like Lambda and RDS, and it provides automatic credential rotation with minimal operational overhead.

AWS Secrets Manager: By storing the database credentials in Secrets Manager, you ensure that the credentials are securely stored, encrypted, and managed. Secrets Manager provides a built-in mechanism to automatically rotate credentials at regular intervals (e.g., every 30 days), which helps in maintaining security best practices without requiring additional manual intervention.

Lambda Integration: The Lambda function can be easily configured to retrieve the credentials from Secrets Manager using the AWS SDK, ensuring that the credentials are accessed securely at runtime.

Why Not Other Options?:

Option A (Parameter Store with Rotation): While Parameter Store can store parameters securely, Secrets Manager is more tailored for secrets management and automatic rotation, offering more features and less operational overhead.

Option C (Encrypted Lambda environment variable): Storing credentials directly in Lambda environment variables, even when encrypted, requires custom code to manage rotation, which increases operational complexity.

Option D (KMS with automatic rotation): KMS is for managing encryption keys, not for storing and rotating secrets like database credentials. This option would require more custom implementation to manage credentials securely.

AWS References:

AWS Secrets Manager- Detailed documentation on how to store, manage, and rotate secrets using AWS Secrets Manager.

Using Secrets Manager with AWS Lambda- Guidance on integrating Secrets Manager with Lambda for secure credential management.

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

AWS Documentation Extract:

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

(Source: Route 53 Resolver documentation)

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

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

This solution is the most cost-effective and efficient way to break down costs per application.

Tagging Resources: By tagging all AWS resources with a specific key (e.g., "cost") and a value representing the application's name, you can easily identify and categorize costs associated with each application. This tagging strategy allows for granular tracking of costs within AWS.

Activating Cost Allocation Tags: Once tags are applied to resources, you need to activate cost allocation tags in the AWS Billing and Cost Management console. This ensures that the costs associated with each tag are included in your billing reports and can be used for cost analysis.

AWS Cost Explorer: Cost Explorer is a powerful tool that allows you to visualize, understand, and manage your AWS costs and usage over time. You can filter and group your cost data by the tags you’ve applied to resources, enabling you to easily see the cost breakdown for each application. Cost Explorer also supports generating regular reports, which can be scheduled and emailed to stakeholders.

Why Not Other Options?:

Option A (AWS Budgets): AWS Budgets is more focused on setting cost and usage thresholds and monitoring them, rather than providing detailed cost breakdowns by application.

Option B (Load Cost and Usage Reports into RDS): This approach is less cost-effective and involves more operational overhead, as it requires setting up and maintaining an RDS instance and running SQL queries.

Option D (AWS Billing and Cost Management Console): While you can download bills, this method is more manual and less dynamic compared to using Cost Explorer with activated tags.

AWS References:

AWS Tagging Strategies- Overview of how to use tagging to organize and track AWS resources.

AWS Cost Explorer- Details on how to use Cost Explorer to analyze costs.

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.

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

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

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

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

AWS References:

Amazon Comprehend

Amazon Textract

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.

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

Amazon DynamoDB provides single-digit millisecond performance at any scale and is fully managed to handle millions of catalog records. It is ideal for structured catalog data such as product metadata and scales seamlessly during high-traffic events like sales. Amazon S3 is optimized for storing unstructured large objects such as videos and manuals, with virtually unlimited scalability and high durability. Option A (RDS) would not handle massive scale or traffic spikes as efficiently. Option C overloads DynamoDB by forcing it to store large binary data, which is not its purpose. Option D (DocumentDB) is suitable for JSON-like documents but not optimal for storing large media files and would add operational complexity. Therefore, option B represents the best separation of structured and unstructured data storage.

Comprehensive and Detailed Explanation:

To authenticate users using an on-premises Active Directory Federation Service (AD FS), AWS provides the AWS Directory Service AD Connector. AD Connector is a proxy that connects AWS applications to your on-premises Microsoft Active Directory without requiring complex directory synchronization or the need to set up a separate directory in the cloud.

By integrating AD Connector with the Application Load Balancer (ALB), you can authenticate and authorize users using your existing on-premises credentials. This setup allows the ALB to leverage the authentication capabilities of your on-premises AD FS, providing a seamless and secure user experience.

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.

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

Key AWS references and benefits ofS3 Intelligent-Tiering:

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

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

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

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

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.

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 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.

When on-premises DNS servers need to resolve private DNS names in a VPC, the correct pattern is to create a Route 53 Resolver inbound endpoint. The inbound endpoint allows DNS queries to flow from the on-premises environment into the VPC, where Route 53 can resolve VPC-specific names (such as private hosted zones or private resource records). Outbound endpoints (C) are for sending VPC DNS queries to on-premises, not the reverse. Verified Access (A) is unrelated to DNS resolution. Direct Connect (B) provides network connectivity but does not provide DNS forwarding capabilities. Therefore, option D is the correct design.

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 most secure and manageable way to provide developers with temporary, least-privilege access is by using AWS IAM Identity Center (formerly AWS SSO). IAM Identity Center allows assigning IAM roles with scoped permissions based on the developer’s team role. This ensures no permanent credentials are required and minimizes risk.

Option B enables role-based access with centralized identity and access management, making it the most secure and scalable solution for managing developer permissions.

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

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

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.

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.

CloudFront Signed URLs: These URLs allow you to provide limited access to content that is being served through an Amazon CloudFront distribution. Signed URLs can be generated to grant time-limited access to premium customers.

Content Restriction:

By using CloudFront signed URLs, you can control access to your media streams and file content stored in S3.

These URLs can be customized with an expiration time, ensuring that access is only available for a specific period, which is useful for scenarios like movie rentals or music downloads.

Security and Flexibility:

Signed URLs ensure that only authenticated users (premium customers) can access the restricted content.

This approach integrates seamlessly with CloudFront and S3, providing an efficient way to manage access controls without additional overhead.

Operational Efficiency: Using CloudFront signed URLs leverages AWS managed services to handle the complexity of access control, reducing the need for custom implementation and maintenance.

AWS WAF (Web Application Firewall) is designed to protect public-facing web applications from common web exploits and allows for deep inspection of HTTP and HTTPS requests. By enabling logging on AWS WAF, you can gain insights into traffic flow, request sources, and blocked requests, which improves both security visibility and posture. Integrating AWS WAF logging with Amazon Kinesis Data Firehose allows automatic, near-real-time delivery of log data to Amazon S3 for analysis, reporting, or integration with analytics tools. This solution not only secures the website but also enables comprehensive traffic analysis, satisfying both requirements efficiently.

Reference Extract from AWS Documentation / Study Guide:

"AWS WAF provides detailed logs of web requests, which can be delivered to Amazon S3 via Amazon Kinesis Data Firehose. This logging enables analysis of web traffic and sources, supporting both security and operational monitoring."

Source: AWS Certified Solutions Architect – Official Study Guide, Security and Compliance section; AWS WAF Developer Guide (Logging and Monitoring).

The optimal solution for scalable and resilient hybrid networking is to use AWS Direct Connect with a Direct Connect gateway for secure, low-latency access to AWS, and an AWS Transit Gateway to manage connectivity among hundreds of VPCs.

By associating the Transit Gateway with the Direct Connect gateway, you enable transitive routing between on-premises and all VPCs, while minimizing network complexity and maintaining high performance.

VPC peering does not scale well, and VPNs don’t offer the same performance or consistency.

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.

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.

An Application Load Balancer supports path- and host-based routing, which makes it ideal for routing requests based on subdomains. EC2 Auto Scaling ensures that the number of instances adjusts dynamically based on traffic, which helps manage cost and performance during predictable peak hours.

Amazon FSx for ONTAP supports both NFS and SMB protocols and includes automated tiering between SSD and capacity pool storage, optimizing cost and performance. It is ideal for mixed operating systems and varied access patterns with minimal administrative overhead.

For the migration of data from an on-premises Oracle database to Amazon Aurora PostgreSQL, this solution effectively handles schema conversion, data migration, and ongoing data replication.

AWS Schema Conversion Tool (SCT): SCT is used to convert the Oracle database schema to a format compatible with Aurora PostgreSQL. This tool automatically converts the database schema and code objects, like stored procedures, to the target database engine.

AWS Database Migration Service (DMS): DMS is employed to perform the data migration. It supports both full-load migrations (for initial data transfer) and continuous replication of ongoing changes (Change Data Capture, or CDC). This ensures that any updates to the Oracle database during the migration are captured and applied to the Aurora PostgreSQL database, minimizing downtime.

Why Not Other Options?:

Option A (SCT + DMS full-load only): This option does not capture ongoing changes, which is crucial for a live database migration to ensure data consistency.

Option B (DataSync + S3): AWS DataSync is more suited for file transfers rather than database migrations, and it doesn’t support ongoing change replication.

Option D (Snowball + S3): Snowball is typically used for large-scale data transfers that don’t require continuous synchronization, making it less suitable for this scenario where ongoing changes must be captured.

AWS References:

AWS Schema Conversion Tool- Guidance on using SCT for database schema conversions.

AWS Database Migration Service- Detailed documentation on using DMS for data migrations and ongoing replication.

Amazon Elastic File System (EFS) is a managed file storage service that can be mounted across multiple EC2 instances. It provides a scalable and high-performing solution to share data among instances within a VPC.

High Performance: EFS provides scalable performance for workloads that require high throughput and IOPS. It is particularly well-suited for applications that need to share data across multiple instances.

Ease of Use: EFS can be easily mounted on multiple instances across different Availability Zones, providing a shared file system accessible to all the instances within the VPC.

Security: EFS can be configured to ensure that data remains within the VPC, and it supports encryption at rest and in transit.

Why Not Other Options?:

Option A (Amazon S3 bucket with APIs): While S3 is excellent for object storage, it is not a file system and does not provide the low-latency access required for shared data between instances.

Option B (S3 bucket as a mounted volume): S3 is not designed to be mounted as a file system, and this approach would introduce unnecessary complexity and latency.

Option C (EBS volume shared across instances): EBS volumes cannot be attached to multiple instances simultaneously. It is not designed to be shared across instances like EFS.

AWS References:

Amazon EFS- Overview of Amazon EFS and its features.

Best Practices for Amazon EFS- Recommendations for using EFS with multiple instances.

Amazon S3 Intelligent-Tiering: This storage class is designed to optimize costs by automatically moving data between two access tiers (frequent and infrequent) when access patterns change. It provides cost savings without performance impact or operational overhead.

S3 Lifecycle Rules: By creating an S3 Lifecycle rule, the company can automatically transition objects to the Intelligent-Tiering storage class. This eliminates the need for manual intervention and ensures that objects are moved to the most cost-effective storage tier based on their access patterns.

Operational Efficiency: Intelligent-Tiering requires no additional management and delivers immediate availability for frequently accessed objects. This makes it the most operationally efficient solution for the given requirements.

Key Requirements:

Host the frontend on AWS as a static website.

Protect the application from common web vulnerabilities.

Minimal operational overhead.

Analysis of Options:

Option A:

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

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

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

Option B:

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

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

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

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

Option C:

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

Incorrect Approach:Over-engineered solution.

Option D:

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

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

AWS Solution Architect References:

Amazon CloudFront Overview

AWS WAF with CloudFront

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

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.

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

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

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

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

AWS References:

DynamoDB Global Tables

Amazon Aurora MySQL supports the SELECT INTO OUTFILE S3 SQL syntax to export query results directly to Amazon S3. This is an efficient and low-overhead method for archiving data.

Once data is in S3, Lifecycle rules can be configured to automatically transition older data to lower-cost storage classes (such as S3 Glacier) or delete it after a defined period, providing a cost-optimized and automated archive solution.

The Glue-based options involve more services and operational overhead. SCT is intended for database migrations, not for periodic data archival.

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.

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

Key AWS features:

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

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

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

Caching frequently accessed, identical datasets is a well-established way to improve backend application performance by reducing load on the database. Amazon ElastiCache with Redis (open source) offers a fast, in-memory data store to cache query results, reducing latency and database requests.

Option B directly addresses the problem by offloading repeated read requests from the database to the cache.

Option A (SNS) is a messaging service and is unrelated to caching or improving database performance. Option C (DAX) accelerates DynamoDB but the backend uses RDS MySQL, so DAX is inapplicable. Option D (Data Firehose) is a data streaming service and does not optimize database read performance.

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 CloudFront is a content delivery network (CDN) service that caches copies of your content at edge locations around the world. This helps improve performance by serving content from the edge nearest to the user. However, when the content in Amazon S3 (your origin) is updated, those updates may not immediately reflect on the website if they are cached at the CloudFront edge locations.

The issue described in the question suggests that the CI/CD pipeline is functioning correctly, and updates are being deployed to S3. However, since CloudFront caches this content, the edge locations may still be serving outdated content, causing the updates to not be reflected on the website.

To resolve this issue, you need to invalidate the CloudFront cache. By invalidating the cache, CloudFront will remove the outdated content and retrieve the latest version from the S3 origin.

AWS documentation on this process:

CloudFront cache invalidation allows you to clear items from the cache so that CloudFront retrieves the latest version from the origin. You can create invalidation requests via the AWS Management Console, AWS CLI, or SDKs.

AWS CloudFront Documentation

Why the other options are incorrect:

A. Add an Application Load Balancer: ALBs are used to distribute incoming application traffic and are not relevant to caching or serving content from CloudFront.

B. Add Amazon ElastiCache for Redis or Memcached: This would help in caching database queries but has no relation to static website content hosted on CloudFront and S3.

D. Use AWS Certificate Manager (ACM): ACM is used for managing SSL/TLS certificates and is unrelated to the issue of content not being updated on CloudFront.

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

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.