Amazon Web Services Updated SAA-C03 Exam Questions and Answers by alvey

The requirement issecure at-least-once processing, not exactly-once processing. Amazon SQSstandard queuesprovide at-least-once delivery, which matches the requirement and is generally more cost-effective than FIFO queues. When Lambda is configured with an encrypted SQS queue that usesSSE-KMS, AWS requires the Lambda execution role to have thekms:Decryptpermission so Lambda can poll and process messages successfully. FIFO queues add ordering and deduplication features that are not required here, so they are not the most economical choice. Option A is therefore the best fit because it combines the correct delivery model, queue encryption with AWS KMS, and the necessary IAM permission for Lambda.

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

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

Amazon RDS supports encryption at rest by using AWS KMS keys backed by AWS CloudHSM. This allows use of dedicated FIPS 140-2 Level 3 validated hardware modules to manage encryption keys, meeting compliance for sensitive data such as PII.

From AWS Documentation:

“You can use AWS KMS with keys that are backed by AWS CloudHSM to control the encryption of RDS databases. This provides dedicated HSM-backed key storage and management.”

(Source: Amazon RDS User Guide – Encrypting Amazon RDS Resources)

Why B is correct:

Meets the requirement for dedicated HSM hardware.

Fully integrates with RDS for transparent encryption at rest.

Satisfies compliance standards for healthcare and regulated data.

Why others are incorrect:

A: Keys in CloudHSM directly are not used by RDS; they must be managed through KMS integration.

C: EC2 instance stores are ephemeral, not suitable for RDS databases.

D: SSE-S3 applies to S3 objects, not databases.