Amazon Web Services examstrust amazon Web Services Saa-c03 Online Access by Alvey q160 vce pdf

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Exam Name:	AWS Certified Solutions Architect - Associate (SAA-C03)
Exam Code:	SAA-C03 Dumps
Vendor:	Amazon Web Services	Certification:	AWS Certified Associate
Questions:	879 Q&A's	Shared By:	alvey

Question 84

A solutions architect is designing an asynchronous application to process credit card data validation requests for a bank. The application must be secure and be able to process each request at least once.

Which solution will meet these requirements MOST cost-effectively?

Options:

Use AWS Lambda event source mapping. Set Amazon SQS standard queues as the event source. Use AWS KMS SSE-KMS for encryption. Add the kms:Decrypt permission for the Lambda execution role.

Use AWS Lambda event source mapping. Use Amazon SQS FIFO queues as the event source. Use SQS managed encryption keys SSE-SQS for encryption. Add the encryption key invocation permission for the Lambda function.

Use AWS Lambda event source mapping. Set Amazon SQS FIFO queues as the event source. Use AWS KMS keys SSE-KMS. Add the kms:Decrypt permission for the Lambda execution role.

Use AWS Lambda event source mapping. Set Amazon SQS standard queues as the event source. Use AWS KMS keys SSE-KMS for encryption. Add the encryption key invocation permission for the Lambda function.

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Question 85

Question:

A company is building an ecommerce application that uses a relational database to store customer data and order history. The company also needs a solution to store 100 GB of product images. The company expects the traffic flow for the application to be predictable. Which solution will meet these requirements MOST cost-effectively?

Options:

Options:

Use Amazon RDS for MySQL for the database. Store the product images in an Amazon S3 bucket.

Use Amazon DynamoDB for the database. Store the product images in an Amazon S3 bucket.

Use Amazon RDS for MySQL for the database. Store the product images in an Amazon Aurora MySQL database.

Create three Amazon EC2 instances. Install MongoDB software on the instances to use as the database. Store the product images in an Amazon RDS for MySQL database with a Multi-AZ deployment.

Discussion

Question 86

A solutions architect needs to host a high performance computing (HPC) workload in the AWS Cloud. The workload will run on hundreds of Amazon EC2 instances and will require parallel access to a shared file system to enable distributed processing of large datasets. Datasets will be accessed across multiple instances simultaneously. The workload requires access latency within 1 ms. After processing has completed, engineers will need access to the dataset for manual postprocessing.

Which solution will meet these requirements?

Options:

Use Amazon Elastic File System (Amazon EFS) as a shared fie system. Access the dataset from Amazon EFS.

Mount an Amazon S3 bucket to serve as the shared file system. Perform postprocessing directly from the S3 bucket.

Use Amazon FSx for Lustre as a shared file system. Link the file system to an Amazon S3 bucket for postprocessing.

Configure AWS Resource Access Manager to share an Amazon S3 bucket so that it can be mounted to all instances for processing and postprocessing.

Discussion

Answer:

Explanation:

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

Question 87

A healthcare company stores personally identifiable information (PII) data in an Amazon RDS for Oracle database. The company must encrypt the PII data at rest. The company must use dedicated hardware modules to store and manage the encryption keys.

Options:

Use AWS Key Management Service (AWS KMS) to configure encryption for the RDS database. Store and manage keys in AWS CloudHSM.

Use AWS CloudHSM backed AWS KMS keys to configure transparent encryption for the RDS database.

Use Amazon EC2 instance store encryption to encrypt database volumes by using AWS CloudHSM backed keys.

Configure RDS snapshots and use server-side encryption with Amazon S3 managed keys (SSE-S3). Store the keys in AWS CloudHSM.