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Amazon SAA-C03 Exam Questions
AWS Certified Solutions Architect - Associate SAA-C03 (Page 34 )

Updated On: 20-Mar-2026
Page 34 of 205
PDF with 824 Questions

A company has a small Python application that processes JSON documents and outputs the results to an on-premises SQL database. The application runs thousands of times each day. The company wants to move the application to the AWS Cloud. The company needs a highly available solution that maximizes scalability and minimizes operational overhead.
Which solution will meet these requirements?

  1. Place the JSON documents in an Amazon S3 bucket. Run the Python code on multiple Amazon EC2 instances to process the documents. Store the results in an Amazon Aurora DB cluster.
  2. Place the JSON documents in an Amazon S3 bucket. Create an AWS Lambda function that runs the Python code to process the documents as they arrive in the S3 bucket. Store the results in an Amazon Aurora DB cluster.
  3. Place the JSON documents in an Amazon Elastic Block Store (Amazon EBS) volume. Use the EBS Multi-Attach feature to attach the volume to multiple Amazon EC2 instances. Run the Python code on the EC2 instances to process the documents. Store the results on an Amazon RDS DB instance.
  4. Place the JSON documents in an Amazon Simple Queue Service (Amazon SQS) queue as messages. Deploy the Python code as a container on an Amazon Elastic Container Service (Amazon ECS) cluster that is configured with the Amazon EC2 launch type. Use the container to process the SQS messages. Store the results on an Amazon RDS DB instance.

Answer(s): B

Explanation:

The correct answer is B because using S3 with a Lambda function provides serverless, highly available, and scalable processing as events arrive, with minimal operational overhead; the Python code runs in managed Lambda execution environments and results can be stored in Aurora for relational queries.
A) While scalable, EC2-based processing requires managing instances and scaling, increasing operational overhead compared to Lambda.
C) EBS Multi-Attach is for shared block storage, not ideal for stateless serverless processing; EC2 management and availability concerns remain.
D) ECS with EC2 requires cluster management and scaling; SQS decoupling adds latency and operational overhead versus a direct Lambda-triggered workflow.



A company wants to use high performance computing (HPC) infrastructure on AWS for financial risk modeling. The company’s HPC workloads run on Linux. Each HPC workflow runs on hundreds of Amazon EC2 Spot Instances, is short-lived, and generates thousands of output files that are ultimately stored in persistent storage for analytics and long-term future use.
The company seeks a cloud storage solution that permits the copying of on-premises data to long-term persistent storage to make data available for processing by all EC2 instances. The solution should also be a high performance file system that is integrated with persistent storage to read and write datasets and output files.
Which combination of AWS services meets these requirements?

  1. Amazon FSx for Lustre integrated with Amazon S3
  2. Amazon FSx for Windows File Server integrated with Amazon S3
  3. Amazon S3 Glacier integrated with Amazon Elastic Block Store (Amazon EBS)
  4. Amazon S3 bucket with a VPC endpoint integrated with an Amazon Elastic Block Store (Amazon EBS) General Purpose SSD (gp2) volume

Answer(s): A

Explanation:

Amazon FSx for Lustre integrated with Amazon S3 provides a high-performance parallel file system (Lustre) for HPC workloads and can automatically link with S3 for persistent storage, enabling on-demand data ingest from on-premises and long-term storage with parallel read/write across hundreds of EC2 Spot Instances.
A) Correct. FSx for Lustre + S3 offers HPC-optimized throughput, Lustre metadata management, and scalable storage with durable S3 backing.
B) FSx for Windows File Server is a Windows-based file system, not optimized for Linux HPC workloads and Lustre-like performance.
C) S3 Glacier is archival storage with retrieval delays, not a high-performance file system for HPC read/write workloads.
D) S3 with EBS gp2 is not a unified HPC file system; lacks built-in parallel file system semantics and seamless on-demand data ingest from on-premises.



A company is building a containerized application on premises and decides to move the application to AWS. The application will have thousands of users soon after it is deployed. The company is unsure how to manage the deployment of containers at scale. The company needs to deploy the containerized application in a highly available architecture that minimizes operational overhead.
Which solution will meet these requirements?

  1. Store container images in an Amazon Elastic Container Registry (Amazon ECR) repository. Use an Amazon Elastic Container Service (Amazon ECS) cluster with the AWS Fargate launch type to run the containers. Use target tracking to scale automatically based on demand.
  2. Store container images in an Amazon Elastic Container Registry (Amazon ECR) repository. Use an Amazon Elastic Container Service (Amazon ECS) cluster with the Amazon EC2 launch type to run the containers. Use target tracking to scale automatically based on demand.
  3. Store container images in a repository that runs on an Amazon EC2 instance. Run the containers on EC2 instances that are spread across multiple Availability Zones. Monitor the average CPU utilization in Amazon CloudWatch. Launch new EC2 instances as needed.
  4. Create an Amazon EC2 Amazon Machine Image (AMI) that contains the container image. Launch EC2 instances in an Auto Scaling group across multiple Availability Zones. Use an Amazon CloudWatch alarm to scale out EC2 instances when the average CPU utilization threshold is breached.

Answer(s): A

Explanation:

AWS Fargate with ECS provides a serverless, highly available, and low-operational-overhead container deployment that scales automatically based on demand, without managing underlying EC2 instances. A uses ECR+ECS with Fargate and target tracking, meeting HA and scalability with minimal ops.
B uses ECS with EC2 launch type, which requires managing EC2 instances, increasing operational overhead.
C relies on self-managed EC2 scaling and manual capacity planning, increasing complexity and maintenance.
D uses traditional EC2 AMIs and Auto Scaling groups, which involves more management and less abstraction for containerized workloads.



A company has two applications: a sender application that sends messages with payloads to be processed and a processing application intended to receive the messages with payloads. The company wants to implement an AWS service to handle messages between the two applications. The sender application can send about 1,000 messages each hour. The messages may take up to 2 days to be processed: If the messages fail to process, they must be retained so that they do not impact the processing of any remaining messages.
Which solution meets these requirements and is the MOST operationally efficient?

  1. Set up an Amazon EC2 instance running a Redis database. Configure both applications to use the instance. Store, process, and delete the messages, respectively.
  2. Use an Amazon Kinesis data stream to receive the messages from the sender application. Integrate the processing application with the Kinesis Client Library (KCL).
  3. Integrate the sender and processor applications with an Amazon Simple Queue Service (Amazon SQS) queue. Configure a dead-letter queue to collect the messages that failed to process.
  4. Subscribe the processing application to an Amazon Simple Notification Service (Amazon SNS) topic to receive notifications to process. Integrate the sender application to write to the SNS topic.

Answer(s): C

Explanation:

SQS with a standard queue and a dead-letter queue meets the throughput, message retention (up to 14 days by default, configurable), and exactly-once-like processing guarantees via idempotent processing, while decoupling sender and processor for operational efficiency.
A) EC2/Redis requires managing infrastructure, does not guarantee durable retention or message processing semantics, and adds operational burden.
B) Kinesis is streaming with limited message retention (default 24 hours, extended) and higher complexity; not ideal for per-message retry/dead-letter semantics.
D) SNS is pub/sub, not a durable queue; messages may be lost if no subscribers, and it lacks robust retry/dead-letter semantics for processing failures.



A solutions architect must design a solution that uses Amazon CloudFront with an Amazon S3 origin to store a static website. The company’s security policy requires that all website traffic be inspected by AWS WAF.
How should the solutions architect comply with these requirements?

  1. Configure an S3 bucket policy to accept requests coming from the AWS WAF Amazon Resource Name (ARN) only.
  2. Configure Amazon CloudFront to forward all incoming requests to AWS WAF before requesting content from the S3 origin.
  3. Configure a security group that allows Amazon CloudFront IP addresses to access Amazon S3 only. Associate AWS WAF to CloudFront.
  4. Configure Amazon CloudFront and Amazon S3 to use an origin access identity (OAI) to restrict access to the S3 bucket. Enable AWS WAF on the distribution.

Answer(s): D

Explanation:

CloudFront with an origin access identity (OAI) ensures CloudFront is the sole requester to S3, preventing direct S3 access, while enabling WAF to inspect traffic at the CloudFront distribution.
A) S3 bucket policy with WAF ARN does not integrate WAF inspection on requests; WAF sits in front of CloudFront, not via S3 bucket policy.
B) WAF is associated with CloudFront, but you don’t forward requests to WAF before S3; WAF sits at the edge in front of CloudFront, not between CloudFront and S3.
C) Security groups and CloudFront IPs are not applicable to S3 access control; WAF attachment is on CloudFront, not via SGs.



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