Practice Free SAA-C03 Exam Online Questions
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?
- A . Use Amazon Elastic File System (Amazon EFS) as a shared fie system. Access the dataset from Amazon EFS.
- B . Mount an Amazon S3 bucket to serve as the shared file system. Perform postprocessing directly from the S3 bucket.
- C . Use Amazon FSx for Lustre as a shared file system. Link the file system to an Amazon S3 bucket for postprocessing.
- D . Configure AWS Resource Access Manager to share an Amazon S3 bucket so that it can be mounted to all instances for processing and postprocessing.
C
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
Reference: Amazon FSx for Lustre
Best Practices for High Performance Computing (HPC)
Amazon FSx and Amazon S3 Integration
An ecommerce company experiences a surge in mobile application traffic every Monday at 8 AM during the company’s weekly sales events. The application’s backend uses an Amazon API Gateway HTTP API and AWS Lambda functions to process user requests. During peak sales periods, users report encountering TooManyRequestsException errors from the Lambda functions. The errors result in a degraded user experience. A solutions architect needs to design a scalable and resilient solution that minimizes the errors and ensures that the application’s overall functionality remains unaffected.
- A . Create an Amazon Simple Queue Service (Amazon SQS) queue. Send user requests to the SQS queue. Configure the Lambda function with provisioned concurrency. Set the SQS queue as the event source trigger.
- B . Use AWS Step Functions to orchestrate and process user requests. Configure Step Functions to invoke the Lambda functions and to manage the request flow.
- C . Create an Amazon Simple Notification Service (Amazon SNS) topic. Send user requests to the SNS topic. Configure the Lambda functions with provisioned concurrency. Subscribe the functions to the SNS topic.
- D . Create an Amazon Simple Queue Service (Amazon SQS) queue. Send user requests to the SQS queue. Configure the Lambda functions with reserved concurrency. Set the SQS queue as the event source trigger for the functions.
A
Explanation:
TooManyRequestsException errors occur when Lambda exceeds concurrency limits. The recommended pattern is to use Amazon SQS with Lambda to decouple and buffer traffic, ensuring that bursts of requests are queued and processed smoothly. Enabling provisioned concurrency for Lambda ensures that functions are pre-initialized and ready to handle spikes in load with low latency. Step Functions (B) is designed for workflow orchestration, not high-throughput request buffering. SNS with Lambda (C) does not provide buffering and may overwhelm Lambda during bursts. Reserved concurrency (D) limits function scaling instead of improving resilience.
Therefore, option A provides a scalable and resilient solution, minimizing errors during traffic surges.
Reference:
• AWS Lambda Developer Guide ― Provisioned concurrency and scaling with SQS
• Amazon SQS User Guide ― Using Lambda with Amazon SQS
• AWS Well-Architected Framework ― Reliability Pillar
A company runs an order management application on AWS. The application allows customers to place orders and pay with a credit card. The company uses an Amazon CloudFront distribution to deliver the application.
A security team has set up logging for all incoming requests. The security team needs a solution to generate an alert if any user modifies the logging configuration. (Select TWO):
- A . Configure an Amazon EventBridge rule that is invoked when a user creates or modifies a CloudFront distribution. Add the AWS Lambda function as a target of the EventBridge rule.
- B . Create an Application Load Balancer (ALB). Enable AWS WAF rules for the ALB. Configure an AWS Config rule to detect security violations.
- C . Create an AWS Lambda function to detect changes in CloudFront distribution logging. Configure the Lambda function to use Amazon Simple Notification Service (Amazon SNS) to send notifications to the security team.
- D . Set up Amazon GuardDuty. Configure GuardDuty to monitor findings from the CloudFront distribution. Create an AWS Lambda function to address the findings.
- E . Create a private API in Amazon API Gateway. Use AWS WAF rules to protect the private API from common security problems.
A,C
Explanation:
Detailed
A company uses Amazon S3 to host its static website. The company wants to add a contact form to the webpage. The contact form will have dynamic server-side components for users to input their name, email address, phone number, and user message.
The company expects fewer than 100 site visits each month. The contact form must notify the company by email when a customer fills out the form.
Which solution will meet these requirements MOST cost-effectively?
- A . Host the dynamic contact form in Amazon Elastic Container Service (Amazon ECS). Set up Amazon Simple Email Service (Amazon SES) to connect to a third-party email provider.
- B . Create an Amazon API Gateway endpoint that returns the contact form from an AWS Lambda function. Configure another Lambda function on the API Gateway to publish a message to an Amazon Simple Notification Service (Amazon SNS) topic.
- C . Host the website by using AWS Amplify Hosting for static content and dynamic content. Use server-side scripting to build the contact form. Configure Amazon Simple Queue Service (Amazon SQS) to deliver the message to the company.
- D . Migrate the website from Amazon S3 to Amazon EC2 instances that run Windows Server. Use Internet Information Services (IIS) for Windows Server to host the webpage. Use client-side scripting to build the contact form. Integrate the form with Amazon WorkMail.
B
Explanation:
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.
Reference: AWS Documentation C Serverless Contact Form with API Gateway, Lambda, and SNS
A company has multiple Amazon RDS DB instances that run in a development AWS account. All the instances have tags to identify them as development resources. The company needs the development DB instances to run on a schedule only during business hours.
Which solution will meet these requirements with the LEAST operational overhead?
- A . Create an Amazon CloudWatch alarm to identify RDS instances that need to be stopped Create an AWS Lambda function to start and stop the RDS instances.
- B . Create an AWS Trusted Advisor report to identify RDS instances to be started and stopped. Create an AWS Lambda function to start and stop the RDS instances.
- C . Create AWS Systems Manager State Manager associations to start and stop the RDS instances.
- D . Create an Amazon EventBridge rule that invokes AWS Lambda functions to start and stop the RDS instances.
D
Explanation:
To run RDS instances only during business hours with the least operational overhead, you can useAmazon EventBridgeto schedule events that invokeAWS Lambda functions. The Lambda functions can be configured to start and stop the RDS instances based on the specified schedule (business hours). EventBridge rules allow you to define recurring events easily, and Lambda functions provide a serverless way to manage RDS instance start and stop operations, reducing administrative overhead.
Option A: While CloudWatch alarms could be used, they are more suited for monitoring, and using Lambda with EventBridge is simpler.
Option B (Trusted Advisor): Trusted Advisor is not ideal for scheduling tasks.
Option C (Systems Manager): Systems Manager could also work, but EventBridge and Lambda offer a more streamlined and lower-overhead solution.
AWS
Reference: Amazon EventBridge Scheduler
AWS Lambda
A company stores data for multiple business units in a single Amazon S3 bucket that is in the company’s payer AWS account. To maintain data isolation, the business units store data in separate prefixes in the S3 bucket by using an S3 bucket policy.
The company plans to add a large number of dynamic prefixes. The company does not want to rely on a single S3 bucket policy to manage data access at scale. The company wants to develop a secure access management solution in addition to the bucket policy to enforce prefix-level data isolation.
- A . Configure the S3 bucket policy to deny s3: GetObject permissions for all users. Configure the bucket policy to allow s3: * access to individual business units.
- B . Enable default encryption on the S3 bucket by using server-side encryption with Amazon S3 managed keys (SSE-S3).
- C . Configure resource-based permissions on the S3 bucket by creating an S3 access point for each business unit.
- D . Use pre-signed URLs to provide access to the S3 bucket.
C
Explanation:
Why Option C is Correct:
S3 Access Points: Provide scalable management of access to large datasets with specific permissions for individual prefixes.
Dynamic Prefixes: Access points simplify managing access to a growing number of prefixes without relying solely on a single bucket policy.
Fine-Grained Control: Resource-based permissions on access points enforce prefix-level isolation effectively.
Why Other Options Are Not Ideal:
Option A: Using deny/allow bucket policies introduces complexity and is less scalable for dynamic prefixes.
Option B: Encryption ensures data security but does not address access management.
Option D: Pre-signed URLs are temporary and not suitable for managing access at scale. AWS
Reference: Amazon S3 Access Points: AWS Documentation – S3 Access Points
A company is developing a serverless, bidirectional chat application that can broadcast messages to connected clients. The application is based on AWS Lambda functions. The Lambda functions receive incoming messages in JSON format.
The company needs to provide a frontend component for the application.
Which solution will meet this requirement?
- A . Use an Amazon API Gateway HTTP API to direct incoming JSON messages to backend destinations.
- B . Use an Amazon API Gateway REST API that is configured with a Lambda proxy integration.
- C . Use an Amazon API Gateway WebSocket API to direct incoming JSON messages to backend destinations.
- D . Use an Amazon CloudFront distribution that is configured with a Lambda function URL as a custom origin.
C
Explanation:
For bidirectional communication such as chat applications, Amazon API Gateway WebSocket API is the correct service. WebSocket APIs allow clients to establish long-lived connections and exchange messages with the backend Lambda functions in real time.
HTTP APIs and REST APIs are suitable for request-response models, not continuous two-way communication. CloudFront cannot maintain stateful WebSocket connections, so only Option C fits the requirements for a real-time, bidirectional application.
A healthcare company is running an Amazon EMR cluster on Amazon EC2 instances to process data that is stored in Amazon S3. The company must ensure that the data processing jobs have access only to the relevant data in Amazon S3. Each job must have specific EMR runtime roles.
Which combination of steps will meet these requirements? (Select THREE.)
- A . Set up security configurations in Amazon EMR, and set EnableApplicationScopedIAMRole to true.
- B . Set up runtime roles to assume the EC2 instance profile of the Amazon EMR cluster.
- C . Set up an EC2 instance profile for the Amazon EMR cluster to assume the runtime roles.
- D . For each IAM role that serves as an EMR runtime role, set up a trust policy with the EC2 instance profile role.
- E . Establish a trust policy between the EMR runtime roles and the EMR service role of the cluster.
- F . Set up security configurations in Amazon EMR, and set EnableInTransitEncryption to true.
A,C,D
Explanation:
Amazon EMR on EC2 supports “runtime roles (application-scoped IAM roles)” so each application/step assumes its own IAM role with least-privilege S3 access. You enable this via an EMR security configuration by setting “EnableApplicationScopedIAMRole = true.”. The EMR core/Task nodes run under the cluster’s EC2 instance profile; therefore the instance profile must be permitted to “sts: AssumeRole” into the defined EMR runtime roles, and each runtime role must trust the instance profile (trust policy principal is the instance profile role). This design limits each job’s S3 scope via role policies and enforces per-job access segregation.
Option B reverses the trust (incorrect).
Option E trusts the EMR service role (not used to assume runtime roles).
Option F is unrelated (encryption in transit). The correct trio is to enable application-scoped roles (A), authorize the instance profile to assume them (C), and configure the runtime roles’ trust relationship to allow that assumption (D).
Reference: Amazon EMR Management Guide ― EMR Runtime Roles / Application-scoped IAM roles; IAM Roles and Trust Policies; EMR Security Configuration settings.
A solutions architect is creating a new Amazon CloudFront distribution for an application. Some of the information submitted by users is sensitive. The application uses HTTPS but needs another layer of security. The sensitive information should be protected throughout the entire application stack, and access to the information should be restricted to certain applications.
Which action should the solutions architect take?
- A . Configure a CloudFront signed URL.
- B . Configure a CloudFront signed cookie.
- C . Configure a CloudFront field-level encryption profile.
- D . Configure CloudFront and set the Origin Protocol Policy setting to HTTPS Only for the Viewer Protocol Policy.
C
Explanation:
Field-level encryption in Amazon CloudFront provides end-to-end encryption for specific data fields (e.g., credit card numbers, social security numbers). It ensures that sensitive fields are encrypted at the edge before being forwarded to the origin, and only the authorized application with the private key can decrypt them.
This adds a layer of protection beyond HTTPS, which encrypts the whole payload but not individual fields. Signed URLs and cookies are for access control, not encryption. Setting HTTPS Only is a good practice but does not satisfy the field-specific encryption requirement.
A company manages millions of documents in hundreds of Amazon S3 buckets in multiple AWS Regions. The company must determine whether any of the S3 buckets contain personally identifiable information (PII).
Which solution will meet this requirement with the LEAST operational overhead?
- A . Use Amazon Detective to detect PII in the S3 buckets.
- B . Use AWS Trusted Advisor to generate PII notifications.
- C . Use Amazon Macie to detect PII in the S3 buckets.
- D . Use AWS Lambda functions to review each file in the S3 buckets to identify PII.
C
Explanation:
Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):
Amazon Macie is the AWS managed service specifically built to discover, classify, and protect sensitive data in Amazon S3, including many common types of personally identifiable information (PII). Macie uses automated analysis and machine learning to identify sensitive data patterns at scale and produces findings that can be reviewed, prioritized, and integrated into security workflows. Because the environment spans hundreds of buckets, millions of objects, and multiple Regions, the key requirement is to minimize operational overhead while achieving broad coverage.
Option C is therefore the best fit: enabling Macie and configuring it to evaluate the targeted buckets provides a centralized, managed approach without building custom scanners, maintaining parsing logic, or operating distributed processing pipelines. Macie is designed for large-scale S3 estates and reduces ongoing maintenance compared with bespoke solutions.
Option A is incorrect because Amazon Detective is used to investigate and analyze security findings and relationships, not to classify S3 object content for PII.
Option B is incorrect because Trusted Advisor provides best-practice checks (cost, security posture items, limits), but it does not inspect S3 object contents to detect PII.
Option D would require building and operating custom Lambda scanning across millions of objects, handling pagination, retries, file types, performance tuning, and cost controls―high operational overhead and ongoing maintenance, which the company wants to avoid.
Therefore, C meets the requirement most directly and with the least operational burden by using the AWS service purpose-built for PII discovery in S3.