Amazon SAA-C03 Online Practice Questions and Exam Preparation

Amazon SAA-C03 Online Questions & Answers

• Question 511:

  A company is developing a latency-sensitive application. Part of the application includes several AWS Lambda functions that need to initialize as quickly as possible. The Lambda functions are written in Java and contain initialization code outside the handlers to load libraries, initialize classes, and generate unique

  IDs.

  Which solution will meet the startup performance requirement MOST cost-effectively?

  A. Move all the initialization code to the handlers for each Lambda function. Activate Lambda SnapStart for each Lambda function. Configure SnapStart to reference the $LATEST version of each Lambda function.
  B. Publish a version of each Lambda function. Create an alias for each Lambda function. Configure each alias to point to its corresponding version. Set up a provisioned concurrency configuration for each Lambda function to point to the corresponding alias.
  C. Publish a version of each Lambda function. Set up a provisioned concurrency configuration for each Lambda function to point to the corresponding version. Activate Lambda SnapStart for the published versions of the Lambda functions.
  D. Update the Lambda functions to add a pre-snapshot hook. Move the code that generates unique IDs into the handlers. Publish a version of each Lambda function. Activate Lambda SnapStart for the published versions of the Lambda functions.
  D. Update the Lambda functions to add a pre-snapshot hook. Move the code that generates unique IDs into the handlers. Publish a version of each Lambda function. Activate Lambda SnapStart for the published versions of the Lambda functions.

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  Explanation

  AWS Lambda SnapStart is designed to improve the cold start performance of Java Lambda functions by initializing the function, taking a snapshot of the execution environment, and then reusing that snapshot for subsequent invocations. However, some code (such as code that generates unique IDs or session-specific data) should run during each invocation, not during the snapshot process. By using a pre-snapshot hook and moving the unique ID generation into the handler, you ensure that non-deterministic or per-invocation code is executed correctly, while the rest of the initialization benefits from SnapStart. This delivers the lowest latency and cost, as you do not need to pay for provisioned concurrency.

  AWS Documentation Extract:

  " Lambda SnapStart is ideal for Java functions with long cold starts due to heavy initialization. Move non-deterministic code such as unique ID generation to the handler, and use pre-snapshot hooks to customize what gets snapshotted.

  SnapStart works only for published versions, not $LATEST. " (Source: AWS Lambda documentation, Using SnapStart for Java Functions)

  Other options:

  Option A: SnapStart is not supported for the $LATEST version; must be a published version.

  Option B & Option C: Provisioned concurrency removes cold starts but is more expensive than SnapStart for most workloads.

  Option C: You must use SnapStart on published versions, but provisioned concurrency is not required for SnapStart and adds cost.

  References:

  AWS Certified Solutions Architect?Official Study Guide, Lambda Performance Section.

• Question 512:

  A company has a mobile game that reads most of its metadata from an Amazon RDS DB instance. As the game increased in popularity, developers noticed slowdowns related to the game's metadata load times.

  Performance metrics indicate that simply scaling the database will not help. A solutions architect must explore all options that include capabilities for snapshots, replication, and sub-millisecond response times.

  What should the solutions architect recommend to solve these issues?

  A. Migrate the database to Amazon Aurora with Aurora Replicas.
  B. Migrate the database to Amazon DynamoDB with global tables.
  C. Add an Amazon ElastiCache for Redis layer in front of the database.
  D. Add an Amazon ElastiCache for Memcached layer in front of the database.
  C. Add an Amazon ElastiCache for Redis layer in front of the database.

• Question 513:

  A company is using an Amazon Redshift cluster to run analytics queries for multiple sales teams. In addition to the typical workload, on the last Monday morning of each month, thousands of users run reports. Users have reported slow response times during the monthly surge.

  The company must improve query performance without impacting the availability of the Redshift cluster.

  Which solution will meet these requirements?

  A. Resize the Redshift cluster by using the classic resize capability of Amazon Redshift before every monthly surge. Reduce the cluster to its original size after each surge.
  B. Resize the Redshift cluster by using the elastic resize capability of Amazon Redshift before every monthly surge. Reduce the cluster to its original size after each surge.
  C. Enable the concurrency scaling feature for the Redshift cluster for specific workload management (WLM) queues.
  D. Enable Amazon Redshift Spectrum for the Redshift cluster before every monthly surge.
  C. Enable the concurrency scaling feature for the Redshift cluster for specific workload management (WLM) queues.

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  Explanation

  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.

• Question 514:

  A company's data platform uses an Amazon Aurora MySQL database. The database has multiple read replicas and multiple DB instances across different Availability Zones. Users have recently reported errors from the database that indicate that there are too many connections. The company wants to reduce the failover time by 20% when a read replica is promoted to primary writer.

  Which solution will meet this requirement?

  A. Switch from Aurora to Amazon RDS with Multi-AZ cluster deployment.
  B. Use Amazon RDS Proxy in front of the Aurora database.
  C. Switch to Amazon DynamoDB with DynamoDB Accelerator (DAX) for read connections.
  D. Switch to Amazon Redshift with relocation capability.
  B. Use Amazon RDS Proxy in front of the Aurora database.

• Question 515:

  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. Use Amazon Macie to detect PII in the S3 buckets.

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  Explanation

  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, Option C meets the requirement most directly and with the least operational burden by using the AWS service purpose-built for PII discovery in S3.

• Question 516:

  A company has a static website that is hosted on Amazon CloudFront in front of Amazon S3. The static website uses a database backend. The company notices that the website does not reflect updates that have been made in the website's Git repository. The company checks the continuous integration and continuous delivery (CI/CD) pipeline between the Git repository and Amazon S3. The company verifies that the webhooks are configured properly and that the CI/CD pipeline is sending messages that indicate successful deployments.

  A solutions architect needs to implement a solution that displays the updates on the website.

  Which solution will meet these requirements?

  A. Add an Application Load Balancer.
  B. Add Amazon ElastiCache for Redis or Memcached to the database layer of the web application.
  C. Invalidate the CloudFront cache.
  D. Use AWS Certificate Manager (ACM) to validate the website's SSL certificate.
  C. Invalidate the CloudFront cache.

• Question 517:

  A company is using microservices to build an ecommerce application on AWS. The company wants to preserve customer transaction information after customers submit orders. The company wants to store transaction data in an Amazon Aurora database. The company expects sales volumes to vary throughout each year.

  Which solution will meet these requirements?

  A. Use an Amazon API Gateway REST API to invoke an AWS Lambda function to send transaction data to the Aurora database. Send transaction data to an Amazon Simple Queue Service (Amazon SQS) queue that has a dead-letter queue. Use a second Lambda function to read from the SQS queue and to update the Aurora database.
  B. Use an Amazon API Gateway HTTP API to send transaction data to an Application Load Balancer (ALB). Use the ALB to send the transaction data to Amazon Elastic Container Service (Amazon ECS) on Amazon EC2. Use ECS tasks to store the data in Aurora database.
  C. Use an Application Load Balancer (ALB) to route transaction data to Amazon Elastic Kubernetes Service (Amazon EKS). Use Amazon EKS to send the data to the Aurora database.
  D. Use Amazon Data Firehose to send transaction data to Amazon S3. Use AWS Database Migration Service (AWS DMS) to migrate the data from Amazon S3 to the Aurora database.
  A. Use an Amazon API Gateway REST API to invoke an AWS Lambda function to send transaction data to the Aurora database. Send transaction data to an Amazon Simple Queue Service (Amazon SQS) queue that has a dead-letter queue. Use a second Lambda function to read from the SQS queue and to update the Aurora database.

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  Explanation

  The solution must handle variable sales volumes, preserve transaction information, and store data in an Amazon Aurora database with minimal operational overhead. UsingAPI Gateway, AWS Lambda, and SQSis the best option because it provides scalability, reliability, and resilience.

  Why Option A is Correct:

  API Gateway: Serves as an entry point for transaction data in a serverless, scalable manner.

  AWS Lambda: Processes the transactions and sends them to Amazon SQS for queuing.

  Amazon SQS: Buffers the transaction data, ensuring durability and resilience against spikes in transaction volume.

  Second Lambda Function: Processes messages from the SQS queue and updates the Aurora database, decoupling the workflow for better scalability.

  Dead-Letter Queue (DLQ): Ensures failed transactions are logged for later debugging or reprocessing.

  Why other options are not correct:

  Option B: Using an ALB with ECS on EC2 introduces operational overhead, such as managing EC2 instances and scaling ECS tasks.Not cost-effective.

  Option C: EKS is highly operationally intensive and requires Kubernetes cluster management, which is unnecessary for this use case.Too complex.

  Option D: Amazon Data Firehose and DMS are not designed for real-time transactional workflows. They are better suited for data analytics pipelines.Not suitable.

  References:

  Amazon API Gateway:AWS Documentation - API Gateway

  AWS Lambda:AWS Documentation - Lambda

  Amazon SQS:AWS Documentation - SQS

  Amazon Aurora:AWS Documentation - Aurora

• Question 518:

  A company is using AWS Key Management Service (AWS KMS) keys to encrypt AWS Lambda environment variables.

  A solutions architect needs to ensure that the required permissions are in place to decrypt and use the environment variables.

  Which steps must the solutions architect take to implement the correct permissions? (Choose two.)

  A. Add AWS KMS permissions in the Lambda resource policy.
  B. Add AWS KMS permissions in the Lambda execution role.
  C. Add AWS KMS permissions in the Lambda function policy.
  D. Allow the Lambda execution role in the AWS KMS key policy.
  E. Allow the Lambda resource policy in the AWS KMS key policy.
  B. Add AWS KMS permissions in the Lambda execution role.
  D. Allow the Lambda execution role in the AWS KMS key policy.

• Question 519:

  A company wants to migrate applications from its on-premises servers to AWS. As a first step, the company is modifying and migrating a non-critical application to a single Amazon EC2 instance. The application will store information in an Amazon S3 bucket. The company needs to follow security best practices when deploying the application on AWS.

  Which approach should the company take to allow the application to interact with Amazon S3?

  A. Store the files in an Amazon S3 bucket. Use the S3 Glacier Instant Retrieval storage class. Create an S3 Lifecycle policy to transition the files to the S3 Glacier Deep Archive storage class after 1 year.
  B. Store the files in an Amazon S3 bucket. Use the S3 Standard storage class. Create an S3 Lifecycle policy to transition the files to the S3 Glacier Flexible Retrieval storage class after 1 year.
  C. Store the files on an Amazon Elastic Block Store (Amazon EBS) volume. Use Amazon Data Lifecycle Manager to create snapshots of the EBS volumes and to store those snapshots in Amazon S3.
  D. Store the files on an Amazon Elastic File System (Amazon EFS) mount. Configure EFS lifecycle management to transition the files to the EFS Standard-Infrequent Access (Standard-IA) storage class after 1 year.
  A. Store the files in an Amazon S3 bucket. Use the S3 Glacier Instant Retrieval storage class. Create an S3 Lifecycle policy to transition the files to the S3 Glacier Deep Archive storage class after 1 year.

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  Explanation

  The company's requirements are:

  Store gigabytes of rarely accessed files daily.

  Files must be available within minutes during the first year.

  Retain files for 7 years cost-effectively. The S3 Glacier.

  Instant Retrieval storage class provides low-cost, long-term storage for data accessed occasionally, with millisecond retrieval time. This fits the requirement for availability within minutes during the first year. After one year, transitioning files to S3 Glacier Deep Archive (the lowest cost S3 storage class) with longer retrieval times is cost-effective for data retention over 7 years.

  Option B uses S3 Standard and Glacier Flexible Retrieval, which is higher cost during the first year and slower retrieval times.

  Option C is less cost-efficient because EBS volumes are expensive for rarely accessed data and snapshots incur additional costs.

  Option D uses EFS, which is designed for low-latency file storage but is more expensive than S3 Glacier classes for long-term archival.

  References:

  Amazon S3 Storage Classes (https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-class-intro.html)

  S3 Glacier. Instant Retrieval (https://aws.amazon.com/s3/storage-classes/glacier-instant-retrieval/)

  Amazon S3 Lifecycle Management (https://docs.aws.amazon.com/AmazonS3/latest/userguide/lifecycle-configuration-examples.html)

  AWS Well-Architected Framework -- Cost Optimization Pillar (https://d1.awsstatic.com/whitepapers/architecture/AWS_Well-Architected_Framework.pdf)

• Question 520:

  A gaming company uses Amazon DynamoDB to store user information such as geographic location, player data, and leaderboards. The company needs to configure continuous backups to an Amazon S3 bucket with a minimal amount of coding. The backups must not affect availability of the application and must not affect the read capacity units (RCUs) that are defined for the table.

  Which solution meets these requirements?

  A. Use an Amazon EMR cluster. Create an Apache Hive job to back up the data to Amazon S3.
  B. Export the data directly from DynamoDB to Amazon S3 with continuous backups. Turn on point-in-time recovery for the table.
  C. Configure Amazon DynamoDB Streams. Create an AWS Lambda function to consume the stream and export the data to an Amazon S3 bucket.
  D. Create an AWS Lambda function to export the data from the database tables to Amazon S3 on a regular basis. Turn on point-in-time recovery for the table.
  B. Export the data directly from DynamoDB to Amazon S3 with continuous backups. Turn on point-in-time recovery for the table.