Amazon SAA-C03 Online Practice Questions and Exam Preparation

Amazon SAA-C03 Online Questions & Answers

• Question 761:

  A company has an Amazon S3 data lake that is governed by AWS Lake Formation. The company wants to create a visualization in Amazon QuickSight by joining the data in the data lake with operational data that is stored in an Amazon Aurora MySQL database. The company wants to enforce column-level authorization so that the company's marketing team can access only a subset of columns in the database.

  Which solution will meet these requirements with the LEAST operational overhead?

  A. Use Amazon EMR to ingest the data directly from the database to the QuickSight SPICE engine. Include only the required columns.
  B. Use AWS Glue Studio to ingest the data from the database to the S3 data lake. Attach an IAM policy to the QuickSight users to enforce column-level access control. Use Amazon S3 as the data source in QuickSight.
  C. Use AWS Glue Elastic Views to create a materialized view for the database in Amazon S3. Create an S3 bucket policy to enforce column-level access control for the QuickSight users. Use Amazon S3 as the data source in QuickSight.
  D. Use a Lake Formation blueprint to ingest the data from the database to the S3 data lake. Use Lake Formation to enforce column-level access control for the QuickSight users. Use Amazon Athena as the data source in QuickSight.
  D. Use a Lake Formation blueprint to ingest the data from the database to the S3 data lake. Use Lake Formation to enforce column-level access control for the QuickSight users. Use Amazon Athena as the data source in QuickSight.

• Question 762:

  A finance company collects streaming data for a real-time search and visualization system. The company wants to migrate to AWS by using a native solution for data ingest, search, and visualization.

  Which solution will meet these requirements?

  A. Use EC2 to ingest/process data to S3 # Athena + Managed Grafana
  B. Use EMR to ingest/process to Redshift # Redshift Spectrum + QuickSight
  C. Use EKS to ingest/process to DynamoDB # CloudWatch Dashboards
  D. Use Kinesis Data Streams # Amazon OpenSearch Service # Amazon QuickSight
  D. Use Kinesis Data Streams # Amazon OpenSearch Service # Amazon QuickSight

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  Explanation

  This is a classic use case for Amazon Kinesis Data Streams + OpenSearch +

  QuickSight:

  Kinesis Data Streams: For real-time ingestion and processing

  OpenSearch Service: For fast full-text search, indexing, and analysis

  QuickSight: For rich dashboard visualizations

  This stack is fully managed, scalable, and native to AWS, minimizing operational overhead.

  References:

  Kinesis + OpenSearch architecture Amazon QuickSight

• Question 763:

  A company is building a solution to provide customers with an API that accesses financial data. The API backend needs to compute tax data for each request. The company anticipates greater demand to access the data during the last 3 months of each year.

  A solutions architect needs to design a scalable solution that can meet the regular demand and the peak demand at the end of each year.

  Which solution will meet these requirements?

  A. Host the API on an Amazon EC2 instance that runs third-party software. Configure the EC2 instance to perform tax computations.
  B. Deploy an Amazon API Gateway REST API. Create an AWS Lambda function to perform tax computations. Integrate the Lambda function with the REST API.
  C. Create an Application Load Balancer (ALB) in front of two Amazon EC2 instances. Configure the EC2 instances to perform tax computations.
  D. Deploy an Amazon API Gateway REST API. Configure an Amazon EC2 instance to perform tax computations. Integrate the EC2 instance with the REST API.
  B. Deploy an Amazon API Gateway REST API. Create an AWS Lambda function to perform tax computations. Integrate the Lambda function with the REST API.

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  Explanation

  For bursty and seasonal workloads, AWS recommends serverless architectures using Amazon API Gateway + AWS Lambda:

  API Gateway exposes a fully managed, scalable API endpoint.

  Lambda provides automatic scaling based on request volume, with no need to provision or manage servers.

  This is ideal for workloads with variable or seasonal demand, such as end-of-year tax computation bursts.

  This combination minimizes operational overhead (no OS patching, no capacity planning) while scaling seamlessly to meet peak demand. Options A, C, and D rely on EC2 instances. They require capacity planning, scaling configuration, and ongoing management. They do not scale as effortlessly as Lambda for unpredictable or highly seasonal workloads.

• Question 764:

  A company's SAP application has a backend SQL Server database in an on-premises environment. The company wants to migrate its on-premises application and database server to AWS. The company needs an instance type that meets the high demands of its SAP database. On-premises performance data shows that both the SAP application and the database have high memory utilization.

  Which solution will meet these requirements?

  A. Use the compute optimized instance family for the application. Use the memory optimized instance family for the database.
  B. Use the storage optimized instance family for both the application and the database.
  C. Use the memory optimized instance family for both the application and the database.
  D. Use the high performance computing (HPC) optimized instance family for the application. Use the memory optimized instance family for the database.
  C. Use the memory optimized instance family for both the application and the database.

• Question 765:

  A company stores its data on premises. The amount of data is growing beyond the company's available capacity.

  The company wants to migrate its data from the on-premises location to an Amazon S3 bucket. The company needs a solution that will automatically validate the integrity of the data after the transfer.

  Which solution will meet these requirements?

  A. Order an AWS Snowball Edge device. Configure the Snowball Edge device to perform the online data transfer to an S3 bucket
  B. Deploy an AWS DataSync agent on premises. Configure the DataSync agent to perform the online data transfer to an S3 bucket.
  C. Create an Amazon S3 File Gateway on premises Configure the S3 File Gateway to perform the online data transfer to an S3 bucket
  D. Configure an accelerator in Amazon S3 Transfer Acceleration on premises. Configure the accelerator to perform the online data transfer to an S3 bucket.
  B. Deploy an AWS DataSync agent on premises. Configure the DataSync agent to perform the online data transfer to an S3 bucket.

• Question 766:

  A company has a serverless web application that is comprised of AWS Lambda functions. The application experiences spikes in traffic that cause increased latency because of cold starts. The company wants to improve the application's ability to handle traffic spikes and to minimize latency. The solution must optimize costs during periods when traffic is low.

  Which solution will meet these requirements?

  A. Configure provisioned concurrency for the Lambda functions. Use AWS Application Auto Scaling to adjust the provisioned concurrency.
  B. Launch Amazon EC2 instances in an Auto Scaling group. Add a scheduled scaling policy to launch additional EC2 instances during peak traffic periods.
  C. Configure provisioned concurrency for the Lambda functions. Set a fixed concurrency level to handle the maximum expected traffic.
  D. Create a recurring schedule in Amazon EventBridge Scheduler. Use the schedule to invoke the Lambda functions periodically to warm the functions.
  A. Configure provisioned concurrency for the Lambda functions. Use AWS Application Auto Scaling to adjust the provisioned concurrency.

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  Explanation

  Key Requirements:

  Handle traffic spikes efficiently and reduce latency caused by cold starts.

  Optimize costs during low traffic periods.

  Analysis of Options:

  Option A:

  Provisioned Concurrency:Reduces cold start latency by pre-warming Lambda environments for the required number of concurrent executions.

  AWS Application Auto Scaling:Automatically adjusts provisioned concurrency based on demand, ensuring cost optimization by scaling down during low traffic.

  Correct Approach:Provides a balance between performance during traffic spikes and cost optimization during idle periods.

  Option B:

  Using EC2 instances with Auto Scaling introduces unnecessary complexity for a serverless architecture. It requires additional management and does not address the issue of cold starts for Lambda.

  Incorrect Approach:Contradicts the serverless design philosophy and increases operational overhead.

  Option C:

  Setting a fixed concurrency level ensures performance during spikes but does not optimize costs during low traffic. This approach would maintain provisioned instances unnecessarily.

  Incorrect Approach:Lacks cost optimization.

  Option D:

  Using EventBridge Scheduler for periodic invocations may reduce cold starts but does not dynamically scale based on traffic demand. It also leads to unnecessary invocations during idle times.

  Incorrect Approach:Suboptimal for high traffic fluctuations and cost control.

  References:

  AWS Lambda Provisioned Concurrency

  AWS Application Auto Scaling with Lambda

• Question 767:

  A company wants to use an event-driven programming model with AWS Lambda. The company wants to reduce startup latency for Lambda functions that run on Java 11. The company does not have strict latency requirements for the applications. The company wants to reduce cold starts and outlier latencies when a function scales up.

  Which solution will meet these requirements MOST cost-effectively?

  A. Configure Lambda provisioned concurrency.
  B. Increase the timeout of the Lambda functions.
  C. Increase the memory of the Lambda functions.
  D. Configure Lambda SnapStart.
  D. Configure Lambda SnapStart.

• Question 768:

  A company hosts a monolithic web application on an Amazon EC2 instance. Application users have recently reported poor performance at specific times. Analysis of Amazon CloudWatch metrics shows that CPU utilization is 100% during the periods of poor performance.

  The company wants to resolve this performance issue and improve application availability.

  Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

  A. Use AWS Compute Optimizer to obtain a recommendation for an instance type to scale vertically.
  B. Create an Amazon Machine Image (AMI) from the web server. Reference the AMI in a new launch template.
  C. Create an Auto Scaling group and an Application Load Balancer to scale vertically.
  D. Use AWS Compute Optimizer to obtain a recommendation for an instance type to scale horizontally.
  E. Create an Auto Scaling group and an Application Load Balancer to scale horizontally.
  A. Use AWS Compute Optimizer to obtain a recommendation for an instance type to scale vertically.
  E. Create an Auto Scaling group and an Application Load Balancer to scale horizontally.

• Question 769:

  A company wants to migrate an on-premises video processing application to AWS. Processing times range from 5?0 minutes. The application must run multiple jobs in parallel. The application processes videos that users upload to an Amazon S3 bucket.

  Which solution will meet these requirements with the LEAST operational overhead?

  A. Configure the S3 bucket to send S3 event notifications to an Amazon SQS standard queue. Deploy the application on an Amazon ECS cluster. Configure automatic scaling for AWS Fargate tasks based on the SQS queue size.
  B. Configure the S3 bucket to send S3 event notifications to an Amazon SQS FIFO queue. Deploy the application on Amazon EC2 instances. Create an Auto Scaling group to scale based on the SQS queue size.
  C. Configure the S3 bucket to send S3 event notifications to an Amazon SQS standard queue. Deploy the application as an AWS Lambda function. Configure the Lambda function to poll the SQS queue.
  D. Configure the S3 bucket to send S3 event notifications to an Amazon SNS topic. Deploy the application as an AWS Lambda function. Configure the SNS topic to invoke the Lambda function.
  A. Configure the S3 bucket to send S3 event notifications to an Amazon SQS standard queue. Deploy the application on an Amazon ECS cluster. Configure automatic scaling for AWS Fargate tasks based on the SQS queue size.

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  Explanation

  The correct answer is A because the application processes videos for 5?0 minutes, must run multiple jobs in parallel, and should have the least operational overhead Amazon ECS with AWS Fargate. is a managed container solution that removes the need to provision and manage EC2 instances while still supporting longer-running parallel jobs. Using Amazon SQS standard queues decouples the upload event from processing and provides a scalable buffer for incoming work.

  With this design, Amazon S3 sends object-created notifications to the SQS queue whenever users upload videos. ECS services or tasks on Fargate can then consume messages from the queue and process videos independently. Auto scaling based on SQS queue depth ensures the system increases task count when more videos arrive and decreases capacity when demand drops. This provides elasticity and efficient parallel processing with minimal infrastructure management.

  Option B is incorrect because EC2-based scaling introduces more operational overhead than Fargate.

  Option C is incorrect because AWS Lambda has a maximum execution duration and is not appropriate for jobs that can run up to 30 minutes.

  Option D is also incorrect for the same reason; Lambda is not the best fit for this processing duration, and SNS does not provide the same durable queued work pattern as SQS for controlled parallel processing.

  AWS best practices recommend using event-driven queues with containerized workers for medium-duration processing jobs that need concurrency and minimal management. Therefore, S3 + SQS + ECS on Fargate with queue-based scaling is the best solution.

• Question 770:

  A company wants to enhance its ecommerce order-processing application that is deployed on AWS. The application must process each order exactly once without affecting the customer experience during unpredictable traffic surges.

  Which solution will meet these requirements?

  A. Create an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Put all the orders in the SQS queue. Configure an AWS Lambda function as the target to process the orders.
  B. Create an Amazon Simple Notification Service (Amazon SNS) standard topic. Publish all the orders to the SNS standard topic. Configure the application as a notification target.
  C. Create a flow by using Amazon AppFlow. Send the orders to the flow. Configure an AWS Lambda function as the target to process the orders.
  D. Configure AWS X-Ray in the application to track the order requests. Configure the application to process the orders by pulling the orders from Amazon CloudWatch.
  A. Create an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Put all the orders in the SQS queue. Configure an AWS Lambda function as the target to process the orders.