Amazon SAA-C03 Online Practice Questions and Exam Preparation

Amazon SAA-C03 Online Questions & Answers

• Question 1511:

  A company hosts a website analytics application on a single Amazon EC2 On-Demand Instance. The analytics software is written in PHP and uses a MySQL database. The analytics software, the web server that provides PHP, and the database server are all hosted on the EC2 instance. The application is showing signs of performance degradation during busy times and is presenting 5xx errors. The company needs to make the application scale seamlessly.

  Which solution will meet these requirements MOST cost-effectively?

  A. Migrate the database to an Amazon RDS for MySQL DB instance. Create an AMI of the web application. Use the AMI to launch a second EC2 On-Demand Instance. Use an Application Load Balancer to distribute the load to each EC2 instance.
  B. Migrate the database to an Amazon RDS for MySQL DB instance. Create an AMI of the web application. Use the AMI to launch a second EC2 On-Demand Instance. Use Amazon Route 53 weighted routing to distribute the load across the two EC2 instances.
  C. Migrate the database to an Amazon Aurora MySQL DB instance. Create an AWS Lambda function to stop the EC2 instance and change the instance type. Create an Amazon CloudWatch alarm to invoke the Lambda function when CPU utilization surpasses 75%.
  D. Migrate the database to an Amazon Aurora MySQL DB instance. Create an AMI of the web application. Apply the AMI to a launch template. Create an Auto Scaling group with the launch template Configure the launch template to use a Spot Fleet. Attach an Application Load Balancer to the Auto Scaling group.
  D. Migrate the database to an Amazon Aurora MySQL DB instance. Create an AMI of the web application. Apply the AMI to a launch template. Create an Auto Scaling group with the launch template Configure the launch template to use a Spot Fleet. Attach an Application Load Balancer to the Auto Scaling group.

• Question 1512:

  A company hosts a web application in a VPC on AWS. A public Application Load Balancer (ALB) forwards connections from the internet to an Auto Scaling group of Amazon EC2 instances. The Auto Scaling group runs in private subnets across four Availability Zones. The company stores data in an Amazon S3 bucket in the same Region. The EC2 instances use NAT gateways in each Availability Zone for outbound internet connectivity.

  The company wants to optimize costs for its AWS architecture.

  Which solution will meet this requirement?

  A. Reconfigure the Auto Scaling group and the ALB to use two Availability Zones instead of four. Do not change the desired count or scaling metrics for the Auto Scaling group to maintain application availability.
  B. Create a new, smaller VPC that still has sufficient IP address availability to run the application. Redeploy the application stack in the new VPC. Delete the existing VPC and its resources.
  C. Deploy an S3 gateway endpoint to the VPC. Configure the EC2 instances to access the S3 bucket through the S3 gateway endpoint.
  D. Deploy an S3 interface endpoint to the VPC. Configure the EC2 instances to access the S3 bucket through the S3 interface endpoint.
  C. Deploy an S3 gateway endpoint to the VPC. Configure the EC2 instances to access the S3 bucket through the S3 gateway endpoint.

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  Explanation

  Using S3 gateway endpoints allows private and cost-free access to S3 without routing traffic through a NAT gateway. NAT gateway traffic incurs charges, especially when used across multiple Availability Zones.

  By using an S3 gateway endpoint, EC2 instances in private subnets can access S3 directly without needing internet access, reducing both data transfer and NAT gateway costs.

  Interface endpoints are more expensive and typically used for services like API Gateway or Systems Manager.

• Question 1513:

  A company has released a new version of its production application. The company's workload uses Amazon EC2, AWS Lambda, AWS Fargate, and Amazon SageMaker.

  The company wants to cost optimize the workload now that usage is at a steady state. The company wants to cover the most services with the fewest savings plans.

  Which combination of savings plans will meet these requirements? (Choose two.)

  A. Purchase an EC2 Instance Savings Plan for Amazon EC2 and SageMaker.
  B. Purchase a Compute Savings Plan for Amazon EC2, Lambda, and SageMaker.
  C. Purchase a SageMaker Savings Plan.
  D. Purchase a Compute Savings Plan for Lambda, Fargate, and Amazon EC2.
  E. Purchase an EC2 Instance Savings Plan for Amazon EC2 and Fargate.
  C. Purchase a SageMaker Savings Plan.
  D. Purchase a Compute Savings Plan for Lambda, Fargate, and Amazon EC2.

• Question 1514:

  A solutions architect is investigating compute options for a critical analytics application. The application uses long-running processes to prepare and aggregate data. The processes cannot be interrupted. The application has a known baseline load. The application needs to handle occasional usage surges.

  Which solution will meet these requirements MOST cost-effectively?

  A. Create an Amazon EC2 Auto Scaling group. Set the Min capacity and Desired capacity parameters to the number of instances required to handle the baseline load. Purchase Reserved Instances for the Auto Scaling group.
  B. Create an Amazon EC2 Auto Scaling group. Set the Min capacity, Max capacity, and Desired capacity parameters to the number of instances required to handle the baseline load. Use On-Demand Instances to address occasional usage surges.
  C. Create an Amazon EC2 Auto Scaling group. Set the Min capacity and Desired capacity parameters to the number of instances required to handle the baseline load. Purchase Reserved Instances for the Auto Scaling group. Use the OnDemandPercentageAboveBaseCapacity parameter to configure the launch template to launch Spot Instances.
  D. Re-architect the application to use AWS Lambda functions instead of Amazon EC2 instances. Purchase a one-year Compute Savings Plan to reduce the cost of Lambda usage.
  C. Create an Amazon EC2 Auto Scaling group. Set the Min capacity and Desired capacity parameters to the number of instances required to handle the baseline load. Purchase Reserved Instances for the Auto Scaling group. Use the OnDemandPercentageAboveBaseCapacity parameter to configure the launch template to launch Spot Instances.

• Question 1515:

  A company runs an application in a private subnet behind an Application Load Balancer (ALB) in a VPC.

  The VPC has a NAT gateway and an internet gateway. The application calls the Amazon S3 API to store objects. According to the company's security policy, traffic from the application must not travel across the internet.

  Which solution will meet these requirements MOST cost-effectively?

  A. Configure an S3 interface endpoint. Create a security group that allows outbound traffic to Amazon S3.
  B. Configure an S3 gateway endpoint. Update the VPC route table to use the endpoint.
  C. Configure an S3 bucket policy to allow traffic from the Elastic IP address that is assigned to the NAT gateway.
  D. Create a second NAT gateway in the same subnet where the legacy application is deployed. Update the VPC route table to use the second NAT gateway.
  B. Configure an S3 gateway endpoint. Update the VPC route table to use the endpoint.

• Question 1516:

  A company's packaged application dynamically creates and returns single-use text files in response to user requests. The company is using Amazon CloudFront for distribution, but wants to further reduce data transfer costs. The company cannot modify the application's source code.

  What should a solutions architect do to reduce costs?

  A. Use Lambda@Edge to compress the files as they are sent to users.
  B. Enable Amazon S3 Transfer Acceleration to reduce the response times.
  C. Enable caching on the CloudFront distribution to store generated files at the edge.
  D. Use Amazon S3 multipart uploads to move the files to Amazon S3 before returning them to users.
  A. Use Lambda@Edge to compress the files as they are sent to users.

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  Explanation

  Lambda@Edge allows you to run functions at CloudFront edge locations, enabling modifications to content before it's delivered to users, including compression -- which directly reduces data transfer cost.

  "Lambda@Edge can be used to compress or modify your content before delivering it to end users, which reduces the amount of data transferred."

  -- Lambda@Edge Use Cases

  Since code modifications aren't allowed, using Lambda@Edge is a non-invasive way to:

  Compress responses.

  Reduce transfer size = lower CloudFront cost.

  Incorrect Options:

  Option B: S3 Transfer Acceleration improves speed, not cost.

  Option C: Caching doesn't help if content is always unique (single-use).

  Option D: Multipart uploads help with large file uploads, not transfers.

  References:

  Lambda@Edge for Content Compression CloudFront Pricing and Cost Optimization

• Question 1517:

  A company is planning to store data on Amazon RDS DB instances. The company must encrypt the data at rest.

  What should a solutions architect do to meet this requirement?

  A. Create a key in AWS Key Management Service (AWS KMS). Enable encryption for the DB instances.
  B. Create an encryption key. Store the key in AWS Secrets Manager. Use the key to encrypt the DB instances.
  C. Generate a certificate in AWS Certificate Manager (ACM). Enable SSL/TLS on the DB instances by using the certificate.
  D. Generate a certificate in AWS Identity and Access Management (IAM). Enable SSL/TLS on the DB instances by using the certificate.
  A. Create a key in AWS Key Management Service (AWS KMS). Enable encryption for the DB instances.

• Question 1518:

  A telemarketing company is designing its customer call center functionality on AWS. The company needs a solution that provides multiple speaker recognition and generates transcript files. The company wants to query the transcript files to analyze the business patterns.

  Which solution will meet these requirements?

  A. Use Amazon Rekognition for multiple speaker recognition. Store the transcript files in Amazon S3. Use machine learning (ML) models to analyze the transcript files.
  B. Use Amazon Transcribe for multiple speaker recognition. Use Amazon Athena to analyze the transcript files.
  C. Use Amazon Translate for multiple speaker recognition. Store the transcript files in Amazon Redshift. Use SQL queries to analyze the transcript files.
  D. Use Amazon Rekognition for multiple speaker recognition. Store the transcript files in Amazon S3. Use Amazon Textract to analyze the transcript files.
  B. Use Amazon Transcribe for multiple speaker recognition. Use Amazon Athena to analyze the transcript files.

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  Explanation

  Amazon Transcribe supports automatic speech recognition (ASR) with speaker diarization (i.e., multiple speaker identification). The transcripts can be stored in Amazon S3 and queried using Amazon Athena, which provides a serverless, pay-as-you-go interactive querying model.

  References:

  AWS Documentation?Amazon Transcribe + Amazon Athena for Speech-to-Insights Pipelines

• Question 1519:

  A company is designing an application on AWS that processes sensitive data. The application stores and processes financial data for multiple customers. To meet compliance requirements, the data for each customer must be encrypted separately at rest by using a secure, centralized key management solution.

  The company wants to use AWS Key Management Service (AWS KMS) to implement encryption.

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

  A. Generate a unique encryption key for each customer. Store the keys in an Amazon S3 bucket. Enable server-side encryption.
  B. Deploy a hardware security appliance in the AWS environment that securely stores customer-provided encryption keys. Integrate the security appliance with AWS KMS to encrypt the sensitive data in the application.
  C. Create a single AWS KMS key to encrypt all sensitive data across the application.
  D. Create separate AWS KMS keys for each customer's data that have granular access control and logging enabled.
  D. Create separate AWS KMS keys for each customer's data that have granular access control and logging enabled.

• Question 1520:

  A company serves a dynamic website from a fleet of Amazon EC2 instances behind an Application Load Balancer (ALB). The website needs to support multiple languages to serve customers around the world.

  The website's architecture is running in the us-west-1 Region and is exhibiting high request latency for users that are located in other parts of the world. The website needs to serve requests quickly and efficiently regardless of a user's location. However, the company does not want to recreate the existing architecture across multiple Regions.

  What should a solutions architect do to meet these requirements?

  A. Replace the existing architecture with a website that is served from an Amazon S3 bucket. Configure an Amazon CloudFront distribution with the S3 bucket as the origin. Set the cache behavior settings to cache based on the Accept-Language request header.
  B. Configure an Amazon CloudFront distribution with the ALB as the origin. Set the cache behavior settings to cache based on the Accept-Language request header.
  C. Create an Amazon API Gateway API that is integrated with the ALB. Configure the API to use the HTTP integration type. Set up an API Gateway stage to enable the API cache based on the Accept-Language request header.
  D. Launch an EC2 instance in each additional Region and configure NGINX to act as a cache server for that Region. Put all the EC2 instances and the ALB behind an Amazon Route 53 record set with a geolocation routing policy.
  B. Configure an Amazon CloudFront distribution with the ALB as the origin. Set the cache behavior settings to cache based on the Accept-Language request header.