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Amazon SAP-C01 Exam
AWS Certified Solutions Architect - Professional SAP-C02 (Page 20 )

Updated On: 1-Feb-2026
Page 20 of 107
PDF with 533 Questions

A company is running an application in the AWS Cloud. The application collects and stores a large amount of unstructured data in an Amazon S3 bucket. The S3 bucket contains several terabytes of data and uses the S3 Standard storage class. The data increases in size by several gigabytes every day.

The company needs to query and analyze the data. The company does not access data that is more than 1 year old. However, the company must retain all the data indefinitely for compliance reasons.

Which solution will meet these requirements MOST cost-effectively?

  1. Use S3 Select to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.
  2. Use Amazon Redshift Spectrum to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old 10 S3 Glacier Deep Archive.
  3. Use an AWS Glue Data Catalog and Amazon Athena to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.
  4. Use Amazon Redshift Spectrum to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Intelligent-Tiering.

Answer(s): C

Explanation:

C) Use an AWS Glue Data Catalog and Amazon Athena to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.

Using AWS Glue Data Catalog with Amazon Athena allows for cost-effective querying of large amounts of unstructured data directly from S3 without needing to move the data to a separate database. Since the data older than 1 year is rarely accessed but must be retained indefinitely, transitioning it to S3 Glacier Deep Archive using an S3 Lifecycle policy provides a highly cost-effective long-term storage solution. This approach minimizes costs while maintaining compliance and query capabilities for recent data.



A video processing company wants to build a machine learning (ML) model by using 600 TB of compressed data that is stored as thousands of files in the company's on-premises network attached storage system. The company does not have the necessary compute resources on premises for ML experiments and wants to use AWS.

The company needs to complete the data transfer to AWS within 3 weeks. The data transfer will be a one-time transfer. The data must be encrypted in transit. The measured upload speed of the company's internet connection is 100 Mbps. and multiple departments share the connection.

Which solution will meet these requirements MOST cost-effectively?

  1. Order several AWS Snowball Edge Storage Optimized devices by using the AWS Management Console. Configure the devices with a destination S3 bucket. Copy the data to the devices. Ship the devices back to AWS.
  2. Set up a 10 Gbps AWS Direct Connect connection between the company location and the nearest AWS Region. Transfer the data over a VPN connection into the Region to store the data in Amazon S3.
  3. Create a VPN connection between the on-premises network attached storage and the nearest AWS Region. Transfer the data over the VPN connection.
  4. Deploy an AWS Storage Gateway file gateway on premises. Configure the file gateway with a destination S3 bucket. Copy the data to the file gateway.

Answer(s): A

Explanation:

A) Order several AWS Snowball Edge Storage Optimized devices by using the AWS Management Console. Configure the devices with a destination S3 bucket. Copy the data to the devices. Ship the devices back to AWS.

Using AWS Snowball Edge is the most cost-effective and efficient solution for transferring 600 TB of data within 3 weeks. Given the limited internet speed (100 Mbps) and shared connection, transferring the data over the internet would take far too long. Snowball Edge devices offer a secure, offline method of transferring large datasets, with encryption in transit, and can handle the scale of data required within the time frame.



A company has migrated Its forms-processing application to AWS. When users interact with the application, they upload scanned forms as files through a web application. A database stores user metadata and references to files that are stored in Amazon S3. The web application runs on Amazon EC2 instances and an Amazon RDS for PostgreSQL database.

When forms are uploaded, the application sends notifications to a team through Amazon Simple Notification Service (Amazon SNS). A team member then logs in and processes each form. The team member performs data validation on the form and extracts relevant data before entering the information into another system that uses an API.

A solutions architect needs to automate the manual processing of the forms. The solution must provide accurate form extraction. minimize time to market, and minimize tong-term operational overhead.

Which solution will meet these requirements?

  1. Develop custom libraries to perform optical character recognition (OCR) on the forms. Deploy the libraries to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster as an application tier. Use this tier to process the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data into an Amazon DynamoDB table. Submit the data to the target system's APL. Host the new application tier on EC2 instances.
  2. Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use artificial intelligence and machine learning (AI/ML) models that are trained and hosted on an EC2 instance to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.
  3. Host a new application tier on EC2 instances. Use this tier to call endpoints that host artificial intelligence and machine teaming (AI/ML) models that are trained and hosted in Amazon SageMaker to perform optical character recognition (OCR) on the forms. Store the output in Amazon ElastiCache. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.
  4. Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use Amazon Textract and Amazon Comprehend to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.

Answer(s): D

Explanation:

D) Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use Amazon Textract and Amazon Comprehend to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.

This solution leverages Amazon Textract for OCR and Amazon Comprehend for extracting relevant information, both of which are fully managed services that reduce operational overhead and ensure high accuracy. Using AWS Step Functions and AWS Lambda provides a serverless, low-maintenance, and scalable solution for automating the forms processing workflow. This solution minimizes time to market by utilizing AWS AI/ML services, which don't require custom development, and provides automation with minimal long-term operational overhead.



A company is refactoring its on-premises order-processing platform in the AWS Cloud. The platform includes a web front end that is hosted on a fleet of VMs, RabbitMQ to connect the front end to the backend, and a Kubernetes cluster to run a containerized backend system to process the orders. The company does not want to make any major changes to the application.

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

  1. Create an AMI of the web server VM. Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer. Set up Amazon MQ to replace the on-premises messaging queue. Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend.
  2. Create a custom AWS Lambda runtime to mimic the web server environment. Create an Amazon API Gateway API to replace the front-end web servers. Set up Amazon MQ to replace the on-premises messaging queue. Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend.
  3. Create an AMI of the web server VM. Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer. Set up Amazon MQ to replace the on-premises messaging queue. Install Kubernetes on a fleet of different EC2 instances to host the order-processing backend.
  4. Create an AMI of the web server VM. Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer. Set up an Amazon Simple Queue Service (Amazon SQS) queue to replace the on-premises messaging queue. Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend.

Answer(s): A

Explanation:

A) Create an AMI of the web server VM. Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer. Set up Amazon MQ to replace the on-premises messaging queue. Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend.

This solution has the least operational overhead because it reuses the existing architecture (web front end, messaging queue, Kubernetes-based backend) with minimal changes while migrating to AWS. It leverages Amazon MQ as a managed service for RabbitMQ, reducing management overhead for the messaging layer. Amazon EKS is used to manage the containerized backend, which is similar to the on-premises Kubernetes setup, ensuring compatibility. Using EC2 Auto Scaling and an Application Load Balancer provides scalability for the web front end without requiring major changes.



A company has developed a web application. The company is hosting the application on a group of Amazon EC2 instances behind an Application Load Balancer. The company wants to improve the security posture of the application and plans to use AWS WAF web ACLs. The solution must not adversely affect legitimate traffic to the application.

How should a solutions architect configure the web ACLs to meet these requirements?

  1. Set the action of the web ACL rules to Count. Enable AWS WAF logging. Analyze the requests for false positives. Modify the rules to avoid any false positive. Over time, change the action of the web ACL rules from Count to Block.
  2. Use only rate-based rules in the web ACLs, and set the throttle limit as high as possible. Temporarily block all requests that exceed the limit. Define nested rules to narrow the scope of the rate tracking.
  3. Set the action of the web ACL rules to Block. Use only AWS managed rule groups in the web ACLs. Evaluate the rule groups by using Amazon CloudWatch metrics with AWS WAF sampled requests or AWS WAF logs.
  4. Use only custom rule groups in the web ACLs, and set the action to Allow. Enable AWS WAF logging. Analyze the requests for false positives. Modify the rules to avoid any false positive. Over time, change the action of the web ACL rules from Allow to Block.

Answer(s): A

Explanation:

A) Set the action of the web ACL rules to Count. Enable AWS WAF logging. Analyze the requests for false positives. Modify the rules to avoid any false positive. Over time, change the action of the web ACL rules from Count to Block.

Starting with the Count action allows you to evaluate traffic without blocking legitimate requests. By enabling AWS WAF logging, you can analyze the traffic patterns and false positives to fine-tune the rules before changing them to Block. This approach ensures that the security posture is improved without affecting legitimate traffic, gradually increasing protection over time.



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