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Amazon Web Services SAP-C02 Exam With Confidence Using Practice Dumps

Exam Code:
SAP-C02
Exam Name:
AWS Certified Solutions Architect - Professional
Certification:
AWS Certified Professional
Vendor:
Amazon Web Services
Questions:
691
Last Updated:
Nov 12, 2026
Exam Status:
Stable
Amazon Web Services SAP-C02

SAP-C02: AWS Certified Professional Exam 2025 Study Guide Pdf and Test Engine

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AWS Certified Solutions Architect - Professional Questions and Answers

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Question 1

A company stores all its infrastructure code in a central Git repository that is hosted on GitHub Enterprise. The company wants to implement a process that submits every infrastructure update by using a pull request. Each pull request must automatically receive an AWS CloudFormation change set. After the pull request is approved, CloudFormation must apply the updates directly. The process must not require custom pipelines or manual template processing.

Which solution will meet these requirements?

Options:

A.

Use CloudFormation to invoke an AWS CodePipeline action. Configure the CodePipeline action to validate the template and publish the validation results as pull request comments.

B.

Use CloudFormation Git sync to link CloudFormation with the repository. Configure automatic change set creation for pull request updates. Configure CloudFormation to post the change set summary and apply the update when the pull request is approved and merged.

C.

Use CloudFormation drift detection. Create an Amazon EventBridge rule that reacts to pull request updates. Configure the EventBridge rule to produce drift comparison results and send the results as comments before the stack is updated.

D.

Use CloudFormation Lambda Hooks. Configure Lambda Hooks to evaluate the updated template and write the planned changes into the pull request when a stack update is requested.

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Question 2

A company is running an application on several Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer. The load on the application varies throughout the day, and EC2 instances are scaled in and out on a regular basis. Log files from the EC2 instances are copied to a central Amazon S3 bucket every 15 minutes. The security team discovers that log files are missing from some of the terminated EC2 instances.

Which set of actions will ensure that log files are copied to the central S3 bucket from the terminated EC2 instances?

Options:

A.

Create a script to copy log files to Amazon S3, and store the script in a file on the EC2 instance. Create an Auto Scaling lifecycle hook and an Amazon EventBridge (Amazon CloudWatch Events) rule to detect lifecycle events from the Auto Scaling group. Invoke an AWS Lambda function on the autoscaling:EC2_INSTANCE_TERMINATING transition to send ABANDON to the Auto Scaling group to prevent termination, run the script to copy the log files, and

B.

Create an AWS Systems Manager document with a script to copy log files to Amazon S3. Create an Auto Scaling lifecycle hook and an Amazon EventBridge (Amazon CloudWatch Events) rule to detect lifecycle events from the Auto Scaling group. Invoke an AWS Lambda function on the autoscaling:EC2_INSTANCE_TERMINATING transition to call the AWS Systems Manager API SendCommand operation to run the document to copy the log files and send CONTINUE to

C.

Change the log delivery rate to every 5 minutes. Create a script to copy log files to Amazon S3, and add the script to EC2 instance user data. Create an Amazon EventBridge (Amazon CloudWatch Events) rule to detect EC2 instance termination. Invoke an AWS Lambda function from the EventBridge (CloudWatch Events) rule that uses the AWS CLI to run the user-data script to copy the log files and terminate the instance.

D.

Create an AWS Systems Manager document with a script to copy log files to Amazon S3. Create an Auto Scaling lifecycle hook that publishes a message to an Amazon Simple Notification Service (Amazon SNS) topic. From the SNS notification, call the AWS Systems Manager API SendCommand operation to run the document to copy the log files and send ABANDON to the Auto Scaling group to terminate the instance.

Question 3

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 long-term operational overhead.

Which solution will meet these requirements?

Options:

A.

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 API. Host the new application tier on EC2 instan

B.

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

C.

Host a new application tier on EC2 instances. Use this tier to call endpoints that host artificial intelligence and machine learning (Al/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.

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.

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