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

Exam Code:
DOP-C02
Exam Name:
AWS Certified DevOps Engineer - Professional
Certification:
AWS Certified Professional
Vendor:
Amazon Web Services
Questions:
392
Last Updated:
Dec 5, 2025
Exam Status:
Stable
Amazon Web Services DOP-C02

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

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AWS Certified DevOps Engineer - Professional Questions and Answers

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Question 1

A company is running a custom-built application that processes records. All the components run on Amazon EC2 instances that run in an Auto Scaling group. Each record's processing is a multistep sequential action that is compute-intensive. Each step is always completed in 5 minutes or less.

A limitation of the current system is that if any steps fail, the application has to reprocess the record from the beginning The company wants to update the architecture so that the application must reprocess only the failed steps.

What is the MOST operationally efficient solution that meets these requirements?

Options:

A.

Create a web application to write records to Amazon S3 Use S3 Event Notifications to publish to an Amazon Simple Notification Service (Amazon SNS) topic Use an EC2 instance to poll Amazon SNS and start processing Save intermediate results to Amazon S3 to pass on to the next step

B.

Perform the processing steps by using logic in the application. Convert the application code to run in a container. Use AWS Fargate to manage the container Instances. Configure the container to invoke itself to pass the state from one step to the next.

C.

Create a web application to pass records to an Amazon Kinesis data stream. Decouple the processing by using the Kinesis data stream and AWS Lambda functions.

D.

Create a web application to pass records to AWS Step Functions. Decouple the processing into Step Functions tasks and AWS Lambda functions.

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Question 2

A company’s web app runs on EC2 Linux instances and needs to monitor custom metrics for API response and DB query latency across instances with least overhead.

Which solution meets this?

Options:

A.

Install CloudWatch agent on instances, configure it to collect custom metrics, and instrument app to send metrics to agent.

B.

Use Amazon Managed Service for Prometheus to scrape metrics, use CloudWatch agent to forward metrics to CloudWatch.

C.

Create Lambda to poll app endpoints and DB, calculate metrics, send to CloudWatch via PutMetricData.

D.

Implement custom logging in app; use CloudWatch Logs Insights to extract and analyze metrics.

Question 3

A company's application uses a fleet of Amazon EC2 On-Demand Instances to analyze and process data. The EC2 instances are in an Auto Scaling group. The Auto Scaling group is a target group for an Application Load Balancer (ALB). The application analyzes critical data that cannot tolerate interruption. The application also analyzes noncritical data that can withstand interruption.

The critical data analysis requires quick scalability in response to real-time application demand. The noncritical data analysis involves memory consumption. A DevOps engineer must implement a solution that reduces scale-out latency for the critical data. The solution also must process the noncritical data.

Which combination of steps will meet these requirements? (Select TWO.)

Options:

A.

For the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the stopped state. Define the warm pool size. Create a new version of the launch template that has detailed monitoring enabled. use Spot Instances.

B.

For the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the stopped state. Define the warm pool size. Create a new version of the launch template that has detailed monitoring enabled. Use On-Demand Instances.

C.

For the critical data. modify the existing Auto Scaling group. Create a lifecycle hook to ensure that bootstrap scripts are completed successfully. Ensure that the application on the instances is ready to accept traffic before the instances are registered. Create a new version of the launch template that has detailed monitoring enabled.

D.

For the noncritical data, create a second Auto Scaling group that uses a launch template. Configure the launch template to install the unified Amazon CloudWatch agent and to configure the CloudWatch agent with a custom memory utilization metric. Use Spot Instances. Add the new Auto Scaling group as the target group for the ALB. Modify the application to use two target groups for critical data and noncritical data.

E.

For the noncritical data, create a second Auto Scaling group. Choose the predefined memory utilization metric type for the target tracking scaling policy. Use Spot Instances. Add the new Auto Scaling group as the target group for the ALB. Modify the application to use two target groups for critical data and noncritical data.

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