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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
Questions:
392
Last Updated:
Dec 19, 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

Question 1

A company runs a microservices application on Amazon Elastic Kubernetes Service (Amazon EKS). Users recently reported significant delays while accessing an account summary feature, particularly during peak business hours.

A DevOps engineer used Amazon CloudWatch metrics and logs to troubleshoot the issue. The logs indicated normal CPU and memory utilization on the EKS nodes. The DevOps engineer was not able to identify where the delays occurred within the microservices architecture.

The DevOps engineer needs to increase the observability of the application to pinpoint where the delays are occurring.

Which solution will meet these requirements?

Options:

A.

Deploy the AWS X-Ray daemon as a DaemonSet in the EKS cluster. Use the X-Ray SDK to instrument the application code. Redeploy the application.

B.

Enable CloudWatch Container Insights for the EKS cluster. Use the Container Insights data to diagnose the delays.

C.

Create alarms based on the existing CloudWatch metrics. Set up an Amazon Simple Notification Service (Amazon SNS) topic to send email alerts.

D.

Increase the timeout settings in the application code for network operations to allow more time for operations to finish.

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Question 2

A company is building a serverless application that uses AWS Lambda functions to process data.

A BeginResponse Lambda function initializes data in response to specific application events. The company needs to ensure that a large number of Lambda functions are invoked after the BeginResponse Lambda function runs. Each Lambda function must be invoked in parallel and depends on only the outputs of the BeginResponse Lambda function. Each Lambda function has retry logic for invocation and must be able to fine-tune concurrency without losing data.

Which solution will meet these requirements with the MOST operational efficiency?

Options:

A.

Create an Amazon Simple Notification Service (Amazon SNS) topic. Modify the BeginResponse Lambda function to publish to the SNS topic before the BeginResponse Lambda function finishes running. Subscribe all Lambda functions that need to invoke after the BeginResponse Lambda function runs to the SNS topic. Subscribe any new Lambda functions to the SNS topic.

B.

Create an Amazon Simple Queue Service (Amazon SQS) queue for each Lambda function that needs to run after the BeginResponse Lambda function runs. Subscribe each Lambda function to its own SQS queue. Create an Amazon Simple Notification Service (Amazon SNS) topic. Subscribe each SQS queue to the SNS topic. Modify the BeginResponse function to publish to the SNS topic when it finishes running.

C.

Create an Amazon Simple Queue Service (Amazon SQS) queue for each Lambda function that needs to run after the BeginResponse Lambda function runs. Subscribe the Lambda function to the SQS queue. Create an Amazon Simple Notification Service (Amazon SNS) topic for each SQS queue. Subscribe the SQS queues to the SNS topics. Modify the BeginResponse function to publish to the SNS topics when the function finishes running.

D.

Create an AWS Step Functions Standard Workflow. Configure states in the workflow to invoke the Lambda functions sequentially. Create an Amazon Simple Notification Service (Amazon SNS) topic. Modify the BeginResponse Lambda function to publish to the SNS topic before the Lambda function finishes running. Create a new Lambda function that is subscribed to the SNS topic and that invokes the Step Functions workflow.

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.