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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:
366
Last Updated:
Nov 22, 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 DevOps engineer manages a Java-based application that runs in an Amazon Elastic Container Service (Amazon ECS) cluster on AWS Fargate. Auto scaling has not been configured for the application. The DevOps engineer has determined that the Java Virtual Machine (JVM) thread count is a good indicator of when to scale the application. The application serves customer traffic on port 8080 and makes JVM metrics available on port 9404. Application use has recently increased. The DevOps engineer needs to configure auto scaling for the application. Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.

Deploy the Amazon CloudWatch agent as a container sidecar. Configure the CloudWatch agent to retrieve JVM metrics from port 9404. Create CloudWatch alarms on the JVM thread count metric to scale the application. Add a step scaling policy in Fargate to scale up and scale down based on the CloudWatch alarms.

B.

Deploy the Amazon CloudWatch agent as a container sidecar. Configure a metric filter for the JVM thread count metric on the CloudWatch log group for the CloudWatch agent. Add a target tracking policy in Fargate. Select the metric from the metric filter as a scale target.

C.

Create an Amazon Managed Service for Prometheus workspace. Deploy AWS Distro for OpenTelemetry as a container sidecar to publish the JVM metrics from port 9404 to the Prometheus workspace. Configure rules for the workspace to use the JVM thread count metric to scale the application. Add a step scaling policy in Fargate. Select the Prometheus rules to scale up and scaling down.

D.

Create an Amazon Managed Service for Prometheus workspace. Deploy AWS Distro for OpenTelemetry as a container sidecar to retrieve JVM metrics from port 9404 to publish the JVM metrics from port 9404 to the Prometheus workspace. Add a target tracking policy in Fargate. Select the Prometheus metric as a scale target.

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Question 2

A development team manually builds a local artifact. The development team moves the artifact to an Amazon S3 bucket to support an application. The application has a local cache that must be cleared when the development team deploys the application to Amazon EC2 instances. For each deployment, the development team runs a command to clear the cache, download the artifact from the S3 bucket, and unzip the artifact to complete the deployment.

The development team wants to migrate the deployment process to a CI/CD process and to track the progress of each deployment.

Which combination of actions will meet these requirements with the MOST operational efficiency? (Select THREE.)

Options:

A.

Set up an AWS CodeConnections compatible Git repository. Allow developers to merge code into the repository. Use AWS CodeBuild to build an artifact and copy the object into the S3 bucket. Configure CodeBuild to run for every merge into the main branch.

B.

Create a custom script to clear the cache. Specify the script in the BeforeInstall lifecycle hook in the AppSpec file.

C.

Create user data for each EC2 instance that contains the cache clearing script. Test the application after deployment. If the deployment is not successful, then redeploy.

D.

Use AWS CodePipeline to deploy the application. Set up an AWS CodeConnections compatible Git repository. Allow developers to merge code into the repository as a source for the pipeline.

E.

Use AWS CodeBuild to build the artifact and place the artifact in the S3 bucket. Use AWS CodeDeploy to deploy the artifact to EC2 instances.

F.

Use AWS Systems Manager to fetch the artifact from the S3 bucket and to deploy the artifact to all the EC2 instances.

Question 3

A DevOps engineer manages an AWS CodePipeline pipeline that builds and deploys a web application on AWS. The pipeline has a source stage, a build stage, and a deploy stage. When deployed properly, the web application responds with a 200 OK HTTP response code when the URL of the home page is requested. The home page recently returned a 503 HTTP response code after CodePipeline deployed the application. The DevOps engineer needs to add an automated test into the pipeline. The automated test must ensure that the application returns a 200 OK HTTP response code after the application is deployed. The pipeline must fail if the response code is not present during the test. The DevOps engineer has added a CheckURL stage after the deploy stage in the pipeline. What should the DevOps engineer do next to implement the automated test?

Options:

A.

Configure the CheckURL stage to use an Amazon CloudWatch action. Configure the action to use a canary synthetic monitoring check on the application URL and to report a success or failure to CodePipeline.

B.

Create an AWS Lambda function to check the response code status of the URL and to report a success or failure to CodePipeline. Configure an action in the CheckURL stage to invoke the Lambda function.

C.

Configure the CheckURL stage to use an AWS CodeDeploy action. Configure the action with an input artifact that is the URL of the application and to report a success or failure to CodePipeline.

D.

Deploy an Amazon API Gateway HTTP API that checks the response code status of the URL and that reports success or failure to CodePipeline. Configure the CheckURL stage to use the AWS Device Farm test action and to provide the API Gateway HTTP API as an input artifact.

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