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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:
449
Last Updated:
Sep 18, 2026
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 is researching the least expensive way to implement an image batch processing cluster on AWS. The application cannot run in Docker containers and must run on Amazon EC2. The batch job stores checkpoint data on an NFS volume and can tolerate interruptions. Configuring the cluster software from a generic EC2 Linux image takes 30 minutes.

What is the MOST cost-effective solution?

Options:

A.

Use Amazon EFS (or checkpoint data. To complete the job, use an EC2 Auto Scaling group and an On-Demand pricing model to provision EC2 instances temporally.

B.

Use GlusterFS on EC2 instances for checkpoint data. To run the batch job configure EC2 instances manually When the job completes shut down the instances manually.

C.

Use Amazon EFS for checkpoint data Use EC2 Fleet to launch EC2 Spot Instances and utilize user data to configure the EC2 Linux instance on startup.

D.

Use Amazon EFS for checkpoint data Use EC2 Fleet to launch EC2 Spot Instances Create a custom AMI for the cluster and use the latest AMI when creating instances.

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Question 2

A company has a continuous integration pipeline where the company creates container images by using AWS CodeBuild. The created images are stored in Amazon Elastic Container Registry (Amazon ECR). Checking for and fixing the vulnerabilities in the images takes the company too much time. The company wants to identify the image vulnerabilities quickly and notify the security team of the vulnerabilities. Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)

Options:

A.

Activate Amazon Inspector enhanced scanning for Amazon ECR. Configure the enhanced scanning to use continuous scanning. Set up a topic in Amazon Simple Notification Service (Amazon SNS).

B.

Create an Amazon EventBridge rule for Amazon Inspector findings. Set an Amazon Simple Notification Service (Amazon SNS) topic as the rule target.

C.

Activate AWS Lambda enhanced scanning for Amazon ECR. Configure the enhanced scanning to use continuous scanning. Set up a topic in Amazon Simple Email Service (Amazon SES).

D.

Create a new AWS Lambda function. Invoke the new Lambda function when scan findings are detected.

E.

Activate default basic scanning for Amazon ECR for all container images. Configure the default basic scanning to use continuous scanning. Set up a topic in Amazon Simple Notification Service (Amazon SNS).

Question 3

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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