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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:
Aug 15, 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 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 company that uses electronic patient health records runs a fleet of Amazon EC2 instances with an Amazon Linux operating system. The company must continuously ensure that the EC2 instances are running operating system patches and application patches that are in compliance with current privacy regulations. The company uses a custom repository to store application patches.

A DevOps engineer needs to automate the deployment of operating system patches and application patches. The DevOps engineer wants to use both the default operating system patch repository and the custom patch repository.

Which solution will meet these requirements with the LEAST effort?

Options:

A.

Use AWS Systems Manager to create a new custom patch baseline that includes the default operating system repository and the custom repository. Run the AWS-RunPatchBaseline document by using the Run command to verify and install patches. Use the BaselineOverride API to configure the new custom patch baseline.

B.

Use AWS Direct Connect to integrate the custom repository with the EC2 instances. Use Amazon EventBridge events to deploy the patches.

C.

Use the yum-config-manager command to add the custom repository to the /etc/yum.repos.d configuration. Run the yum-config-manager-enable command to activate the new repository.

D.

Use AWS Systems Manager to create a patch baseline for the default operating system repository and a second patch baseline for the custom repository. Run the AWS-RunPatchBaseline document by using the Run command to verify and install patches. Use the BaselineOverride API to configure the default patch baseline and the custom patch baseline.

Question 3

A company uses an Amazon Elastic Kubernetes Service (Amazon EKS) cluster to host its machine learning (ML) application. As the ML model and the container image grow, pod startup time has increased to several minutes. The DevOps engineer created an EventBridge rule that triggers Systems Manager automation to prefetch container images from ECR. Node groups and the cluster have tags configured.

What should the DevOps engineer do next to meet the requirements?

Options:

A.

Create an IAM role allowing EventBridge to use Systems Manager to run commands in the control plane nodes. Create a State Manager association using control plane node tags.

B.

Create an IAM role allowing EventBridge to use Systems Manager to run commands in the worker nodes. Create a State Manager association using the nodes’ machine size.

C.

Create an IAM role allowing EventBridge to use Systems Manager to run commands in the worker nodes. Create a State Manager association using the nodes’ tags to prefetch container images.

D.

Create an IAM role allowing EventBridge to use Systems Manager to run commands in the control plane nodes. Create a State Manager association using the nodes’ tags.

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