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DOP-C02 Exam Dumps : AWS Certified DevOps Engineer - Professional

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AWS Certified DevOps Engineer - Professional Questions and Answers

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 ' 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.