Amazon Web Services DOP-C02 Practice Exam Dumps 2025

The quality assurance (QA) team requests permission to inspect the build artifact before the deployment to the production environment occurs. The QA team wants to run an internal penetration testing tool to conduct manual tests. The tool will be invoked by a REST API call.

Which combination of actions should the DevOps engineer take to fulfill this request? (Choose two.)

Options:

Insert a manual approval action between the test actions and deployment actions of the pipeline.

Modify the buildspec.yml file for the compilation stage to require manual approval before completion.

Update the CodeDeploy deployment groups so that they require manual approval to proceed.

Update the pipeline to directly call the REST API for the penetration testing tool.

Update the pipeline to invoke an AWS Lambda function that calls the REST API for the penetration testing tool.

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Question 2

A company is developing a web application that runs on Amazon EC2 Linux instances. The application requires monitoring of custom performance metrics. The company must collect metrics for API response times and database query latency across multiple instances. Which solution will generate the custom metrics with the LEAST operational overhead?

Options:

Install the Amazon CloudWatch agent on the instances. Configure the agent to collect the custom metrics. Instrument the application to send the metrics to the agent.

Use Amazon Managed Service for Prometheus to scrape the custom metrics from the application. Use the Amazon CloudWatch agent to forward the metrics to CloudWatch.

Create a custom AWS Lambda function that polls the application endpoints and database at regular intervals. Program the Lambda function to calculate the custom metrics and to send the metrics to Amazon CloudWatch by using PutMetricData API calls.

Implement custom logging in the application code to record the custom metrics. Use Amazon CloudWatch Logs Insights to extract and analyze the metrics.

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:

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.

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.

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.

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.

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