DOP-C02 Practice Test

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.)

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 newversion 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 newversion 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 AmazonCloudWatch agent and to configure the CloudWatch agent with a custom memory utilization metric. Use Spot Instances. Add the new Auto Scaling group asthe 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.

A DevOps engineer used an AWS Cloud Formation custom resource to set up AD Connector. The AWS Lambda function ran and created AD Connector, but Cloud Formation is not transitioning from CREATE_IN_PROGRESS to CREATE_COMPLETE.
Which action should the engineer take to resolve this issue?

A. Ensure the Lambda function code has exited successfully.

B. Ensure the Lambda function code returns a response to the pre-signed URL.

C. Ensure the Lambda function IAM role has cloudformation UpdateStack permissions for the stack ARN.

D. Ensure the Lambda function IAM role has ds ConnectDirectory permissions for the AWS account.

A company is using an Amazon Aurora cluster as the data store for its application. The Aurora cluster is configured with a single DB instance. The application performs read and write operations on the database by using the cluster's instance endpoint.

The company has scheduled an update to be applied to the cluster during an upcoming maintenance window. The cluster must remain available with the least possible interruption during the maintenance window.

What should a DevOps engineer do to meet these requirements?

A. Add a reader instance to the Aurora cluster. Update the application to use the Aurora cluster endpoint for write operations. Update the Aurora cluster's reader endpoint for reads.

B. Add a reader instance to the Aurora cluster. Create a custom ANY endpoint for the cluster. Update the application to use the Aurora cluster's custom ANY endpoint for read and write operations.

C. Turn on the Multi-AZ option on the Aurora cluster. Update the application to use the Aurora cluster endpoint for write operations. Update the Aurora cluster’s reader endpoint for reads.

D. Turn on the Multi-AZ option on the Aurora cluster. Create a custom ANY endpoint for the cluster. Update the application to use the Aurora cluster's custom ANY endpoint for read and write operations.

A company has an application that runs on Amazon EC2 instances that are in an Auto Scaling group. When the application starts up. the application needs to process data from an Amazon S3 bucket before the application can start to serve requests.

The size of the data that is stored in the S3 bucket is growing. When the Auto Scaling group adds new instances, the application now takes several minutes to download and process the data before the application can serve requests. The company must reduce the time that elapses before new EC2 instances are ready to serve requests.

Which solution is the MOST cost-effective way to reduce the application startup time?

A. Configure a warm pool for the Auto Scaling group with warmed EC2 instances in the Stopped state. Configure an autoscaling:EC2_INSTANCE_LAUNCHING lifecycle hook on the Auto Scaling group. Modify the application to complete the lifecycle hook when the application is ready to serve requests.

B. Increase the maximum instance count of the Auto Scaling group. Configure an autoscaling:EC2_INSTANCE_LAUNCHING lifecycle hook on the Auto Scaling group.
Modify the application to complete the lifecycle hook when the application is ready to serve requests.

C. Configure a warm pool for the Auto Scaling group with warmed EC2 instances in the Running state. Configure an autoscaling:EC2_INSTANCE_LAUNCHING lifecycle hook on the Auto Scaling group. Modify the application to complete the lifecycle hook when the application is ready to serve requests.

D. Increase the maximum instance count of the Auto Scaling group. Configure an autoscaling:EC2_INSTANCE_LAUNCHING lifecycle hook on the Auto Scaling group.
Modify the application to complete the lifecycle hook and to place the new instance in the Standby state when the application is ready to serve requests.

A company builds a container image in an AWS CodeBuild project by running Docker commands. After the container image is built, the CodeBuild project uploads the container image to an Amazon S3 bucket. The CodeBuild project has an 1AM service role that has permissions to access the S3 bucket.

A DevOps engineer needs to replace the S3 bucket with an Amazon Elastic Container Registry (Amazon ECR) repository to store the container images. The DevOps engineer creates an ECR private image repository in the same AWS Region of the CodeBuild project. The DevOps engineer adjusts the 1AM service role with the permissions that are necessary to work with the new ECR repository. The DevOps engineer also places new repository information into the docker build command and the docker push command that are used in the buildspec.yml file.

When the CodeBuild project runs a build job, the job fails when the job tries to access the ECR repository.

Which solution will resolve the issue of failed access to the ECR repository?

A. Update the buildspec.yml file to log in to the ECR repository by using the aws ecr getlogin- password AWS CLI command to obtain an authentication token. Update the docker login command to use the authentication token to access the ECR repository.

B. Add an environment variable of type SECRETS_MANAGER to the CodeBuild project. In the environment variable, include the ARN of the CodeBuild project's lAM service role.
Update the buildspec.yml file to use the new environment variable to log in with the docker login command to access the ECR repository.

C. Update the ECR repository to be a public image repository. Add an ECR repository policy that allows the 1AM service role to have access.

D. Update the buildspec.yml file to use the AWS CLI to assume the 1AM service role for ECR operations. Add an ECR repository policy that allows the 1AM service role to have access.