AWS-DevOps-Professional Practice Questions

A.  

Use AWS CloudForrnation to create an AWS Step Functions state machine and Auto Scaling lifecycle hooks to move to one instance at a time into a wait state. Use AWS Systems Manager automation to deploy the update to each instance and move it back into the Auto Scaling group using the heartbeat timeout

B.  

Use AWS CodeDeploy with Amazon EC2 Auto Scaling Configure an alarm tied to the CPU utilization metric Use the CodeDeployDefault OneAtAtime configuration as a deployment strategy Configure automatic rollbacks within the deployment group to roll back the deployment if the alarm thresholds are breached

C.  

Use AWS Elastic Beanstalk for load balancing and AWS Auto Scaling Configure an alarm tied to the CPU utilization metric Configure rolling deployments with a fixed batch size of one instance Enable enhanced health to monitor the status of the deployment and roll back based on the alarm previously created

D.  

Use AWS Systems Manager to perform a blue/green deployment with Amazon EC2 Auto Scaling Configure an alarm tied to the CPU utilization metric Deploy updates one at a time Configure automatic rollbacks within the Auto Scaling group to roll back the deployment if the alarm thresholds are breached.

A.  

Allow developers to check the code into a code repository. Using Amazon CloudWatch Events, on every pull into master, trigger an AWS Lambda function to build the artifact and store it in Amazon

B.  

Create a custom script to clear the cache. Specify the script in the Beforelnstall lifecycle hook in the AppSpec file.

C.  

Create user data for each Amazon EC2 instance that contains the clear cache script. Once deployed, test the application. If it is not successful, deploy it again.

D.  

Set up AWS CodePipeline to deploy the application. Allow developers to check the code into a code repository as a source for the pipeline.

E.  

Use AWS CodeBuild to build the artifact and place it in Amazon S3. Use AWS CodeDeploy to deploy the artifact to Amazon EC2 instances.

F.  

Use AWS Systems Manager to fetch the artifact from Amazon S3 and deploy it to all the instances.

A.  

Deletion has failed because the S3 bucket has an active website configuration. Modify the CloudFormation template to remove the Website Configuration property from the S3 bucket resource.

B.  

Deletion has failed because the S3 bucket is not empty. Modify the custom resource's AWS Lambda function code to recursively empty the bucket when is Delete. RequestType

C.  

Deletion has failed because the custom resource does not define a deletion policy. Add a Deletion Policy property to the custom resource definition with a value of RemoveOnDeletion.

D.  

Deletion has failed because the S3 bucket is not empty. Modify the S3 bucket resource in the CloudFormation template to add a Deletion Policy property with a value of Empty.

A.  

Use AWS CodePipeline to trigger a build process when software is pushed to a self-hosted GitHub repository. CodePipeline will use a Jenkins build server to build new Docker images. CodePipeline will deploy into a second target group in Amazon ECS behind an Application Load Balancer. Cutover will be managed by swapping the listener rules on the Application Load Balancer.

B.  

Use AWS CodePipeline to trigger a build process when software is pushed to a private GitHub repository. CodePipeline will use AWS CodeBuild to build new Docker images. CodePipeline will deploy into a second target group in Amazon ECS behind an Application Load Balancer. Cutover will be managed by swapping the listener rules on the Application Load Balancer.

C.  

Use a Jenkins pipeline to trigger a build process when software is pushed to a private GitHub repository. AWS CodePipeline will use AWS CodeBuild new Docker images. CodePipeline will deploy into a second target group in Amazon ECS behind an Application Load Balancer. Cutover will be managed by swapping the listener rules on the Application Load Balancer.

D.  

Use AWS CodePipeline to trigger a build process when software is pushed to an AWS CodeCommit repository CodePipeline will use an AWS CodeBuild build server to build new Docker images. CodePipeline will deploy into a second target group in a Kubernetes Cluster hosted on Amazon EC2 behind an Application Load Balancer. Cutover will be managed by swapping the listener rules on the Application Load Balancer.

A.  

Add the code updates as commits to the release branch. The team can delay the deployment until all features are ready.

B.  

Add the code updates as commits to a feature branch. Merge the commits to a release branch as features are ready.

C.  

Add the code updates as a single commit when a feature is ready. Tag this commit with "new-country."

D.  

Create a new repository named "new-country". Commit all the code changes to the new repository.

A.  

Tag the Amazon EC2 instances depending on the deployment group. Then place a script into the application revision that calls the metadata service and the EC2 API to identify which deployment group the instance is part of. Use this information to configure the log level settings. Reference the script as part of the Afterinstall lifecycle hook in the appspec.yml file.

B.  

Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_NAME to identify which deployment group the instances is part of. Use this information to configure the log level settings. Reference this script as part of the BeforeInstall lifecycle hook in the appspec.yml file

C.  

Create a CodeDeploy custom environment variable for each environment. Then place a script into the application revision that checks this environment variable to identify which deployment group the instance is part of. Use this information to configure the log level settings. Reference this script as part of the ValidateService lifecycle hook in the appspec.yml file.

D.  

Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_ID to identify which deployment group the instance is part of to configure the log level settings. Reference this script as part of the Install lifecycle hook in the appspec.yml file.

A.  

At instance launch time, use EC2 user data to deploy the AWS CodeDeploy agent. Use CodeDeploy to install the Puppet agent. When the Auto Scaling group scales out, run a script to register the newly deployed instances to the Puppet master. When the Auto Scaling group scales in, use the EC2 Auto Scaling lifecycle hook to trigger de-registration from the Puppet master. EC2_INSTANCE_TERMINATING

B.  

Bake the AWS CodeDeploy agent into the base AMI. When the Auto Scaling group scales out, use CodeDeploy to install the Puppet agent, and execute a script to register the newly deployed instances to the Puppet master. When the Auto Scaling group scales in, use the CodeDeploy ApplicationStop lifecycle hook to run a script to de-register the instance from the Puppet master.

C.  

At instance launch time, use EC2 user data to deploy the AWS CodeDeploy agent. When the Auto Scaling group scales out, use CodeDeploy to install the Puppet agent, and run a script to register the newly deployed instances to the Puppet master. When the Auto Scaling group scales in, use the EC2 user data instance stop script to run a script to de-register the instance from the Puppet master.

D.  

Bake the AWS Systems Manager agent into the base AMI. When the Auto Scaling group scales out, use the AWS Systems Manager to install the Puppet agent, and run a script to register the newly deployed instances to the Puppet master. When the Auto Scaling group scales in, use the Systems Manager instance stop lifecycle hook to run a script to de-register the instance from the Puppet master.

A.  

Install the Amazon CloudWatch Logs agent on all instances to push the application logs to CloudWatch Logs. Use metric filters to count the error messages every minute, and trigger a CloudWatch alarm if the count exceeds 10 errors.

B.  

Install the Amazon CloudWatch Logs agent on all instances to push the access logs to CloudWatch Logs. Create CloudWatch Events rule to count the error messages every minute, and trigger a CloudWatch alarm if the count exceeds 10 errors.

C.  

Install the Amazon CloudWatch Logs agent on all instances to push the application logs to CloudWatchLogs. Use a metric filter to generate a custom CloudWatch metric that records the number of failures and triggers a CloudWatch alarm if the custom metric reaches 10 errors in a 1-minute period.

D.  

Deploy a custom script on all instances to check application logs regularly in a cron job. Count the number of error messages every minute, and push a data point to a custom. CloudWatch metric. Trigger a CloudWatch alarm if the custom metric reaches 10 errors in a 1-minute period.

A.  

Create a second ELB, and a new Auto Scaling Group assigned a new Launch Configuration. Create a new AMI with the updated app. Use Route53 Weighted Round Robin records to adjust the proportion of traffic hitting the two ELBs.

B.  

Create new AM Is with the new app. Then use the new EC2 instances in half proportion to the older instances.

C.  

Redeploy with AWS Elastic Beanstalk and Elastic Beanstalk versions. Use Route 53 Weighted Round Robin records to adjust the proportion of traffic hitting the two ELBs

D.  

Create a full second stack of instances, cut the DNS over to the new stack of instances, and change the DNS back if a rollback is needed.

A.  

Write a shell script to run as a cron job on EC2 instances to collect and push the data to Amazon S3. For on-premises resources, use VMware vSphere to collect the data and write it into a file gateway for storing the data in S3. Finally, use Amazon Athena on the S3 bucket for analytics.

B.  

Use a script on the on-premises virtual machines as well as the EC2 instances to gather and push the data into Amazon S3, and then use Amazon Athena for analytics.

C.  

Install AWS Systems Manager agents on both the on-premises virtual machines and the EC2 instances. Enable inventory collection and configure resource data sync to an Amazon S3 bucket to analyze the data with Amazon Athena.

D.  

Use AWS Application Discovery Service for deploying Agentless Discovery Connector in the VMware environment and Discovery Agents on the EC2 instances for collecting the data. Then use the AWS Migration Hub Dashboard for analytics.