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AWS-DevOps-Engineer-Professional PDF DEMO:
QUESTION NO: 1
A company has an application that has predictable peak traffic times. The company wants the application instances to scale up only during the peak times. The application stores state in Amazon
DynamoDB. The application environment uses a standard Node.js application stack and custom Chef recipes stored in a private Git repository.
Which solution is MOST cost-effective and requires the LEAST amount of management overhead when performing rolling updates of the application environment?
A. Configure AWS OpsWorks stacks and push the custom recipes to an Amazon S3 bucket and configure custom recipes to point to the S3 bucket. Then add an application layer type for a standard
Node.js application server and configure the custom recipe to deploy the application in the deploy step from the S3 bucket. Configure time-based instances and attach an Amazon EC2 IAM role that provides permission to access DynamoDB
B. Create a custom AMI with the Node.js environment and application stack using Chef recipes. Use the AMI in an Auto Scaling group and set up scheduled scaling for the required times, then set up an
Amazon EC2 IAM role that provides permission to access DynamoDB.
C. Create a Docker file that uses the Chef recipes for the application environment based on an official
Node.js Docker image. Create an Amazon ECS cluster and a service for the application environment, then create a task based on this Docker image. Use scheduled scaling to scale the containers at the appropriate times and attach a task-level IAM role that provides permission to access DynamoD
D. Configure AWS OpsWorks stacks and use custom Chef cookbooks. Add the Git repository information where the custom recipes are stored, and add a layer in OpsWorks for the Node.js application server.
Then configure the custom recipe to deploy the application in the deploy step. Configure time-based instances and attach an Amazon EC2 IAM role that provides permission to access DynamoDB.
Answer: A
QUESTION NO: 2
A Security team is concerned that a Developer can unintentionally attach an Elastic IP address to an Amazon EC2 instance in production. No Developer should be allowed to attach an Elastic IP address to an instance.
The Security team must be notified if any production server has an Elastic IP address at any time.
How can this task be automated?
A. Ensure that all IAM groups are associated with Developers do not have associate-address permissions.
Create a scheduled AWS Lambda function to check whether an Elastic IP address is associated with any instance tagged as production, and alert the Security team if an instance has an Elastic IP address associated with it.
B. Create an AWS Config rule to check that all production instances have the EC2 IAM roles that include deny associate-address permissions. Verify whether there is an Elastic IP address associated with any instance, and alert the Security team if an instance has an Elastic IP address associated with it.
C. Use Amazon Athena to query AWS CloudTrail logs to check for any associate-address attempts.
Create an AWS Lambda function to dissociate the Elastic IP address from the instance, and alert the
Security team.
D. Attach an IAM policy to the Developer's IAM group to deny associate-address permissions. Create a custom AWS Config rule to check whether an Elastic IP address is associated with any instance tagged as production, and alert the Security team.
Answer: D
QUESTION NO: 3
An Application team is refactoring one of its internal tools to run in AWS instead of on- premises hardware.
All of the code is currently written in Python and is standalone. There is also no external state store or relational database to be queried.
Which deployment pipeline incurs the LEAST amount of changes between development and production?
A. Developers should use their native Python environment. When Dependencies are changed and a new container is ready, use AWS CodePipeline and AWS CodeBuild to perform functional tests and then upload the new container to the Amazon ECR. Use AWS CloudFormation with the custom container to deploy the new Amazon ECS.
B. Developers should use Docker for local development. Use AWS SMS to import these containers as
AMIs for Amazon EC2 whenever dependencies are updated. Use AWS CodePipeline to test new code changes against the Auto Scaling group.
C. Developers should use their native Python environment. When Dependencies are changed and a new code is ready, use AWS CodePipeline and AWS CodeBuild to perform functional tests and then upload the new container to the Amazon ECR. Use CodePipeline and CodeBuild with the custom container to test new code changes inside AWS Elastic Beanstalk
Answer: B
QUESTION NO: 4
A company is migrating an application to AWS that runs on a single Amazon EC2 instance.
Because of licensing limitations, the application does not support horizontal scaling. The application will be using Amazon Aurora for its database.
How can the DevOps Engineer architect automated healing to automatically recover from EC2 and
Aurora failures, in addition to recovering across Availability Zones (AZs), in the MOST cost-effective manner?
A. Create an EC2 instance and enable instance recovery. Create an Aurora database with a read replica in a second AZ, and promote it to a primary database instance if the primary database instance fails.
B. Create an Amazon CloudWatch Events rule to trigger an AWS Lambda function to start a new EC2 instance in an available AZ when the instance status reaches a failure state. Create an Aurora database with a read replica in a second AZ, and promote it to a primary database instance when the primary database instance fails.
C. Create an EC2 Auto Scaling group with a minimum and maximum instance count of 1, and have it span across AZs. Use a single-node Aurora instance.
D. Assign an Elastic IP address on the instance. Create a second EC2 instance in a second AZ. Create an Amazon CloudWatch Events rule to trigger an AWS Lambda function to move the Elastic IP address to the second instance when the first instance fails. Use a single-node Aurora instance.
Answer: B
QUESTION NO: 5
A company has developed an AWS Lambda function that handles orders received through an
API. The company is using AWS CodeDeploy to deploy the Lambda function as the final stage of a
CI/CD pipeline. A DevOps Engineer has notices there are intermittent failures of the ordering API for a few seconds after deployment. After some investigation, the DevOps Engineer believes the failures are due to database changes the CloudFormation stack for the application lambda function begins executing. How should the DevOps Engineer overcome this?
A. Add a BeforeAllowTraffic hook to the AppSpec file that tests and waits for any necessary database changes before traffic can flow to the new version of the Lambda function
B. Add an AfterAllowTraffic hook to the AppSpec file that forces traffic to wait for any pending database changes before allowing the new version of the Lambda function to respond
C. Add a ValidateService hook to the AppSpec file that inspects incoming traffic and rejects the payload if dependent services such as the database are not yet ready
D. Add a BeforeInstall hook to the AppSpec file that tests and waits for any necessary database changes before deploying the new version of the Lambda function
Answer: B
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Updated: May 28, 2022