VALID TEST AWS-DEVOPS TESTKING - LATEST AWS-DEVOPS EXAM PAPERS

Valid Test AWS-DevOps Testking - Latest AWS-DevOps Exam Papers

Valid Test AWS-DevOps Testking - Latest AWS-DevOps Exam Papers

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Tags: Valid Test AWS-DevOps Testking, Latest AWS-DevOps Exam Papers, AWS-DevOps Latest Braindumps Sheet, Latest AWS-DevOps Mock Test, Test AWS-DevOps Questions Pdf

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Latest AWS-DevOps Exam Papers, AWS-DevOps Latest Braindumps Sheet

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q462-Q467):

NEW QUESTION # 462
A Development team uses AWS CodeCommit for source code control. Developers apply their changes to various feature branches and create pull requests to move those changes to the master branch when they are ready for production. A direct push to the master branch should not be allowed. The team applied the AWS managed policy AWSCodeCommitPowerUser to the Developers' IAM role, but now members are able to push to the master branch directly on every repository in the AWS account.
What actions should be taken to restrict this?

  • A. Create an additional policy to include a deny rule for the codecommit: GitPush action, and include a restriction for the specific repositories in the resource statement with a condition for the master reference.
  • B. Modify the IAM policy and include a deny rule for the codecommit: GitPush action for the specific repositories in the resource statement with a condition for the master reference.
  • C. Remove the IAM policy and add an AWSCodeCommitReadOnly policy. Add an allow rule for the codecommit: GitPush action for the specific repositories in the resource statement with a condition for the master reference.
  • D. Create an additional policy to include an allow rule for the codecommit: GitPush action and include a restriction for the specific repositories in the resource statement with a condition for the feature branches reference.

Answer: B


NEW QUESTION # 463
You have a web application that is currently running on a collection of micro instance types in a single AZ behind a single load balancer.
You have an Auto Scaling group configured to scale from 2 to 64 instances. When reviewing your CloudWatch metrics, you see that sometimes your Auto Scaling group is running 64 micro instances. The web application is reading and writing to a DynamoDB-configured backend and configured with 800 Write Capacity Units and 800 Read Capacity Units.
Your customers are complaining that they are experiencing long load times when viewing your website.
You have investigated the DynamoDB CloudWatch metrics; you are under the provisioned Read and write Capacity Units and there is no throttling.
How do you scale your service to improve the load times and ensure the principles of high availability?

  • A. Change your Auto Scaling group configuration to include multiple AZs.
  • B. Add a second load balancer to your Auto Scaling group so that you can support more inbound connections per second.
  • C. Change your Auto Scaling group configuration to use larger instances and include multiple AZ's instead of one.
  • D. Change your Auto Scaling group configuration to include multiple AZs, and increase the number of Read Capacity Units in your DynamoDB table by a factor of three, because you will need to be calling DynarnoDB from three AZs.

Answer: C


NEW QUESTION # 464
The Development team has grown substantially in recent months and so has the number of projects that use separate code repositories. The current process involves configuring AWS CodePipeline manually. There have been service limit alerts regarding the number of Amazon S3 buckets that exist.
Which pipeline option will reduce S3 bucket sprawl alerts?

  • A. Create a new pipeline in a different region for each project to bypass the service limits for S3 buckets in a single region.
  • B. Create new pipelines by using the AWS API or AWS CLI, and configure them to use a single S3 bucket with separate prefixes for each project.
  • C. Combine the multiple separate code repositories into a single one, and deploy using an AWS CodePipeline that has logic for each project.
  • D. Create a new pipeline and S3 bucket for each project by using the AWS API or AWS CLI to bypass the service limits for S3 buckets in a single account.

Answer: B


NEW QUESTION # 465
Your company currently runs a large multi-tier web application. One component is an API service that all other components of your application rely on to perform read/write operations.
This service must have high availability and zero downtime during deployments.
Which technique should you use to provide cost-effective, zero-downtime deployments for this component?

  • A. Re-deploy your application behind a load balancer using an AWS OpsWorks stack and use AWS OpsWorks stack versioning, during deployment create a new version of your application, tell AWS OpsWorks to launch the new version behind your load balancer, and when the new version is launched, terminate the old AWS OpsWorks stack.
  • B. Re-deploy your application behind a load balancer that uses Auto Scaling groups. Create a new identical Auto Scaling group and associate it to your Amazon Route53 zone.
    Configure Amazon Route53 to auto- weight traffic over to the new Auto Scaling group when all instances are marked as healthy.
  • C. Re-deploy your application on Elastic Beanstalk. During deployment, create a new version of your application, and create a new environment running that version in Elastic BeanStalk. Finally, take advantage of the Elastic Beanstalk Swap CNAME operation to switch to the new environment.
  • D. Use an AWS CloudFormation template to re-deploy your application behind a load balancer, and launch a new AWS CloudFormation stack during each deployment.
    Update your load balancer to send traffic to the new stack, and then deploy your software. Leave your old stacks running, and tag their resources with the version for rollback.

Answer: C


NEW QUESTION # 466
Your serverless architecture using AWS API Gateway, AWS Lambda, and AWS DynamoDB experienced a large increase in traffic to a sustained 400 requests per second, and dramatically increased in failure rates. Your requests, during normal operation, last 500 milliseconds on average. Your DynamoDB table did not exceed 50% of provisioned throughput, and Table primary keys are designed correctly. What is the most likely issue?

  • A. You did not request a limit increase on concurrent Lambda function executions.
  • B. You used Consistent Read requests on DynamoDB and are experiencing semaphore lock.
  • C. Your AWS API Gateway Deployment is bottlenecking on request (de)serialization.
  • D. Your API Gateway deployment is throttling your requests.

Answer: A

Explanation:
AWS API Gateway by default throttles at 500 requests per second steady-state, and 1000 requests per second at spike. Lambda, by default, throttles at 100 concurrent requests for safety.
At 500 milliseconds (half of a second) per request, you can expect to support 200 requests per second at 100 concurrency. This is less than the 400 requests per second your system now requires. Make a limit increase request via the AWS Support Console.
AWS Lambda: Concurrent requests safety throttle per account -> 100
http://docs.aws.amazon.com/general/latest/gr/aws_service_limits.html#limits_lambda


NEW QUESTION # 467
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