|Exam Name||AWS Certified DevOps Engineer - Professional|
|Update Date||May 29,2023|
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The AWS Certified DevOps Engineer - Professional DOP-C01 Exam is a certification exam offered by Amazon Web Services (AWS) that tests a candidate's knowledge and skills in the area of DevOps engineering. This exam is designed for professionals who have experience in managing and automating the deployment of applications, as well as managing the infrastructure on which those applications run.
The exam covers a range of topics, including:
The exam consists of multiple-choice and multiple-response questions and is timed at 180 minutes. The passing score is 750 out of a possible 1000 points. Candidates must have already passed the AWS Certified SysOps Administrator - Associate or AWS Certified Developer - Associate exam before attempting the DevOps Engineer - Professional exam.
Achieving this certification demonstrates that the candidate has the skills and knowledge necessary to design, manage, and maintain a DevOps environment on AWS, and can be a valuable asset to organizations looking to implement or improve their DevOps practices.
You can use the "practise exam" and "virtual exam" choices to review your DOP-C01 questions and answers and practise test questions. Test your knowledge by taking a virtual exam, which simulates the experience of taking exams at a Prometric or VUE testing facility. DOP-C01 Exam practise: go over each exam question individually and look at the justifications and correct solutions.
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Products purchased through Amazon Dumps are valid for 90 days after the date of purchase. This ensures that you receive the most recent DOP-C01 exam preparation materials within those 90 days. Updates to the DOP-C01 may include, but are not limited to, new questions, updates, and modifications made by our editorial staff.
A Development team uses AWS CodeCommit for source code control. Developers applytheir changes to various feature branches and create pull requests to move those changesto the master branch when they are ready for production. A direct push to the masterbranch should not be allowed. The team applied theAWS managed policy AWSCodeCommitPowerUser to the Developers' IAM Rote, but nowmembers are able to push to the master branch directly on every repository in the AWSaccount.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 acondition for the master reference
B. Remove the IAM policy and add an AWSCodeCommitReadOnly policy. Add an allow rule for the codecommit:GitPush action for the specific repositories in the resourcestatement with a condition for the master reference.
C. Modify the IAM policy and include a deny rule for the codecommit:GitPush action for thespecific 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 actionand include a restriction for the specific repositories in the resource statement with acondition for the feature branches reference.
An Application team is refactoring one of its internal tools to run in AWS instead of onpremises hardware. All of the code is currently written in Python and is standalone. Thereis also no external state store or relational database to be queried.Which deployment pipeline incurs the LEAST amount of changes between developmentand production?
A. Developers should use Docker for local development. Use AWS SMS to import thesecontainers as AMIs for Amazon EC2 whenever dependencies are updated. Use AWSCodePipeline to test new code changes against the Auto Scaling group.
B. Developers should use their native Python environment. When Dependencies arechanged and a new container is ready, use AWS CodePipeline and AWS CodeBuild toperform functional tests and then upload the new container to the Amazon ECR. Use AWSCloudFormation with the custom container to deploy the new Amazon ECS.
C. Developers should use their native Python environment. When Dependencies arechanged and a new code is ready, use AWS CodePipeline and AWS CodeBuild to performfunctional tests and then upload the new container to the Amazon ECR. Use CodePipelineand CodeBuild with the custom container to test new code changes inside AWS ElasticBeanstalk
A company wants to implement a CI/CD pipeline for an application that is deployed onAWS. The company also has a source-code analysis tool hosted on premises that checksfor security flaws. The tool has not yet been migrated to AWS and can be accessed only onpremises. The company wants to run checks against the source code as part of thepipeline before the code is compiled. The checks take anywhere from minutes to an hour tocomplete.How can a DevOps Engineer meet these requirements?
A. Use AWS CodePipeline to create a pipeline. Add an action to the pipeline to invoke anAWS Lambda function after the source stage. Have the Lambda function invoke thesource-code analysis tool on premises against the source input from CodePipeline. Thefunction then waits for the execution to complete and places the output in a specifiedAmazon S3 location.
B. Use AWS CodePipeline to create a pipeline, then create a custom action type. Create ajob worker for the custom action that runs on hardware hosted on premises. The job workerhandles running security checks with the on-premises code analysis tool and then returnsthe job results to CodePipeline. Have the pipeline invoke the custom action after the sourcestage.
C. Use AWS CodePipeline to create a pipeline. Add a step after the source stage to makean HTTPS request to the on-premises hosted web service that invokes a test with thesource code analysis tool. When the analysis is complete, the web service sends theresults back by putting the results in an Amazon S3 output location provided byCodePipeline.
D. Use AWS CodePipeline to create a pipeline. Create a shell script that copies the inputsource code to a location on premises. Invoke the source code analysis tool and return theresults to CodePipeline. Invoke the shell script by adding a custom script action after thesource stage.
You have an application consisting of a stateless web server tier running on Amazon EC2 instances behind load balancer, and are using Amazon RDS with read replicas. Which of the following methods should you use to implement a self-healing and cost-effective architecture? Choose 2 answers from the optionsgiven below
A. Set up a third-party monitoring solution on a cluster of Amazon EC2 instances in orderto emit custom Cloud Watch metrics to trigger the termination of unhealthy Amazon EC2instances.
B. Set up scripts on each Amazon EC2 instance to frequently send ICMP pings to the loadbalancer in order to determine which instance is unhealthy and replace it.
C. Set up an Auto Scalinggroup for the web server tier along with an Auto Scaling policythat uses the Amazon RDS DB CPU utilization Cloud Watch metric to scale the instances.
D. Set up an Auto Scalinggroup for the web server tier along with an Auto Scaling policythat uses the Amazon EC2 CPU utilization CloudWatch metric to scale the instances.
E. Use a larger Amazon EC2 instance type for the web server tier and a larger DB instancetype for the data storage layer to ensure that they don't become unhealthy.
F. Set up an Auto Scalinggroup for the database tier along with an Auto Scaling policy thatuses the Amazon RDS read replica lag CloudWatch metric to scale out the Amazon RDSread replicas.
G. Use an Amazon RDS Multi-AZ deployment.
A web application for healthcare services runs on Amazon EC2 instances behind an ELBApplication Load Balancer. The instances run in an Amazon EC2 AutoScaling group across multiple Availability Zones. A DevOps Engineer must create amechanism in which an EC2 instance can be taken out of production so its system logs canbe analyzed for issues to quickly troubleshot problems on the web tier.How can the Engineer accomplish this task while ensuring availability and minimizingdowntime?
A. Implement EC2 Auto Scaling groups cooldown periods. Use EC2 instance metadata todetermine the instance state, and an AWS Lambda function to snapshot Amazon EBSvolumes to preserve system logs.
B. Implement Amazon CloudWatch Events rules. Create an AWS Lambda function that canreact to an instance termination to deploy the CloudWatch Logs agent to upload the systemand access logs to Amazon S3 for analysis.
C. Terminate the EC2 instances manually. The Auto Scaling service will upload all loginformation to CloudWatch Logs for analysis prior to instance termination.
D. Implement EC2 Auto Scaling groups with lifecycle hooks. Create an AWS Lambdafunction that can modify an EC2 instance lifecycle hook into a standby state, extract logsfrom the instance through a remote script execution, and place them in an Amazon S3bucket for analysis.
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