Amazon Web Services freetechexam full Access Dop-c02 Tutorials by Emil q68 vce pdf

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Exam Name:	AWS Certified DevOps Engineer - Professional
Exam Code:	DOP-C02 Dumps
Vendor:	Amazon Web Services	Certification:	AWS Certified Professional
Questions:	207 Q&A's	Shared By:	emil

Question 40

A company runs an application with an Amazon EC2 and on-premises configuration. A DevOps engineer needs to standardize patching across both environments. Company policy dictates that patching only happens during non-business hours.

Which combination of actions will meet these requirements? (Choose three.)

Options:

Add the physical machines into AWS Systems Manager using Systems Manager Hybrid Activations.

Attach an IAM role to the EC2 instances, allowing them to be managed by AWS Systems Manager.

Create IAM access keys for the on-premises machines to interact with AWS Systems Manager.

Run an AWS Systems Manager Automation document to patch the systems every hour.

Use Amazon EventBridge scheduled events to schedule a patch window.

Use AWS Systems Manager Maintenance Windows to schedule a patch window.

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Question 41

A company deploys a web application on Amazon EC2 instances that are behind an Application Load Balancer (ALB). The company stores the application code in an AWS CodeCommit repository. When code is merged to the main branch, an AWS Lambda function invokes an AWS CodeBuild project. The CodeBuild project packages the code, stores the packaged code in AWS CodeArtifact, and invokes AWS Systems Manager Run Command to deploy the packaged code to the EC2 instances.

Previous deployments have resulted in defects, EC2 instances that are not running the latest version of the packaged code, and inconsistencies between instances.

Which combination of actions should a DevOps engineer take to implement a more reliable deployment solution? (Select TWO.)

Options:

Create a pipeline in AWS CodePipeline that uses the CodeCommit repository as a source provider. Configure pipeline stages that run the CodeBuild project in parallel to build and test the application. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action.

Create a pipeline in AWS CodePipeline that uses the CodeCommit repository as a source provider. Create separate pipeline stages that run a CodeBuild project to build and then test the application. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action.

Create an AWS CodeDeploy application and a deployment group to deploy the packaged code to the EC2 instances. Configure the ALB for the deployment group.

Create individual Lambda functions that use AWS CodeDeploy instead of Systems Manager to run build, test, and deploy actions.

Create an Amazon S3 bucket. Modify the CodeBuild project to store the packages in the S3 bucket instead of in CodeArtifact. Use deploy actions in CodeDeploy to deploy the artifact to the EC2 instances.

Discussion

Answer:

A, C

Explanation:

Explanation:

To implement a more reliable deployment solution, a DevOps engineer should take the following actions:

Create a pipeline in AWS CodePipeline that uses the CodeCommit repository as a source provider. Configure pipeline stages that run the CodeBuild project in parallel to build and test the application. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action. This action will improve the deployment reliability by automating the entire process from code commit to deployment, reducing human errors and inconsistencies. By running the build and test stages in parallel, the pipeline can also speed up the delivery time and provide faster feedback. By using CodeDeploy as the deployment action, the pipeline can leverage the features of CodeDeploy, such as traffic shifting, health checks, rollback, and deployment configuration123
Create an AWS CodeDeploy application and a deployment group to deploy the packaged code to the EC2 instances. Configure the ALB for the deployment group. This action will improve the deployment reliability by using CodeDeploy to orchestrate the deployment across multiple EC2 instances behind an ALB. CodeDeploy can perform blue/green deployments or in-place deployments with traffic shifting, which can minimize downtime and reduce risks. CodeDeploy can also monitor the health of the instances during and after the deployment, and automatically roll back if any issues are detected. By configuring the ALB for the deployment group, CodeDeploy can register and deregister instances from the load balancer as needed, ensuring that only healthy instances receive traffic45

The other options are not correct because they do not improve the deployment reliability or follow best practices. Creating separate pipeline stages that run a CodeBuild project to build and then test the application is not a good option because it will increase the pipeline execution time and delay the feedback loop. Creating individual Lambda functions that use CodeDeploy instead of Systems Manager to run build, test, and deploy actions is not a valid option because it will add unnecessary complexity and cost to the solution. Lambda functions are not designed for long-running tasks such as building or deploying applications. Creating an Amazon S3 bucket and modifying the CodeBuild project to store the packages in the S3 bucket instead of in CodeArtifact is not a necessary option because it will not affect the deployment reliability. CodeArtifact is a secure, scalable, and cost-effective package management service that can store and share software packages for application development67

References:

1: What is AWS CodePipeline? - AWS CodePipeline
2: Create a pipeline in AWS CodePipeline - AWS CodePipeline
3: Deploy an application with AWS CodeDeploy - AWS CodePipeline
4: What is AWS CodeDeploy? - AWS CodeDeploy
5: Configure an Application Load Balancer for your blue/green deployments - AWS CodeDeploy
6: What is AWS Lambda? - AWS Lambda
7: What is AWS CodeArtifact? - AWS CodeArtifact

Question 42

A developer is maintaining a fleet of 50 Amazon EC2 Linux servers. The servers are part of an Amazon EC2 Auto Scaling group, and also use Elastic Load Balancing for load balancing.

Occasionally, some application servers are being terminated after failing ELB HTTP health checks. The developer would like to perform a root cause analysis on the issue, but before being able to access application logs, the server is terminated.

How can log collection be automated?

Options:

Use Auto Scaling lifecycle hooks to put instances in a Pending:Wait state. Create an Amazon CloudWatch alarm for EC2 Instance Terminate Successful and trigger an AWS Lambda function that invokes an SSM Run Command script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.

Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create an AWS Config rule for EC2 Instance-terminate Lifecycle Action and trigger a step function that invokes a script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.

Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create an Amazon CloudWatch subscription filter for EC2 Instance Terminate Successful and trigger a CloudWatch agent that invokes a script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.

Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create an Amazon EventBridge rule for EC2 Instance-terminate Lifecycle Action and trigger an AWS Lambda function that invokes an SSM Run Command script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.

Discussion

Question 43

A company is storing 100 GB of log data in csv format in an Amazon S3 bucket SQL developers want to query this data and generate graphs to visualize it. The SQL developers also need an efficient automated way to store metadata from the csv file.

Which combination of steps will meet these requirements with the LEAST amount of effort? (Select THREE.)

Options:

Fitter the data through AWS X-Ray to visualize the data.

Filter the data through Amazon QuickSight to visualize the data.

Query the data with Amazon Athena.

Query the data with Amazon Redshift.

Use the AWS Glue Data Catalog as the persistent metadata store.

Use Amazon DynamoDB as the persistent metadata store.