Amazon Web Services DOP-C02 Practice Test Questions Answers

A comprehensive and detailed explanation is:

Option A is correct because using batch writes to write multiple log events in a single write operation is a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Batch writes can reduce the number of network round trips and API calls, and can also take advantage of parallel processing by Timestream. Batch writes can also improve the compression ratio of data in the memory store and the magnetic store, which can reduce the storage costs and improve the query performance1.

Option B is incorrect because writing each log event as a single write operation is not a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Writing each log event as a single write operation would increase the number of network round trips and API calls, and would also reduce the compression ratio of data in the memory store and the magnetic store. This would increase the storage costs and degrade the query performance1.

Option C is incorrect because treating each log as a single-measure record is not a recommended practice for optimizing the query performance in Timestream. Treating each log as a single-measure record would result in creating multiple records for each timestamp, which would increase the storage size and the query latency. Moreover, treating each log as a single-measure record would require using joins to query multiple measures for the same timestamp, which would add complexity and overhead to the query processing2.

Option D is correct because treating each log as a multi-measure record is a recommended practice for optimizing the query performance in Timestream. Treating each log as a multi-measure record would result in creating a single record for each timestamp, which would reduce the storage size and the query latency. Moreover, treating each log as a multi-measure record would allow querying multiple measures for the same timestamp without using joins, which would simplify and speed up the query processing2.

Option E is incorrect because configuring the memory store retention period to be longer than the magnetic store retention period is not a valid option in Timestream. The memory store retention period must always be shorter than or equal to the magnetic store retention period. This ensures that data is moved from the memory store to the magnetic store before it expires out of the memory store3.

Option F is correct because configuring the memory store retention period to be shorter than the magnetic store retention period is a valid option in Timestream. The memory store retention period determines how long data is kept in the memory store, which is optimized for fast point-in-time queries. The magnetic store retention period determines how long data is kept in the magnetic store, which is optimized for fast analytical queries. By configuring these retention periods appropriately, you can balance your storage costs and query performance according to your application needs3.

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

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

Comprehensive and Detailed Explanation From Exact Extract:

AWS Config has a managed rule called alb-waf-enabled that checks whether AWS WAF is enabled on ALBs. AWS Config supports automatic remediation actions that can be triggered when noncompliance is detected.

By creating a Systems Manager Automation document that adds AWS WAF to the ALB and associating it as the remediation action for the AWS Config rule, the system can automatically detect and remediate any removal of AWS WAF from ALBs without manual intervention.

This is the most operationally efficient and reliable approach to ensure continuous compliance.

Option B lacks automatic remediation. Options C and D rely on drift detection and Lambda, which add complexity and risk downtime during stack replacement.

To prevent ApplicationA from being installed on the additional instance, the deployment group configuration needs to be more specific. By changing the current single tag group to include only the Environment=Production tag and adding another single tag group that includes only the Name=ApplicationA tag, the deployment process will target only the instances that match both tag groups. This ensures that only instances intended for ApplicationA with the correct environment and name tags will receive the deployment, thus excluding the additional instance with the Department=Marketing and Name=ApplicationB tags.

AWS CodeDeploy Documentation: Working with instances for CodeDeploy

AWS CodeDeploy Documentation: Stop a deployment with CodeDeploy

Stack Overflow Discussion: CodeDeploy Deployment failed to stop Application

The correct answer is D. Creating an AWS Config conformance pack that contains the AWS Config rules and remediation actions and deploying it from the delegated administrator account by using AWS Config will meet the requirements. A conformance pack is a collection of AWS Config rules and remediation actions that can be easily deployed as a single entity in an account and a region or across an organization in AWS Organizations1. By using the delegated administrator account, the DevOps engineer can centrally manage the conformance pack and prevent non-administrator users from modifying it in the member accounts. Option A is incorrect because creating a CloudFormation template that contains the AWS Config rules and remediation actions and deploying it from the Organizations management account by using CloudFormation StackSets will not prevent non-administrator users from modifying the AWS Config rules in the member accounts. Option B is incorrect because deploying the conformance pack from the Organizations management account by using CloudFormation StackSets will not use the trusted access feature of AWS Config and will require additional permissions and resources. Option C is incorrect because creating a CloudFormation template that contains the AWS Config rules and remediation actions and deploying it from the delegated administrator account by using AWS Config will not leverage the benefits of conformance packs, such as simplified deployment and management. References:

Conformance Packs - AWS Config

Certified DevOps Engineer - Professional (DOP-C02) Study Guide (page 176)