DOP-C02 Amazon Web Services AWS Certified DevOps Engineer - Professional Free Practice Exam Questions (2025 Updated)

 Turn off cross-zone load balancing on the ALB's target group:

Cross-zone load balancing distributes traffic evenly across all registered targets in all enabled Availability Zones. Turning this off will ensure that each target group only handles requests from its respective Availability Zone.

To disable cross-zone load balancing:

Go to the Amazon EC2 console.

Navigate to Load Balancers and select the ALB.

Choose the Target Groups tab, select the target group, and then select the Group details tab.

Click on Edit and turn off Cross-zone load balancing.

 Use Amazon Route 53 Application Recovery Controller to start a zonal shift away from the Availability Zone:

Amazon Route 53 Application Recovery Controller provides the ability to control traffic flow to ensure high availability and disaster recovery.

By using Route 53 Application Recovery Controller, you can perform a zonal shift to redirect traffic away from the unhealthy Availability Zone.

To start a zonal shift:

Configure Route 53 Application Recovery Controller by creating a cluster and control panel.

Create routing controls to manage traffic shifts between Availability Zones.

Use the routing control to shift traffic away from the affected Availability Zone.

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer Documents Only:

AWS X-Ray provides distributed tracing for microservice-based applications. Deploying the X-Ray daemon as a DaemonSet in the EKS cluster and instrumenting the application with the X-Ray SDK enables end-to-end tracing across microservices, helping identify performance bottlenecks. This method is documented in “Using AWS X-Ray with Amazon EKS” (AWS Observability Guide).

Canary deployments gradually shift traffic (10% at first), enabling monitoring before full rollout.

Monitoring TargetResponseTime aligns with detecting increased latency, a key issue reported.

Alarms tied to the deployment group trigger automatic rollback on threshold breach.

UnHealthyHostCount and TargetConnectionErrorCount are less specific to response time issues.

Linear deployment is valid but canary is preferred for quick detection.

 Step 1: Attaching the CloudWatchAgentServerPolicy to the IAM Role

The CloudWatch agent needs permissions to collect and send metrics, including memory metrics, to Amazon CloudWatch. You can attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile or service account role to grant these permissions.

Action: Attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile that the EKS cluster uses.

Why: This ensures the CloudWatch agent has the necessary permissions to collect memory metrics.

Option A is incorrect because setting the deployment configuration to LambdaAllAtOnce means that the new version of the application will be deployed to all Lambda functions at once, affecting all customers. This does not meet the requirement of affecting the fewest customers possible. Moreover, configuring automatic rollbacks on the deployment group is not operationally efficient, as it requires manual intervention to fix the errors and redeploy the application.

Option B is correct because setting the deployment configuration to LambdaCanary10Percent10Minutes means that the new version of the application will be deployed to 10 percent of the Lambda functions first, and then to the remaining 90 percent after 10 minutes. This minimizes the impact of errors on customers, as only 10 percent of them will be affected by a faulty deployment. Configuring automatic rollbacks on the deployment group also meets the requirement of reverting to the most recent stable version of the application when an error is detected. Creating a CloudWatch alarm that detects HTTP Bad Gateway errors on API Gateway is a valid way to monitor the health of the application and trigger a rollback if needed.

Option C is incorrect because setting the deployment configuration to LambdaAllAtOnce means that the new version of the application will be deployed to all Lambda functions at once, affecting all customers. This does not meet the requirement of affecting the fewest customers possible. Moreover, configuring manual rollbacks on the deployment group is not operationally efficient, as it requires human intervention to stop the current deployment and start a new one. Creating an SNS topic to send notifications every time a deployment fails is not sufficient to detect errors in the application, as it does not monitor the API Gateway responses.

Option D is incorrect because configuring manual rollbacks on the deployment group is not operationally efficient, as it requires human intervention to stop the current deployment and start a new one. Creating a metric filter on a CloudWatch log group for API Gateway to monitor HTTP Bad Gateway errors is a valid way to monitor the health of the application, but invoking a new Lambda function to perform a rollback is unnecessary and complex, as CodeDeploy already provides automatic rollback functionality.

The best solution to log and review all stopped tasks for errors is to use Amazon EventBridge and Amazon CloudWatch Logs. Amazon EventBridge allows the DevOps engineer to create a rule that matches task state change events from Amazon ECS. The rule can then send the event data to Amazon CloudWatch Logs as the target. Amazon CloudWatch Logs can store and monitor the log data, and also provide CloudWatch Logs Insights, a feature that enables the DevOps engineer to interactively search and analyze the log data. Using CloudWatch Logs Insights, the DevOps engineer can filter and aggregate the log data based on various fields, such as cluster, task, container, and reason. This way, the DevOps engineer can easily identify and investigate the stopped tasks and their errors.

The other options are not as effective or efficient as the solution in option A. Option B is not suitable because the embedded metric format is designed for custom metrics, not for logging task state changes. Option C is not feasible because the EC2 instances do not store the task state change events in their logs. Option D is not relevant because the EC2_INSTANCE_TERMINATING lifecycle hook is triggered when an EC2 instance is terminated by the Auto Scaling group, not when a task is stopped by Amazon ECS.

Creating a CloudWatch Events Rule That Triggers on an Event - Amazon Elastic Container Service

Sending and Receiving Events Between AWS Accounts - Amazon EventBridge

Working with Log Data - Amazon CloudWatch Logs

Analyzing Log Data with CloudWatch Logs Insights - Amazon CloudWatch Logs

Embedded Metric Format - Amazon CloudWatch

Amazon EC2 Auto Scaling Lifecycle Hooks - Amazon EC2 Auto Scaling

(A) Use ECR replication to keep images synchronized between Regions, minimizing CI/CD pipeline changes.

(C) DynamoDB global tables allow multi-Region replication and provide local read/write access, so tasks should interact with the DynamoDB replica in their Region.

(D) Use S3 cross-Region replication or separate buckets with replication; tasks access the bucket in the same Region for latency and data sovereignty.

(B) Using DynamoDB global table but pointing tasks to only one Region reduces resilience.

(E) S3 Multi-Region Access Points are newer but add complexity and are not required for minimal changes.

(F) Managing CI/CD pipeline for multiple Regions and failover routing adds complexity beyond minimal changes.

https://repost.aws/knowledge-center/cloudformation-update-rollback-failed If your stack is stuck in the UPDATE_ROLLBACK_FAILED state after a failed update, then the only actions that you can perform on the stack are the ContinueUpdateRollback or DeleteStack operations.

Step 1: Reducing Replication Lag in Aurora Global DatabasesIn Amazon Aurora global databases, write operations on the primary cluster can be delayed due to the time it takes to replicate to secondary clusters, especially when there are multiple secondary regions involved.

Issue: The write operations are occasionally blocked due to the RPO setting, which guarantees replication within 60 seconds.

Action: Remove one of the secondary clusters from the global database.

Why: Fewer secondary clusters will reduce the overall replication lag, improving write performance and reducing the frequency of blocked writes.

Comprehensive and Detailed Explanation From Exact Extract:

To add an automated test in AWS CodePipeline that checks the HTTP response code of a deployed application, the most straightforward and operationally efficient method is to create an AWS Lambda function that makes an HTTP request to the application URL, checks the response code, and then reports success or failure back to CodePipeline.

The Lambda function can be invoked as an action in the pipeline stage.

If the response code is not 200 OK, the Lambda function can return a failure, causing the pipeline stage to fail and halt further execution.

Option A’s CloudWatch synthetic canary monitors are useful for ongoing monitoring but are not natively integrated as CodePipeline actions to control pipeline flow.

Option C incorrectly uses CodeDeploy action for testing, which is not designed for this purpose.

Option D introduces unnecessary complexity by deploying API Gateway and Device Farm, which is not required for a simple HTTP status check.Therefore, option B provides a clean, simple, and fully automated way to enforce HTTP response validation within the pipeline.

Reference from AWS Official Documentation:

AWS CodePipeline Custom Actions:"You can create Lambda invoke actions in your pipeline stages to run custom validation or testing functions."(AWS CodePipeline User Guide - Lambda Actions)

AWS Lambda Function for Health Checks:"Lambda functions can be used to perform health checks or API response validations and report results back to invoking services."(AWS Lambda Developer Guide)

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-attribute-updatepolicy.html

Step 1: Handling Invalid Data with Failed Batch ItemsThe Lambda function is processing batches of messages, and some messages contain invalid data, causing processing delays. Lambda provides the capability to report failed batch items, which allows valid messages to be processed while skipping invalid ones. This functionality ensures that the valid messages are processed within the required timeline.

Action: Keep the Lambda function's batch size the same and configure it to report failed batch items.

Why: By reporting failed batch items, the Lambda function can skip invalid messages and continue processing valid ones, ensuring that they meet the processing timeline.

Option A is incorrect because creating an Amazon EventBridge rule that invokes an AWS Lambda function to query the applications on a 5-minute interval is not a valid solution. EventBridge rules can only trigger Lambda functions based on events, not on time intervals. Moreover, querying the applications on a 5-minute interval might incur unnecessary costs and network overhead, and might not detect performance issues in real time.

Option B is correct because creating an Amazon CloudWatch Synthetics canary that runs a custom script to query the applications on a 5-minute interval is a valid solution. CloudWatch Synthetics canaries are configurable scripts that monitor endpoints and APIs by simulating customer behavior. Canaries can run as often as once per minute, and can measure the latency and availability of the applications. Canaries can also send notifications to an Amazon SNS topic when they detect errors or performance issues1.

Option C is incorrect because creating an Amazon CloudWatch alarm that uses the AWS/ApplicationELB namespace RequestCountPerTarget metric is not a valid solution. The RequestCountPerTarget metric measures the number of requests completed or connections made per target in a target group2. This metric does not reflect the application response time, which is the requirement. Moreover, configuring the CloudWatch alarm to send a notification when the number of connections becomes greater than the configured number of threads that the application supports is not a valid way to measure the application performance, as it depends on the application design and implementation.

Option D is incorrect because creating an Amazon CloudWatch alarm that uses the AWS/ApplicationELB namespace RequestCountPerTarget metric is not a valid solution, for the same reason as option C. The RequestCountPerTarget metric does not reflect the application response time, which is the requirement. Moreover, configuring the CloudWatch alarm to send a notification when the average response time becomes greater than the longest response time that the application supports is not a valid way to measure the application performance, as it does not account for variability or outliers in the response time distribution.

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)

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

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 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

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.