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To earn the Amazon DOP-C02 certification, candidates must pass a challenging two-part exam that covers a range of topics related to DevOps and AWS. The first part of the exam focuses on core DevOps concepts, such as continuous integration, continuous delivery, and infrastructure as code. The second part of the exam tests candidates on their knowledge of AWS services and how they can be used to implement DevOps practices effectively.

The AWS Certified DevOps Engineer – Professional certification is highly valued in the industry and is recognized by many organizations. AWS Certified DevOps Engineer - Professional certification provides many opportunities for professionals to advance their careers and work on complex AWS projects. AWS Certified DevOps Engineer - Professional certification also helps professionals to enhance their knowledge and skills in DevOps practices and AWS services.

To prepare for the DOP-C02 Exam, candidates should have a solid understanding of DevOps principles and practices, as well as experience working with AWS services and tools. Amazon recommends that candidates have at least two years of experience in a DevOps role and a strong understanding of programming languages and scripting. Candidates can also take advantage of AWS training and certification resources, including online courses, practice exams, and instructor-led training, to prepare for the exam and enhance their skills and knowledge in DevOps and AWS.

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q76-Q81):

NEW QUESTION # 76
A software engineering team is using AWS CodeDeploy to deploy a new version of an application. The team wants to ensure that if any issues arise during the deployment, the process can automatically roll back to the previous version.
During the deployment process, a health check confirms the application's stability. If the health check fails, the deployment must revert automatically.
Which solution will meet these requirements?

A. Configure automatic rollback settings in AWS CodeDeploy.
B. Implement lifecycle event hooks in the deployment configuration.
C. Set up alarms in Amazon CloudWatch to start a rollback.
D. Use AWS CloudFormation to monitor the health of the deployment.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
AWS CodeDeploy supports automatic rollback capabilities, which allow the deployment to automatically revert to the last known good revision if a failure occurs during the deployment or if health checks fail.
You can enable automatic rollback on CodeDeploy deployments by configuring rollback settings in the deployment group.
Automatic rollback triggers can include failed deployments and alarms failing during the deployment lifecycle, which means if the health check fails, CodeDeploy will automatically roll back without manual intervention.
Lifecycle event hooks (Option A) provide custom scripting capabilities during deployment but do not provide built-in rollback automation.
CloudFormation (Option B) can deploy and manage infrastructure but does not control CodeDeploy rollback.
CloudWatch alarms (Option C) can trigger notifications but require manual or additional automation to trigger rollback.
Hence, enabling CodeDeploy's automatic rollback settings is the simplest and most reliable method for automatic rollback upon health check failure.
Reference from AWS Official Documentation and Study Guide:
AWS CodeDeploy Automatic Rollback:
"You can configure automatic rollback in AWS CodeDeploy to roll back a deployment if it fails or if specified CloudWatch alarms are triggered." (AWS CodeDeploy User Guide - Automatic Rollbacks) AWS Certified DevOps Engineer Professional Study Guide:
"Leverage CodeDeploy automatic rollback features to reduce downtime and ensure application stability during deployments."

NEW QUESTION # 77
A company configured an Amazon S3 event source for an AWS Lambda function. The company needs the Lambda function to run when a new object is created or an existing object is modified in a specific S3 bucket.
The Lambda function will use the S3 bucket name and the S3 object key of the incoming event to read the contents of the new or modified S3 object. The Lambda function will parse the contents and save the parsed contents to an Amazon DynamoDB table.
The Lambda function's execution role has permissions to 'eari from the S3 bucket and to Write to the DynamoDB table. During testing, a DevOpS engineer discovers that the Lambda fund on does rot run when objects are added to the S3 bucket or when existing objects are modified.
Which solution will resolve these problems?

A. Configure an Amazon Simple Queue Service (Amazon SQS) queue as an OnFailure destination for the Lambda function. Update the Lambda function to process messages from the SQS queue and the S3 event notifications.
B. Create an S3 bucket policy for the S3 bucket that grants the S3 bucket permission to invoke the Lambda function.
C. Create a resource policy for the Lambda function to grant Amazon S3 permission to invoke the Lambda function on the S3 bucket.
D. Configure an Amazon Simple Queue Service (Amazon SQS) queue as the destination for the S3 bucket event notifications. Update the Lambda function's execution role to have permission to read from the SQS queue. Update the Lambda function to consume messages from the SQS queue.

Answer: A

NEW QUESTION # 78
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 CodeConnections compatible Git repository.
When the company merges code to the main branch, an AWS CodeBuild project is initiated. The CodeBuild project compiles 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 were not running the latest version of the packaged code, and inconsistencies between instances. A DevOps engineer needs to improve the reliability of the deployment solution.
Which combination of actions will meet this requirement? (Select TWO.)

A. Create individual AWS Lambda functions that use AWS CodeDeploy instead of Systems Manager to run build, test, and deploy actions.
B. 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.
C. Create a pipeline in AWS CodePipeline that uses the Git repository as the source provider. Configure the pipeline to have a build stage followed by a test stage. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action.
D. Create a pipeline in AWS CodePipeline that uses the Git repository as the source provider. Configure the pipeline to have parallel build and test stages. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action.
E. 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.

Answer: C,E

Explanation:
The core problem described is deployment inconsistency and lack of reliability caused by using AWS Systems Manager Run Command for application deployment. Run Command executes ad hoc commands and does not provide deployment orchestration, version tracking, lifecycle hooks, or health-based traffic control, which commonly leads to drift between EC2 instances.
The most reliable AWS-native solution is to adopt AWS CodePipeline combined with AWS CodeDeploy.
Option B introduces a structured CI/CD pipeline with a clear build stage followed by a test stage, ensuring that only tested artifacts progress to deployment. Sequential build and test stages are preferred for reliability and deterministic behavior, especially when test results must gate deployments.
Option C is essential because AWS CodeDeploy is the service specifically designed to deploy application revisions consistently across EC2 fleets. By creating a CodeDeploy application and deployment group and integrating it with the ALB, deployments gain support for lifecycle events, health checks, instance synchronization, and automatic rollback. This ensures that all EC2 instances receive the same application version and that traffic is managed safely during deployments.
Option A introduces unnecessary parallelism that does not address the core issue. Option D adds excessive complexity with Lambda orchestration. Option E incorrectly replaces CodeArtifact with S3 without addressing deployment reliability.
Therefore, combining CodePipeline (B) with CodeDeploy and ALB integration (C) provides consistent, repeatable, and reliable deployments aligned with AWS best practices.

NEW QUESTION # 79
A company releases a new application in a new AWS account. The application includes an AWS Lambda function that processes messages from an Amazon Simple Queue Service (Amazon SOS) standard queue. The Lambda function stores the results in an Amazon S3 bucket for further downstream processing. The Lambda function needs to process the messages within a specific period of time after the messages are published. The Lambda function has a batch size of 10 messages and takes a few seconds to process a batch of messages.
As load increases on the application's first day of service, messages in the queue accumulate at a greater rate than the Lambda function can process the messages. Some messages miss the required processing timelines.
The logs show that many messages in the queue have data that is not valid. The company needs to meet the timeline requirements for messages that have valid data.
Which solution will meet these requirements?

A. Reduce the Lambda function's batch size. Increase the SOS message throughput quota. Request a Lambda concurrency increase in the AWS Region.
B. Keep the Lambda function's batch size the same. Configure the Lambda function to report failed batch items. Configure an SOS dead-letter queue.
C. Increase the Lambda function's batch size. Configure S3 Transfer Acceleration on the S3 bucket.
Configure an SOS dead-letter queue.
D. Increase the Lambda function's batch size. Change the SOS standard queue to an SOS FIFO queue.
Request a Lambda concurrency increase in the AWS Region.

Answer: B

Explanation:
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.
Reference: AWS documentation on Lambda batch item failure handling.
Step 2: Using an SQS Dead-Letter Queue (DLQ)Configuring a dead-letter queue (DLQ) for SQS will ensure that messages with invalid data, or those that cannot be processed successfully, are moved to the DLQ. This prevents such messages from clogging the queue and allows the system to focus on processing valid messages.
Action: Configure an SQS dead-letter queue for the main queue.
Why: A DLQ helps isolate problematic messages, preventing them from continuously reappearing in the queue and causing processing delays for valid messages.
Reference: AWS documentation on Amazon SQS Dead-Letter Queues.
Step 3: Maintaining the Lambda Function's Batch SizeKeeping the current batch size allows the Lambda function to continue processing multiple messages at once. By addressing the failed items separately, there's no need to increase or reduce the batch size.
Action: Maintain the Lambda function's current batch size.
Why: Changing the batch size is unnecessary if the invalid messages are properly handled by reporting failed items and using a DLQ.
This corresponds to Option D: Keep the Lambda function's batch size the same. Configure the Lambda function to report failed batch items. Configure an SQS dead-letter queue.

NEW QUESTION # 80
A DevOps engineer needs to implement integration tests into an existing AWS CodePipelme CI/CD workflow for an Amazon Elastic Container Service (Amazon ECS) service. The CI/CD workflow retrieves new application code from an AWS CodeCommit repository and builds a container image. The CI/CD workflow then uploads the container image to Amazon Elastic Container Registry (Amazon ECR) with a new image tag version.
The integration tests must ensure that new versions of the service endpoint are reachable and that vanous API methods return successful response data The DevOps engineer has already created an ECS cluster to test the service Which combination of steps will meet these requirements with the LEAST management overhead? (Select THREE.)

A. Update the image build pipeline stage to output an imagedefinitions json file that references the new image tag.
B. Add a deploy stage to the pipeline Configure Amazon ECS as the action provider
C. Add an appspec.yml file to the CodeCommit repository
D. Write a script that runs integration tests against the service. Upload the script to an Amazon S3 bucket.
Integrate the script in the S3 bucket with CodePipeline by using an S3 action stage.
E. Create an AWS Lambda function that runs connectivity checks and API calls against the service.
Integrate the Lambda function with CodePipeline by using aLambda action stage
F. Add a deploy stage to the pipeline Configure AWS CodeDeploy as the action provider

Answer: A,B,E

Explanation:
Add a Deploy Stage to the Pipeline, Configure Amazon ECS as the Action Provider:
By adding a deploy stage to the pipeline and configuring Amazon ECS as the action provider, the pipeline can automatically deploy the new container image to the ECS cluster.
This ensures that the service is updated with the new image tag, making the new version of the service endpoint reachable.
Reference: AWS CodePipeline with ECS
Update the Image Build Pipeline Stage to Output an imagedefinitions.json File that References the New Image Tag:
The imagedefinitions.json file provides the necessary information about the container images and their tags for the ECS task definitions.
Updating the pipeline to output this file ensures that the correct image version is deployed.
Example imagedefinitions.json
[
{
"name": "container-name",
"imageUri": "123456789012.dkr.ecr.region.amazonaws.com/my-repo:my-tag"
}
]
Reference: CodePipeline ECS Deployment
Create an AWS Lambda Function that Runs Connectivity Checks and API Calls against the Service.
Integrate the Lambda Function with CodePipeline by Using a Lambda Action Stage:
The Lambda function can perform the necessary integration tests by making connectivity checks and API calls to the deployed service endpoint.
Integrating this Lambda function into CodePipeline ensures that these tests are run automatically after deployment, providing near-real-time feedback on the new deployment's health.
Example Lambda function integration:
actions:
- name: TestService
actionTypeId:
category: Test
owner: AWS
provider: Lambda
version: 1
runOrder: 2
configuration:
FunctionName: testServiceFunction
Reference: Integrating Lambda with CodePipeline
These steps ensure that the CI/CD workflow deploys the new container image to ECS, updates the image references, and performs integration tests, meeting the requirements with minimal management overhead.

NEW QUESTION # 81
......

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