Amazon Web Services DOP-C02 Actual Questions

AWS Certified DevOps Engineer - Professional Questions and Answers

Question 29

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.)

Options:

Add a deploy stage to the pipeline Configure Amazon ECS as the action provider

Add a deploy stage to the pipeline Configure AWS CodeDeploy as the action provider

Add an appspec.yml file to the CodeCommit repository

Update the image build pipeline stage to output an imagedefinitions json file that references the new image tag.

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

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.

Answer:

A, D, 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., , , , ]

Question 30

A company is developing a web application that runs on Amazon EC2 Linux instances. The application requires monitoring of custom performance metrics. The company must collect metrics for API response times and database query latency across multiple instances. Which solution will generate the custom metrics with the LEAST operational overhead?

Options:

Install the Amazon CloudWatch agent on the instances. Configure the agent to collect the custom metrics. Instrument the application to send the metrics to the agent.

Use Amazon Managed Service for Prometheus to scrape the custom metrics from the application. Use the Amazon CloudWatch agent to forward the metrics to CloudWatch.

Create a custom AWS Lambda function that polls the application endpoints and database at regular intervals. Program the Lambda function to calculate the custom metrics and to send the metrics to Amazon CloudWatch by using PutMetricData API calls.

Implement custom logging in the application code to record the custom metrics. Use Amazon CloudWatch Logs Insights to extract and analyze the metrics.

Question 31

A space exploration company receives telemetry data from multiple satellites. Small packets of data are received through Amazon API Gateway and are placed directly into an Amazon Simple Queue Service (Amazon SQS) standard queue. A custom application is subscribed to the queue and transforms the data into a standard format.

Because of inconsistencies in the data that the satellites produce, the application is occasionally unable to transform the data. In these cases, the messages remain in the SQS queue. A DevOps engineer must develop a solution that retains the failed messages and makes them available to scientists for review and future processing.

Which solution will meet these requirements?

Options:

Configure AWS Lambda to poll the SQS queue and invoke a Lambda function to check whether the queue messages are valid. If validation fails, send a copy of the data that is not valid to an Amazon S3 bucket so that the scientists can review and correct the data. When the data is corrected, amend the message in the SQS queue by using a replay Lambda function with the corrected data.

Convert the SQS standard queue to an SQS FIFO queue. Configure AWS Lambda to poll the SQS queue every 10 minutes by using an Amazon EventBridge schedule. Invoke the Lambda function to identify any messages with a SentTimestamp value that is older than 5 minutes, push the data to the same location as the application's output location, and remove the messages from the queue.

Create an SQS dead-letter queue. Modify the existing queue by including a redrive policy that sets the Maximum Receives setting to 1 and sets the dead-letter queue ARN to the ARN of the newly created queue. Instruct the scientists to use the dead-letter queue to review the data that is not valid. Reprocess this data at a later time.

Configure API Gateway to send messages to different SQS virtual queues that are named for each of the satellites. Update the application to use a new virtual queue for any data that it cannot transform, and send the message to the new virtual queue. Instruct the scientists to use the virtual queue to review the data that is not valid. Reprocess this data at a later time.

Question 32

A company has a mobile application that makes HTTP API calls to an Application Load Balancer (ALB). The ALB routes requests to an AWS Lambda function. Many different versions of the application are in use at any given time, including versions that are in testing by a subset of users. The version of the application is defined in the user-agent header that is sent with all requests to the API.

After a series of recent changes to the API, the company has observed issues with the application. The company needs to gather a metric for each API operation by response code for each version of the application that is in use. A DevOps engineer has modified the Lambda function to extract the API operation name, version information from the user-agent header and response code.

Which additional set of actions should the DevOps engineer take to gather the required metrics?

Options:

Modify the Lambda function to write the API operation name, response code, and version number as a log line to an Amazon CloudWatch Logs log group. Configure a CloudWatch Logs metric filter that increments a metric for each API operation name. Specify response code and application version as dimensions for the metric.

Modify the Lambda function to write the API operation name, response code, and version number as a log line to an Amazon CloudWatch Logs log group. Configure a CloudWatch Logs Insights query to populate CloudWatch metrics from the log lines. Specify response code and application version as dimensions for the metric.

Configure the ALB access logs to write to an Amazon CloudWatch Logs log group. Modify the Lambda function to respond to the ALB with the API operation name, response code, and version number as response metadata. Configure a CloudWatch Logs metric filter that increments a metric for each API operation name. Specify response code and application version as dimensions for the metric.

Configure AWS X-Ray integration on the Lambda function. Modify the Lambda function to create an X-Ray subsegment with the API operation name, response code, and version number. Configure X-Ray insights to extract an aggregated metric for each API operation name and to publish the metric to Amazon CloudWatch. Specify response code and application version as dimensions for the metric.

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Amazon Web Services DOP-C02 Actual Questions

AWS Certified DevOps Engineer - Professional Questions and Answers

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