Amazon DOP-C02 Exam Questions

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.

Step 3: Maintaining the Lambda Function's Batch Size Keeping 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.

Step 2: Creating CloudWatch Metric Filters CloudWatch metric filters can be used to search for specific patterns in log data. The operations team can create filters for certain log patterns and set up alarms based on these filters.

Action: Instruct the operations team to create CloudWatch metric filters to detect patterns in the WAF log output.

Why: Metric filters allow the team to trigger alarms based on specific patterns without needing to manually search through logs.

This corresponds to Option A: Create an Amazon CloudWatch Logs log group. Configure the appropriate AWS WAF web ACL to send log messages to the log group. Instruct the operations team to create CloudWatch metric filters.

This corresponds to Option B: Add the AWS::CodeDeployBlueGreen transform and the AWS::CodeDeploy::BlueGreen hook parameter with the CodeDeployDefault.ECSLinear10PercentEvery1Minutes deployment configuration.

Step 2: Creating CloudWatch Metric Filters CloudWatch metric filters can be used to search for specific patterns in log data. The operations team can create filters for certain log patterns and set up alarms based on these filters.

Action: Instruct the operations team to create CloudWatch metric filters to detect patterns in the WAF log output.

Why: Metric filters allow the team to trigger alarms based on specific patterns without needing to manually search through logs.

This corresponds to Option A: Create an Amazon CloudWatch Logs log group. Configure the appropriate AWS WAF web ACL to send log messages to the log group. Instruct the operations team to create CloudWatch metric filters.

* Modify the Build Stage to Add a Test Action with a RunOrder Value of 2:

The build stage in AWS CodePipeline can have multiple actions. By adding a test action with a runOrder value of 2, the test action will execute after the initial build action completes.

* Use AWS CodeBuild as the Action Provider to Run Unit Tests:

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages.

Using CodeBuild to run unit tests ensures that the tests are executed in a controlled environment and that only the code changes that pass the unit tests proceed to the deploy stage.

Example configuration in CodePipeline:

{

'name': 'BuildStage',

'actions': [

{

'name': 'Build',

'actionTypeId': {

'category': 'Build',

'owner': 'AWS',

'provider': 'CodeBuild',

'version': '1'

},

'runOrder': 1

},

{

'name': 'Test',

'actionTypeId': {

'category': 'Test',

'owner': 'AWS',

'provider': 'CodeBuild',

'version': '1'

},

'runOrder': 2

}

]

}

By integrating the unit tests into the build stage and ensuring they run after the build process, the pipeline guarantees that only code changes passing all unit tests are deployed.

AWS CodePipeline

AWS CodeBuild

Using CodeBuild with CodePipeline