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AWS Certified DevOps Engineer - Professional Questions and Answers

Question 1

A company uses AWS CloudFormation stacks to deploy updates to its application. The stacks consist of different resources. The resources include AWS Auto Scaling groups, Amazon EC2 instances, Application Load Balancers (ALBs), and other resources that are necessary to launch and maintain independent stacks. Changes to application resources outside of CloudFormation stack updates are not allowed.

The company recently attempted to update the application stack by using the AWS CLI. The stack failed to update and produced the following error message: "ERROR: both the deployment and the CloudFormation stack rollback failed. The deployment failed because the following resource(s) failed to update: [AutoScalingGroup]."

The stack remains in a status of UPDATE_ROLLBACK_FAILED. *

Which solution will resolve this issue?

Options:

Update the subnet mappings that are configured for the ALBs. Run the aws cloudformation update-stack-set AWS CLI command.

Update the 1AM role by providing the necessary permissions to update the stack. Run the aws cloudformation continue-update-rollback AWS CLI command.

Submit a request for a quota increase for the number of EC2 instances for the account. Run the aws cloudformation cancel-update-stack AWS CLI command.

Delete the Auto Scaling group resource. Run the aws cloudformation rollback-stack AWS CLI command.

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Question 2

A company has an organization in AWS Organizations. A DevOps engineer needs to maintain multiple AWS accounts that belong to different OUs in the organization. All resources, including 1AM policies and Amazon S3 policies within an account, are deployed through AWS CloudFormation. All templates and code are maintained in an AWS CodeCommit repository Recently, some developers have not been able to access an S3 bucket from some accounts in the organization.

The following policy is attached to the S3 bucket.

What should the DevOps engineer do to resolve this access issue?

Options:

Modify the S3 bucket policy Turn off the S3 Block Public Access setting on the S3 bucket In the S3 policy, add the awsSourceAccount condition. Add the AWS account IDs of all developers who are experiencing the issue.

Verify that no 1AM permissions boundaries are denying developers access to the S3 bucket Make the necessary changes to IAM permissions boundaries. Use an AWS Config recorder in the individual developer accounts that are experiencing the issue to revert any changes that are blocking access. Commit the fix back into the CodeCommit repository. Invoke deployment through Cloud Formation to apply the changes.

Configure an SCP that stops anyone from modifying 1AM resources in developer OUs. In the S3 policy, add the awsSourceAccount condition. Add the AWS account IDs of all developers who are experiencing the issue Commit the fix back into the CodeCommit repository Invoke deployment through CloudFormation to apply the changes

Ensure that no SCP is blocking access for developers to the S3 bucket Ensure that no 1AM policy permissions boundaries are denying access to developer 1AM users Make the necessary changes to the SCP and 1AM policy permissions boundaries in the CodeCommit repository Invoke deployment through CloudFormation to apply the changes

Question 3

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?

Options:

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.

Reduce the Lambda function's batch size. Increase the SOS message throughput quota. Request a Lambda concurrency increase in the AWS Region.

Increase the Lambda function's batch size. Configure S3 Transfer Acceleration on the S3 bucket. Configure an SOS dead-letter queue.

Keep the Lambda function's batch size the same. Configure the Lambda function to report failed batch items. Configure an SOS dead-letter queue.

Answer:

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., , , , ]

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AWS Certified DevOps Engineer - Professional Questions and Answers

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