DOP-C02 Exam Dumps - AWS Certified DevOps Engineer - Professional

Step 1: Understanding the Deletion FailureThe most likely reason why the CloudFormation stack failed to delete is that the S3 bucket was not empty. AWS CloudFormation cannot delete an S3 bucket that contains objects, so if the website files are still in the bucket, the deletion will fail.

Issue: The S3 bucket is not empty during deletion, preventing the stack from being deleted.

Step 2: Modifying the Custom Resource to Handle DeletionTo mitigate this issue, you can modify the Lambda function associated with the custom resource to automatically empty the S3 bucket when the stack is being deleted. By adding logic to handle the RequestType: Delete event, the function can recursively delete all objects in the bucket before allowing the stack to be deleted.

Action: Modify the Lambda function to recursively delete the objects in the S3 bucket when RequestType is set to Delete.

Why: This ensures that the S3 bucket is empty before CloudFormation tries to delete it, preventing the stack deletion failure.

The requirement is to prevent any product files containing unannounced product IDs (prefixed with a specific UUID) from being stored in the production S3 bucket that users can access.

To achieve this, a best practice is to use a staging bucket as a control point before files go to production, combined with Amazon Macie’s data classification capabilities.

Creating a custom data identifier in Amazon Macie allows precise detection of product IDs starting with the specific UUID, which default managed identifiers will not detect.

By running a Macie sensitive data discovery job on the staging bucket, you can identify files containing these sensitive product IDs.

Only files without findings (i.e., files that do not contain unannounced product IDs) are copied to the production bucket, ensuring no sensitive information is exposed. This approach aligns with AWS best practices for data classification and staged deployment workflows, maximizing control and reducing risk. Using Macie on the production bucket directly (options B and D) risks exposing sensitive data before detection and deletion. Option C uses managed data identifiers, which will likely not detect the custom UUID prefix pattern.

Reference from AWS Official Documentation and Study Guide:

Amazon Macie Custom Data Identifiers: " You can create custom data identifiers in Amazon Macie to find sensitive data that is unique to your organization. " (Amazon Macie User Guide)

Data Security Best Practices: " Use staging environments to inspect and sanitize data before moving it to production to reduce exposure risks. " (AWS Security Best Practices)

https://docs.aws.amazon.com/autoscaling/ec2/userguide/ASGettingNotifications.html#auto-scaling-sns-notifications

To meet the requirements, the DevOps team needs to configure a monitoring solution for the VPC flow logs that can detect anomalies in network traffic over time and initiate a response to the anomaly. The DevOps team can use Amazon Kinesis Data Streams to ingest and process streaming data from CloudWatch Logs. The DevOps team can subscribe the log group to a Kinesis data stream, which will deliver log events from CloudWatch Logs to Kinesis Data Streams in near real-time. The DevOps team can then create an AWS Lambda function to detect log anomalies using machine learning or statistical methods. The Lambda function can be set as a processor for the data stream, which means that it will process each record from the stream before sending it to downstream applications or destinations. The Lambda function can also write to the default Amazon EventBridge event bus if it detects an anomaly, which will allow other AWS services or custom applications to respond to the anomaly event.

To invoke many Lambda functions in parallel and allow each function to have independent retry logic and concurrency management, using SQS queues for each Lambda function is recommended.

The BeginResponse Lambda publishes a message to an SNS topic, which fans out to multiple SQS queues (one per Lambda).

Each Lambda function polls its own SQS queue, allowing fine-grained control of concurrency and retry behavior.

SNS alone (Option A) invokes Lambda functions but lacks the queue’s buffering and retry durability.

Step Functions (Option D) would invoke Lambdas sequentially, not in parallel, and add complexity.

Option C reverses SNS and SQS in an inefficient manner.

To allow only creation/configuration of service-linked roles , you need a way to tightly scope what IAM actions the developer can perform. The most AWS-appropriate mechanism for “delegate limited IAM admin” is a role with a permissions boundary :

A permissions boundary sets the maximum permissions the role (and any roles it creates, depending on design) can ever have. This is a standard pattern to safely delegate IAM tasks without granting broad IAM administration.

You can define the role’s policy + boundary so the developer can call only the APIs required for service-linked roles (for example actions like creating service-linked roles and passing only allowed AWS service principals), while preventing general role/policy creation outside that scope.

Why the others don’t meet “service-linked roles only”:

A is vague (“common services”) and cross-account access doesn’t inherently restrict to only service-linked roles. It’s also more operational overhead than needed for a single dev account use case.

B PowerUserAccess is far too broad and does not restrict to service-linked roles.

C An SCP with Deny iam:* would block IAM entirely (including what the developer needs). Even if refined, SCPs set account-wide guardrails and are not the right tool to grant a single developer the precise ability to create only service-linked roles.

Option B is the only choice that directly satisfies both requirements:

Continuously evaluate the account-level S3 Block Public Access setting

AWS Config is designed to record configuration state and evaluate resources/settings against rules over time.

A Config rule (managed rule) can check whether the account-level “S3 Block Public Access” settings are configured as required (i.e., blocking public access).

Automatically revert drift (auto-remediate) if someone changes the setting later

AWS Config Remediation can automatically trigger an AWS Systems Manager Automation runbook when the rule becomes NON_COMPLIANT .

Using an SSM Automation document/runbook that sets S3 account-level Block Public Access back to the required “blocked” configuration ensures that any future change is corrected automatically , restoring compliance without manual intervention.

Why the other options don’t fully meet the requirement:

A (Security Hub) : Security Hub primarily aggregates findings and checks controls. While it can integrate with automation, AWS Config is the standard service for configuration drift detection + automatic remediation loops for account-level posture settings. Security Hub is not the most direct “detect config drift and auto-fix” mechanism for an account setting in the way Config remediation is.

C (SCP) : An SCP can restrict API actions, but it doesn’t “revert” a changed S3 Block Public Access configuration ; it only prevents/limits what actions can be called. Also, “deny S3 actions from outside the account” is not the same as enforcing Block Public Access settings at the account level.

D (Macie + EventBridge) : Macie focuses on data discovery and sensitive data findings , not enforcing or continuously remediating S3 account-level Block Public Access configuration drift. Triggering remediation off Macie findings is indirect and not aligned to “setting changed → immediately revert.”

AWS Organizations is a service that helps to manage multiple AWS accounts. AWS Control Tower is a service that makes it easy to set up and govern secure, compliant multi-account AWS environments. Account Factory for Terraform (AFT) is an AWS Control Tower feature that provisions new accounts using Terraform templates. To provision new accounts with the Enterprise Support plan, the DevOps engineer can set the aft_feature_enterprise_support feature flag to True in the AFT deployment input configuration. This flag enables the Enterprise Support plan for newly provisioned accounts.

https://docs.aws.amazon.com/controltower/latest/userguide/aft-feature-options.html