DOP-C02 Exam Dumps - Amazon Web Services AWS Certified Professional Questions and Answers

This hook runs before the new application version is installed on the replacement instances. This is the best place to run the script because it ensures that the license file is downloaded and installed before the replacement instances start to handle request traffic. If you use any other hook, you may encounter errors or inconsistencies in your application.

Update the buildspec.yml file to log in to the ECR repository by using the aws ecr get-login-password AWS CLI command to obtain an authentica-tion token. Update the docker login command to use the authentication token to access the ECR repository.

This is the correct solution. The aws ecr get-login-password AWS CLI command retrieves and displays an authentication token that can be used to log in to an ECR repository. The docker login command can use this token as a password to authenticate with the ECR repository. This way, the CodeBuild project can push and pull images from the ECR repository without any errors. For more information, see Using Amazon ECR with the AWS CLI and get-login-password.

The Lambda function must call the API Gateway management API to flush a stage cache, so this is a control-plane API operation, not a data-plane API invoke operation. AWS documents that API Gateway includes a control plane for creating and managing APIs and a data plane for invoking deployed APIs. The PrivateLink service list also shows separate API Gateway service names: com.amazonaws.region.apigateway for API Gateway control-plane access and com.amazonaws.region.execute-api for API execution. Therefore, an interface VPC endpoint for the API Gateway control plane is required in the workload VPC. Option A is invalid because API Gateway does not use gateway endpoints. Option B targets only the data plane, and option C still relies on external routing rather than private AWS service access.

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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.”

The best solution to log and review all stopped tasks for errors is to use Amazon EventBridge and Amazon CloudWatch Logs. Amazon EventBridge allows the DevOps engineer to create a rule that matches task state change events from Amazon ECS. The rule can then send the event data to Amazon CloudWatch Logs as the target. Amazon CloudWatch Logs can store and monitor the log data, and also provide CloudWatch Logs Insights, a feature that enables the DevOps engineer to interactively search and analyze the log data. Using CloudWatch Logs Insights, the DevOps engineer can filter and aggregate the log data based on various fields, such as cluster, task, container, and reason. This way, the DevOps engineer can easily identify and investigate the stopped tasks and their errors.

The other options are not as effective or efficient as the solution in option A. Option B is not suitable because the embedded metric format is designed for custom metrics, not for logging task state changes. Option C is not feasible because the EC2 instances do not store the task state change events in their logs. Option D is not relevant because the EC2_INSTANCE_TERMINATING lifecycle hook is triggered when an EC2 instance is terminated by the Auto Scaling group, not when a task is stopped by Amazon ECS.

Creating a CloudWatch Events Rule That Triggers on an Event - Amazon Elastic Container Service

Sending and Receiving Events Between AWS Accounts - Amazon EventBridge

Working with Log Data - Amazon CloudWatch Logs

Analyzing Log Data with CloudWatch Logs Insights - Amazon CloudWatch Logs

Embedded Metric Format - Amazon CloudWatch

Amazon EC2 Auto Scaling Lifecycle Hooks - Amazon EC2 Auto Scaling

Step 1: Reacting to RDS Storage Autoscaling Events Using Amazon EventBridgeAmazon RDS emits events when storage autoscaling occurs. To visualize these events in a CloudWatch dashboard, you can create an EventBridge rule that listens for these specific autoscaling events.

Action: Create an EventBridge rule that reacts to RDS storage autoscaling events from the RDS event stream.

Why: EventBridge allows you to listen to RDS events and route them to specific AWS services for processing.

Step 2: Creating a Custom CloudWatch Metric via LambdaOnce the EventBridge rule detects a storage autoscaling event, you can use a Lambda function to publish a custom metric to CloudWatch. This metric can then be visualized in a CloudWatch dashboard.

Action: Use a Lambda function to publish custom metrics to CloudWatch based on the RDS storage autoscaling events.

Why: Custom metrics allow you to track specific events like autoscaling and visualize them easily on a CloudWatch dashboard.

CodePipeline needs an explicit gate between regional deployment stages. AWS supports a Step Functions invoke action in CodePipeline, which starts a state machine execution from the pipeline. The state machine can check the CloudWatch alarm or Route 53 health-check-derived state and fail if the alarm is in ALARM state. A failed invoke action stops the pipeline before the next Region receives the deployment, which is exactly the required sequential health validation. Route 53 health checks can be associated with CloudWatch alarms, so the alarm is already a valid health signal. Option B replaces the deployment model unnecessarily. Options C and D refer to a non-existent native “CloudWatch alarm action” for CodePipeline stages and do not provide a real pipeline gate.

A- https://docs.aws.amazon.com/codedeploy/latest/userguide/codedeploy-agent.html D - This option correctly utilizes AWS CodePipeline to invoke the CodeBuild job and create CloudFormation change sets. It adds a manual approval step before executing the change sets and starting the AWS CodeDeploy deployment. This ensures that the deployment process is automated while retaining the final manual approval step.

https://docs.aws.amazon.com/glue/latest/dg/components-overview.html