Amazon Web Services DOP-C02 Exam With Confidence Using Practice Dumps

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.

To restrict access to CloudFront to a specific IP address range:

Create an AWS WAF IP address set with the corporate office IPs.

Modify the existing WebACL ' s default action to Block (deny all except explicitly allowed).

Add a high-priority rule that allows traffic from the IP address set (the corporate IPs). This way, only requests from the corporate IPs are allowed; all others are blocked. Regex pattern sets are not necessary for IP-based restrictions and add complexity. Setting default action to Allow with exceptions is less secure and more complex to manage.

The core problem described is deployment inconsistency and lack of reliability caused by using AWS Systems Manager Run Command for application deployment. Run Command executes ad hoc commands and does not provide deployment orchestration, version tracking, lifecycle hooks, or health-based traffic control, which commonly leads to drift between EC2 instances.

The most reliable AWS-native solution is to adopt AWS CodePipeline combined with AWS CodeDeploy . Option B introduces a structured CI/CD pipeline with a clear build stage followed by a test stage , ensuring that only tested artifacts progress to deployment. Sequential build and test stages are preferred for reliability and deterministic behavior, especially when test results must gate deployments.

Option C is essential because AWS CodeDeploy is the service specifically designed to deploy application revisions consistently across EC2 fleets. By creating a CodeDeploy application and deployment group and integrating it with the ALB, deployments gain support for lifecycle events, health checks, instance synchronization, and automatic rollback. This ensures that all EC2 instances receive the same application version and that traffic is managed safely during deployments.

Option A introduces unnecessary parallelism that does not address the core issue. Option D adds excessive complexity with Lambda orchestration. Option E incorrectly replaces CodeArtifact with S3 without addressing deployment reliability.

Therefore, combining CodePipeline (B) with CodeDeploy and ALB integration (C) provides consistent, repeatable, and reliable deployments aligned with AWS best practices.