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

AWS CodeDeploy supports deployment of both Lambda functions and ECS services with native integration.

It provides deployment strategies such as canary deployments for Lambda and blue/green deployments for ECS, which minimize downtime and risk.

CodeDeploy automates the deployment process, which reduces manual effort and improves consistency.

AWS Systems Manager Parameter Store is a simple, managed solution for storing configuration data securely and at scale.

Option A uses CloudFormation stack updates with resource replacement, which can cause downtime and is less flexible for minimizing downtime compared to CodeDeploy’s deployment strategies.

Option C adds unnecessary orchestration complexity with Step Functions and cross-region deployments, increasing deployment effort.

Option D’s all-at-once deployments for Lambda and rolling updates for ECS do not minimize downtime as effectively as canary and blue/green strategies. Hence, option B meets the requirement with the least deployment effort and maximizes automation and availability.

Reference from AWS Official Documentation:

Deploying Lambda Functions with CodeDeploy: " AWS CodeDeploy supports canary and linear deployment strategies for AWS Lambda functions to reduce deployment risk. " (CodeDeploy Lambda Deployment)

Deploying ECS Services with CodeDeploy Blue/Green: " CodeDeploy supports blue/green deployments for ECS to minimize downtime during service updates. " (CodeDeploy ECS Blue/Green)

AWS Systems Manager Parameter Store: " Parameter Store provides secure, hierarchical storage for configuration data management. " (Systems Manager Parameter Store)

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.

AWS CodePipeline supports multiple stages including source, build, test, and deploy. The most efficient way to integrate automated testing is by adding an AWS CodeBuild action to the pipeline that runs the tests using a buildspec.yml file. CodeBuild can be configured to fail the pipeline automatically if tests fail, ensuring that deployments do not proceed to the staging environment. This pattern is directly supported and documented in AWS CodePipeline + CodeBuild CI/CD architecture guidance.

https://aws.amazon.com/blogs/mt/aws-config-auto-remediation-s3-compliance/ https://aws.amazon.com/blogs/aws/aws-config-rules-dynamic-compliance-checking-for-cloud-resources/