A company deploys a web application on Amazon EC2 instances that are behind an Application Load Balancer (ALB). The company stores the application code in an AWS CodeConnections compatible Git repository.
When the company merges code to the main branch, an AWS CodeBuild project is initiated. The CodeBuild project compiles the code, stores the packaged code in AWS CodeArtifact, and invokes AWS Systems Manager Run Command to deploy the packaged code to the EC2 instances.
Previous deployments have resulted in defects, EC2 instances that were not running the latest version of the packaged code, and inconsistencies between instances. A DevOps engineer needs to improve the reliability of the deployment solution.
Which combination of actions will meet this requirement? (Select TWO.)
A. Create a pipeline in AWS CodePipeline that uses the Git repository as the source provider. Configure the pipeline to have parallel build and test stages. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action.
B. Create a pipeline in AWS CodePipeline that uses the Git repository as the source provider. Configure the pipeline to have a build stage followed by a test stage. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action.
C. Create an AWS CodeDeploy application and a deployment group to deploy the packaged code to the EC2 instances. Configure the ALB for the deployment group.
D. Create individual AWS Lambda functions that use AWS CodeDeploy instead of Systems Manager to run build, test, and deploy actions.
E. Create an Amazon S3 bucket. Modify the CodeBuild project to store the packages in the S3 bucket instead of in CodeArtifact. Use deploy actions in CodeDeploy to deploy.
Explanation:
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.
