20+ practice questions focused on SDLC Automation — one of the most tested topics on the AWS Certified DevOps Engineer Professional DOP-C02 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start SDLC Automation PracticeA company uses AWS CodePipeline with a multi-branch strategy. A new feature branch triggers a pipeline that runs unit tests and deploys to a test environment. The deployment step uses AWS CodeDeploy with a deployment group configured for in-place deployment to Amazon EC2 instances. The deployment fails intermittently with the error 'The overall deployment failed because too many individual instances failed deployment, too few healthy instances are available for deployment, or some instances in your deployment group are experiencing problems.' The instances are healthy and pass health checks. What is the most likely cause?
Explanation: Option D is correct because CodeDeploy enforces a per-deployment-group concurrency limit of one deployment at a time. If a previous deployment is still in progress or in a 'frozen' state (e.g., due to a failed or stopped deployment that hasn't been explicitly rolled back or cleaned up), new deployments will fail with the 'too many individual instances failed' error even when instances are healthy. The error message is misleading because it reflects CodeDeploy's inability to proceed with the new deployment, not actual instance health issues.
A development team uses AWS CodeBuild to compile a Java application and run unit tests. The build takes 30 minutes, but the team wants to reduce build time. The codebase has not changed significantly, and dependencies are stable. Which action would be MOST effective in reducing build time?
Explanation: Caching dependencies in an Amazon S3 bucket allows CodeBuild to reuse previously downloaded Maven/Gradle dependencies across builds, eliminating the need to re-download them each time. Since the codebase and dependencies are stable, this directly reduces the build time by avoiding repeated network transfers of large artifact repositories.
A company uses AWS CodePipeline with multiple stages: Source (Amazon S3), Build (AWS CodeBuild), and Deploy (AWS CodeDeploy). The build stage runs a series of tests, and if they pass, the pipeline proceeds to deploy. Recently, a developer committed a change that passed all tests but caused a production outage. The team wants to add an approval step before the deploy stage, but they also want to ensure that only changes from specific branches can be deployed. What is the MOST secure and maintainable way to enforce this?
Explanation: Option C is correct because it enforces branch-based deployment at the pipeline level, ensuring that only changes from specific branches trigger the production pipeline with the approval step. This approach is secure and maintainable as it leverages AWS CodePipeline's native ability to trigger on branch events, avoiding custom logic or manual verification. By isolating production deployments to a dedicated pipeline, the team reduces the risk of unauthorized or untested code reaching production.
A company uses AWS CodeCommit for source control. Developers frequently push large binary files (e.g., compiled JARs) to the repository, causing the repository size to grow rapidly and slowing down clone operations. The team wants to enforce a policy to reject pushes that contain files larger than 50 MB. Which approach should be used?
Explanation: Option A is correct because AWS CodeCommit supports custom triggers that invoke AWS Lambda functions on repository events, including pushes. By configuring a trigger for the 'push' event, a Lambda function can inspect each file in the push payload, check its size against the 50 MB threshold, and programmatically reject the push by returning an error response. This approach enforces the policy at the repository level without requiring client-side changes.
An organization uses AWS CodePipeline to orchestrate deployments to multiple environments (dev, test, prod). Each environment uses a different AWS account. The pipeline uses cross-account actions with IAM roles. Recently, the pipeline failed at the deploy stage for the prod account with the error 'Access Denied' when assuming the cross-account role. The role ARN is correct and the trust policy allows the pipeline's service role. What is the MOST likely cause?
Explanation: The pipeline's service role must have an `sts:AssumeRole` permission on the cross-account role to perform the role assumption. Even if the trust policy on the cross-account role allows the pipeline's service role, the pipeline's service role itself needs an IAM policy granting `sts:AssumeRole` for the cross-account role ARN. Without this permission, the `AssumeRole` API call fails with 'Access Denied', which is the exact error described.
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Practice all SDLC Automation questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of SDLC Automation. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
SDLC Automation questions on the DOP-C02 frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. SDLC Automation is tested as part of the AWS Certified DevOps Engineer Professional DOP-C02 blueprint. Practicing with targeted SDLC Automation questions ensures you can handle any format or difficulty that appears.
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Difficulty is subjective, but SDLC Automation is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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