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: The correct answer is B. The error occurs when instances fail deployment, which can happen if the CodeDeploy agent is not running or crashing. Instances can be healthy at the EC2 level but unable to run deployments. Options A and C are not related. Option D is misleading because CodeDeploy queues deployments when one is already in progress, rather than failing with this error.
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: 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: 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.
A company uses AWS CodePipeline with a source stage from Amazon S3 and a deploy stage to AWS Elastic Beanstalk. The pipeline has been working for months, but recently the deploy stage started failing with the error 'The S3 object does not exist.' The source artifact is uploaded to the S3 bucket by an external system. Which TWO actions should be taken to resolve this issue? (Choose TWO.)
Explanation: The deploy stage fails with 'The S3 object does not exist' when the external system overwrites the source artifact after the pipeline execution starts. CodePipeline references the object by its key at the time the pipeline is triggered; if the object is replaced (i.e., deleted and re-uploaded with the same key), the pipeline may attempt to download a version that no longer exists, especially if the S3 bucket is not versioned. Ensuring the external system does not overwrite the object during execution prevents this race condition.
A DevOps team is designing a CI/CD pipeline for a microservices application. Each microservice has its own code repository and build artifacts. The team wants to use AWS CodePipeline with multiple parallel actions to build and test all microservices simultaneously. They also want to ensure that if one microservice's build fails, the pipeline does not block other microservices. Which THREE steps should the team take? (Choose THREE.)
Explanation: Using a parallel action group with separate build actions for each microservice allows simultaneous builds within a single pipeline, and failures in one action do not block others. Option B is correct because creating separate pipelines per microservice fully isolates failures, ensuring that a failure in one microservice does not affect the pipelines of other microservices. Option E is correct because setting the 'RunOrder' field to the same number for multiple build actions in a parallel group allows them to run concurrently. Options C and D are incorrect: C would block subsequent stages on failure, which is not desired; D uses sequential builds, which would slow down the pipeline and cause a single failure to block all.
+15 more SDLC Automation questions available
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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