- A
Use a Lambda function in the pipeline to check the branch name and fail if not allowed.
Why wrong: The Lambda function can be modified by anyone with pipeline edit permissions.
- B
Add a manual approval step in the pipeline and rely on the approver to verify the branch.
Why wrong: Manual approval does not prevent a developer from approving their own change.
- C
Create a separate pipeline for each allowed branch, with the approval step only in the production pipeline.
Isolating pipelines prevents direct deployment from unauthorized branches.
- D
Tag the source artifacts with the branch name and use a condition in CodePipeline to allow only specific tags.
Why wrong: CodePipeline does not support conditions on artifact tags.
Question 224 of 1,750
DOP-C02 SDLC Automation Practice Question
This DOP-C02 practice question tests your understanding of sdlc automation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a separate pipeline for each allowed branch, with the approval step only in the production pipeline.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a Lambda function in the pipeline to check the branch name and fail if not allowed.
Why it's wrong here
The Lambda function can be modified by anyone with pipeline edit permissions.
- ✗
Add a manual approval step in the pipeline and rely on the approver to verify the branch.
Why it's wrong here
Manual approval does not prevent a developer from approving their own change.
- ✓
Create a separate pipeline for each allowed branch, with the approval step only in the production pipeline.
Why this is correct
Isolating pipelines prevents direct deployment from unauthorized branches.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Tag the source artifacts with the branch name and use a condition in CodePipeline to allow only specific tags.
Why it's wrong here
CodePipeline does not support conditions on artifact tags.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often overestimate the flexibility of CodePipeline's built-in filtering or underestimate the security and maintainability benefits of using separate pipelines per branch, leading them to choose a custom Lambda solution (Option A) that introduces unnecessary complexity and risk.
Detailed technical explanation
How to think about this question
Under the hood, AWS CodePipeline triggers can be configured with Git-based events (e.g., push to a specific branch) using webhooks or Amazon EventBridge rules, allowing separate pipelines per branch without custom code. This pattern aligns with GitFlow or trunk-based development, where a production pipeline only triggers on the main branch, while feature branches use separate pipelines with different stages. In practice, this avoids the overhead of maintaining a Lambda function and ensures that approval steps are only applied to the critical deployment path, reducing blast radius.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DOP-C02 question test?
SDLC Automation — This question tests SDLC Automation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a separate pipeline for each allowed branch, with the approval step only in the production pipeline. — 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.
What should I do if I get this DOP-C02 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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