The recommended approach is to store DAGs in a source control repository and use CI/CD to deploy to Cloud Composer. This is correct because Cloud Composer, built on Apache Airflow, relies on a shared DAGs folder in Cloud Storage; without version control, multiple data scientists overwriting files directly would cause conflicts and broken pipelines. A CI/CD pipeline ensures each change is reviewed, tested, and synced atomically, preserving workflow integrity and enabling rollback. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of MLOps best practices for collaborative development, often appearing as a trap where you might choose manual uploads or environment-specific branches. The key memory tip is “commit before you compose”—always treat DAGs as code under version control, not as ad-hoc scripts.
PMLE Practice Question: Collaborating within and across teams to manage data and models
This PMLE practice question tests your understanding of collaborating within and across teams to manage data and models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 team is using Cloud Composer to orchestrate ML workflows. They want to allow multiple data scientists to contribute DAGs without interfering with each other. What is the recommended approach?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Store DAGs in a source control repository and use CI/CD to deploy to Cloud Composer
Option C is correct because Cloud Composer (based on Apache Airflow) recommends managing DAGs via source control and CI/CD pipelines to ensure version control, code review, and consistent deployment. This prevents conflicts when multiple data scientists contribute, as each change is tracked and tested before being synced to the DAGs folder in Cloud Storage, avoiding overwrites or broken workflows.
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.
✗
Give each data scientist write access to the DAGs folder in Cloud Storage
Why it's wrong here
Direct write access can lead to conflicts and disrupted workflows.
✗
Use a complex naming convention for DAG files to avoid overwriting
Why it's wrong here
Naming conventions do not prevent overwriting or conflicts.
✓
Store DAGs in a source control repository and use CI/CD to deploy to Cloud Composer
Why this is correct
Version control and CI/CD provide collaboration, testing, and safe deployment.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create a separate Cloud Composer environment for each data scientist
Why it's wrong here
This is costly and leads to resource fragmentation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume direct write access or naming conventions are sufficient for collaboration, but Cisco tests the understanding that production-grade ML workflows require source control and CI/CD to enforce code quality and prevent deployment conflicts.
Detailed technical explanation
How to think about this question
Under the hood, Cloud Composer syncs DAG files from a Cloud Storage bucket to the Airflow workers every few minutes (default sync interval is 3–5 minutes). Using CI/CD ensures that only validated DAGs are uploaded, and the Airflow scheduler parses them without conflicts. In real-world scenarios, teams often use GitHub Actions or Cloud Build to run linting (e.g., with pylint-airflow) and unit tests before deploying, which prevents syntax errors from breaking the entire Airflow instance.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Collaborating within and across teams to manage data and models — This question tests Collaborating within and across teams to manage data and models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Store DAGs in a source control repository and use CI/CD to deploy to Cloud Composer — Option C is correct because Cloud Composer (based on Apache Airflow) recommends managing DAGs via source control and CI/CD pipelines to ensure version control, code review, and consistent deployment. This prevents conflicts when multiple data scientists contribute, as each change is tracked and tested before being synced to the DAGs folder in Cloud Storage, avoiding overwrites or broken workflows.
What should I do if I get this PMLE 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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Question Discussion
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