easyMultiple Choice
PMLE Practice Question: A team is using Cloud Composer to orchestrate ML…
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?
⚠ Common exam trap
Test-takers frequently assume direct write access or naming conventions are sufficient for collaboration, but the Google Cloud recommended approach emphasizes source control and CI/CD to enforce code quality and prevent deployment conflicts.
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
✓
Store DAGs in a source control repository and use CI/CD to deploy to Cloud Composer
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.
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
Granting every scientist write access to the shared DAGs bucket lets any of them overwrite or delete another's files, which is exactly the interference the stem asks to prevent. It is tempting because it is the quickest way to let contributors upload DAGs, but it removes the per-user isolation the recommended approach provides.
- ✗
Use a complex naming convention for DAG files to avoid overwriting
Why it's wrong here
Naming conventions only reduce filename collisions; they do not isolate DAG parsing, dependencies or scheduling, so one scientist's broken DAG still affects the shared environment. Naming discipline is tempting because it is free and immediate, but it addresses labels rather than the shared bucket and scheduler that cause interference.
- ✓
Store DAGs in a source control repository and use CI/CD to deploy to Cloud Composer
Why this is correct
Cloud Composer syncs DAGs from a single bucket, so direct edits cause collisions between data scientists. A source control repository with CI/CD deployment gives each contributor isolated branches and controlled merges into the shared environment.
- ✗
Create a separate Cloud Composer environment for each data scientist
Why it's wrong here
Separate environments per scientist do isolate DAGs, but they duplicate orchestration infrastructure and fragment shared pipelines, defeating the collaboration the stem requires. This is tempting because environment-level separation is the strongest isolation boundary, yet the recommended pattern is a single environment with per-user DAG folders and scoped IAM.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.