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PMLE Practice Question: A team of data scientists and ML engineers is…
A team of data scientists and ML engineers is collaborating on a project using Vertex AI Workbench. They need to share notebooks and code, but want to avoid conflicts and maintain a history of changes. Which approach should they use?
⚠ Common exam trap
It's easy for candidates to confuse collaboration tools (like shared storage or experiment tracking) with version control, assuming that any shared access or logging mechanism can replace the structured history and conflict resolution of a git-based workflow.
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
✓
Use a git repository (e.g., Cloud Source Repositories) to manage code and notebooks.
Using a git repository (e.g., Cloud Source Repositories) provides version control, branching, and a full history of changes, which is essential for collaborative development. This approach avoids conflicts by allowing team members to work on separate branches and merge changes systematically, unlike shared storage or manual methods that lack conflict resolution and audit trails.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Email notebook files to each other and manually merge changes.
Why it's wrong here
Emailing files gives no shared repository, so concurrent edits overwrite each other and no commit history exists. It is tempting because email is universally available and needs no setup, and would suffice only for a single author distributing a finished notebook to reviewers.
- ✗
Store notebooks in a shared Cloud Storage bucket and access them simultaneously.
Why it's wrong here
A Cloud Storage bucket provides object storage without version control or merge, so simultaneous edits to one notebook overwrite each other and no change history is retained. It is tempting because buckets are shared and durable, and would suit distributing read-only datasets rather than collaborative code.
- ✗
Use Vertex AI Experiments to share notebook outputs.
Why it's wrong here
Vertex AI Experiments tracks runs, parameters and metrics, not notebook source files, so it records no code changes and cannot prevent edit conflicts. It is tempting because it is a Vertex AI feature for collaboration, and would be correct for comparing model training runs rather than sharing notebooks.
- ✓
Use a git repository (e.g., Cloud Source Repositories) to manage code and notebooks.
Why this is correct
A git repository provides version control: commits preserve a full change history, and branching or merging resolves concurrent edits, preventing the conflicts that shared notebook storage causes. Cloud Source Repositories hosts this centrally, letting data scientists and ML engineers collaborate on Vertex AI Workbench notebooks safely.
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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.