PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A data science team collaborates using Vertex AI Workbench user-managed notebooks. They want to version control their notebook code and share it with team members. Which TWO tools should they use? (Choose 2)
⚠ Common exam trap
The trap is selecting other Vertex AI components like Model Registry or Experiments, which sound related to ML workflows but are not for source code version control — candidates must distinguish between code versioning and model/experiment tracking.
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
✓
Git integration in Vertex AI Workbench
Option A, Git integration in Vertex AI Workbench, is correct because user-managed notebooks include built-in Git support, allowing data scientists to clone repositories, commit, push, and pull notebook code directly from the JupyterLab interface for version control and collaboration. Option E, Cloud Source Repositories, is correct because it is a fully managed private Git repository service on Google Cloud that can host the team's notebook code and integrate with Workbench's Git tooling for sharing and versioning. Option B, Cloud Functions, is incorrect because it is a serverless compute service for event-driven functions, not a version control or code-sharing tool. Option C, Vertex AI Model Registry, is incorrect because it manages trained model versions and their metadata, not notebook source code. Option D, Vertex AI Experiments, is incorrect because it tracks experiment runs, parameters, and metrics, not Git-based notebook version control.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Git integration in Vertex AI Workbench
Why this is correct
Git integration in Vertex AI Workbench lets users commit notebook code to a remote repository, track revisions and share branches with teammates. This satisfies the version-control and collaboration requirement without leaving the managed notebook environment.
- ✗
Cloud Functions
Why it's wrong here
Cloud Functions runs event-driven serverless code and provides no repository or notebook version control. It is tempting because it is a Google Cloud service that integrates with Vertex AI pipelines, but versioning and sharing notebooks requires Git-based tooling such as Cloud Source Repositories or GitHub.
- ✗
Vertex AI Model Registry
Why it's wrong here
Vertex AI Model Registry catalogues trained model versions and their deployment metadata, not notebook source code. It is tempting because it versions artefacts, but those artefacts are models, not .ipynb files. Sharing and versioning notebook code needs a Git repository, which the Model Registry does not offer.
- ✗
Vertex AI Experiments
Why it's wrong here
Vertex AI Experiments tracks and compares training runs, metrics and parameters, not notebook source files. It is tempting because it versions artefacts within a project, but it records experiment lineage rather than Git-style code history. Version-controlling notebook code requires a repository such as Cloud Source Repositories or GitHub, which Experiments does not provide.
- ✓
Cloud Source Repositories
Why this is correct
Cloud Source Repositories provides Git-based version control, letting the team commit notebook code, track revisions and share repositories with members. This directly satisfies the stem's requirement to version control and share notebook code across the data science team.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.