Quick Answer
The answer is to use a user-managed notebook instance with multiple users. This approach is correct because Vertex AI Workbench allows you to share a single, persistent notebook environment by granting IAM permissions to team members, while the underlying Git integration provides full version control for the codebase. On the Google Professional Data Engineer exam, this scenario tests your understanding of how to balance collaboration with reproducibility—a common trap is choosing a managed notebook instance, which lacks multi-user sharing and persistent storage, or relying solely on exporting notebooks to a shared drive without version tracking. Remember that user-managed instances are designed for team-based, long-running work, whereas managed instances are ephemeral and single-user. A helpful memory tip: think of "user-managed" as "team-managed"—if you need multiple people in the same environment with Git history, always pick the user-managed option.
This PDE practice question tests your understanding of operationalizing machine learning models. 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 data scientist uses Vertex AI Workbench notebooks for model development. They want to share the environment with team members while maintaining version control. Which approach should they use?
Use Cloud Shell and clone the repo
Why wrong: Limited resources and not scalable.
Use a user-managed notebook instance with multiple users
Allows collaboration with version control.
Share the notebook via Cloud Storage
Why wrong: No version control or collaboration.
Store notebooks in Cloud Source Repositories
Why wrong: No interactive environment.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a user-managed notebook instance with multiple users
A user-managed notebook instance with multiple users is the correct approach because Vertex AI Workbench supports collaboration by allowing multiple users to access the same instance via IAM permissions, while the underlying Git integration enables version control. This setup provides a shared, persistent environment where team members can work on the same codebase without duplicating work, and changes can be tracked through Git repositories.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use Cloud Shell and clone the repo
Why it's wrong here
Limited resources and not scalable.
Use a user-managed notebook instance with multiple users
Why this is correct
Allows collaboration with version control.
Related concept
Read the scenario before looking for a memorised answer.
Share the notebook via Cloud Storage
Why it's wrong here
No version control or collaboration.
Store notebooks in Cloud Source Repositories
Why it's wrong here
No interactive environment.
Common exam traps
The trap here is that candidates confuse storing notebooks in a version control system (like Cloud Source Repositories) with having a shared, interactive development environment, overlooking that version control alone does not provide the compute and collaboration features of a user-managed notebook instance.
Detailed technical explanation
Under the hood, a user-managed notebook instance in Vertex AI Workbench runs on a Compute Engine VM with JupyterLab, and IAM roles (e.g., roles/notebooks.viewer, roles/notebooks.editor) control multi-user access. The instance can be configured with a Git repository (e.g., Cloud Source Repositories or GitHub) at creation time, enabling automatic synchronization and version control of notebooks and scripts. In a real-world scenario, a team of data scientists can collaborate on the same instance, each with their own kernel sessions, while Git handles branching and merging for code changes.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Operationalizing machine learning models — study guide chapter
Learn the concepts, then practise the questions
Operationalizing machine learning models practice questions
Targeted practice on this topic area only
All PDE questions
499 questions across all exam domains
Google Professional Data Engineer study guide
Full concept coverage aligned to exam objectives
PDE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise PDE questions linked to Designing data processing systems.
Practise PDE questions linked to Building and operationalizing data processing systems.
Practise PDE questions linked to Operationalizing machine learning models.
Practise PDE questions linked to Ensuring solution quality.
Practise PDE questions linked to PDE fundamentals.
Practise PDE questions linked to PDE scenario.
Practise PDE questions linked to PDE troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
The correct answer is: Use a user-managed notebook instance with multiple users — A user-managed notebook instance with multiple users is the correct approach because Vertex AI Workbench supports collaboration by allowing multiple users to access the same instance via IAM permissions, while the underlying Git integration enables version control. This setup provides a shared, persistent environment where team members can work on the same codebase without duplicating work, and changes can be tracked through Git repositories.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 11, 2026
This PDE 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 PDE exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.