- A
Allow data scientists to edit feature definitions directly in the Vertex AI Feature Store console.
Why wrong: Direct edits bypass review and versioning.
- B
Require code reviews for all changes to feature definitions before merging to the main branch.
Code reviews ensure quality and approval.
- C
Define multiple feature views in Vertex AI Feature Store for different environments and manage access via IAM.
Why wrong: This addresses access, not versioning or review.
- D
Store feature definition code in a version-controlled repository such as Cloud Source Repositories.
Version control provides history and collaboration.
- E
Use scheduled batch jobs to synchronize feature definitions from a shared spreadsheet to Vertex AI Feature Store.
Why wrong: No built-in review or version control.
Quick Answer
The answer is to store feature definition code in a version-controlled repository and require code reviews for all changes before merging to the main branch. This combination enforces a peer-review gate on feature definitions, ensuring that modifications are validated for correctness, consistency, and compliance before reaching production—a core tenet of MLOps governance. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of how Vertex AI Feature Store integrates with CI/CD pipelines; a common trap is to assume that versioning the feature values themselves suffices, but the exam emphasizes that the *definitions* must be treated as code. Remember that features are the contract between data and models, so treat them with the same rigor as application code. A useful memory tip is “Code first, review always”—version the definition code in a repo, then gate every change with a peer review to prevent silent breaking changes in production.
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 of data scientists and ML engineers is collaborating on a shared feature store in Vertex AI Feature Store. They need to ensure that feature definitions are versioned and that changes are reviewed before being used in production pipelines. Which TWO practices should they implement?
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
Require code reviews for all changes to feature definitions before merging to the main branch.
Option B is correct because requiring code reviews for all changes to feature definitions before merging to the main branch enforces a peer-review gate, ensuring that modifications are validated for correctness, consistency, and compliance before they reach production. This aligns with MLOps best practices for governance and reduces the risk of introducing errors or breaking changes into the feature store.
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.
- ✗
Allow data scientists to edit feature definitions directly in the Vertex AI Feature Store console.
Why it's wrong here
Direct edits bypass review and versioning.
- ✓
Require code reviews for all changes to feature definitions before merging to the main branch.
Why this is correct
Code reviews ensure quality and approval.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Define multiple feature views in Vertex AI Feature Store for different environments and manage access via IAM.
Why it's wrong here
This addresses access, not versioning or review.
- ✓
Store feature definition code in a version-controlled repository such as Cloud Source Repositories.
Why this is correct
Version control provides history and collaboration.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use scheduled batch jobs to synchronize feature definitions from a shared spreadsheet to Vertex AI Feature Store.
Why it's wrong here
No built-in review or version control.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between environment isolation (IAM and multiple feature views) and the actual versioning/review process, leading candidates to mistakenly select Option C as a versioning practice when it only addresses access control and environment separation.
Detailed technical explanation
How to think about this question
Vertex AI Feature Store stores feature definitions as metadata in a central registry, and versioning is achieved by storing the definition code (e.g., YAML or Python files) in a Git-based repository like Cloud Source Repositories. Under the hood, each commit triggers a CI/CD pipeline that can validate the schema, check for backward compatibility, and deploy the new feature view version to the feature store, ensuring that production pipelines always reference a reviewed and approved version.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PMLE question test?
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: Require code reviews for all changes to feature definitions before merging to the main branch. — Option B is correct because requiring code reviews for all changes to feature definitions before merging to the main branch enforces a peer-review gate, ensuring that modifications are validated for correctness, consistency, and compliance before they reach production. This aligns with MLOps best practices for governance and reduces the risk of introducing errors or breaking changes into the feature store.
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.
About these practice questions
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Last reviewed: Jun 30, 2026
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