The answer is Vertex AI Model Registry with versioning and alias. This feature is correct because it provides a centralized system for managing model lifecycle, where you can register models, track versions, and assign aliases like 'champion' or 'production' to designate which specific version is approved for deployment, ensuring only vetted models are promoted to production. On the Google Professional Machine Learning Engineer exam, this tests your understanding of MLOps governance and model deployment controls, often appearing as a scenario where you must enforce compliance without manual oversight. A common trap is confusing Model Registry with Vertex AI Endpoints or Feature Store; remember that aliases are the key differentiator for approval workflows. Memory tip: think of aliases as a "stamp of approval" — only models with the 'production' alias get deployed, just like a champion team gets the trophy.
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 wants to ensure that only approved models are deployed to production. Which Vertex AI feature should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Vertex AI Model Registry with versioning and alias.
Vertex AI Model Registry with versioning and alias (Option E) is the correct feature because it allows teams to manage model lifecycle, track approved versions, and assign aliases (e.g., 'champion' or 'production') to designate which model is approved for deployment. This ensures only vetted models are promoted to production, aligning with governance and compliance requirements.
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.
✗
Vertex AI Experiments.
Why it's wrong here
Experiments track training runs, not deployment approval.
✗
Cloud DLP.
Why it's wrong here
Cloud DLP is for data loss prevention, not model deployment.
✗
Vertex AI Pipelines.
Why it's wrong here
Pipelines orchestrate workflows but do not enforce deployment approval.
✗
Vertex AI Feature Store.
Why it's wrong here
Feature Store manages features, not model deployment.
✓
Vertex AI Model Registry with versioning and alias.
Why this is correct
Model Registry provides version control and alias-based deployment gates.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between model tracking (Experiments) and model governance (Registry), so the trap here is assuming that any 'management' feature (like Pipelines or Experiments) can enforce deployment approvals, when only the Registry with aliases provides explicit version control and approval semantics.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Model Registry stores model artifacts with metadata, version IDs, and custom aliases that can be updated via the `google.cloud.aiplatform` SDK. A common real-world scenario is using a CI/CD pipeline that deploys only models with the 'production' alias, preventing accidental rollouts of unapproved versions. The registry also integrates with Vertex AI Endpoints for canary deployments and rollback strategies.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Vertex AI Model Registry with versioning and alias. — Vertex AI Model Registry with versioning and alias (Option E) is the correct feature because it allows teams to manage model lifecycle, track approved versions, and assign aliases (e.g., 'champion' or 'production') to designate which model is approved for deployment. This ensures only vetted models are promoted to production, aligning with governance and compliance requirements.
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.
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Question Discussion
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