Quick Answer
The answer is Vertex AI Model Registry. This service is the correct choice because it is purpose-built for managing multiple model versions, tracking lineage from training to deployment, and enforcing approval workflows, all while integrating seamlessly with Vertex AI Pipelines for automated retraining and deployment. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of which Vertex AI service handles model governance versus feature management or hyperparameter tuning; a common trap is confusing Model Registry with ML Metadata, but remember that ML Metadata is a lower-level API for recording artifact metadata, not a user-friendly versioning interface. Another frequent mistake is selecting Feature Store, which stores feature values, not model artifacts, or Vizier, which optimizes hyperparameters. To lock this in, use the mnemonic “Models need a Registry, not a Feature Store or a Vizier.”
This PMLE practice question tests your understanding of serving and scaling 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 machine learning engineer wants to manage multiple model versions and facilitate collaboration across teams. The goal is to track model lineage, versioning, and approvals. Which Vertex AI service should they use?
Vertex AI Model Registry
Model Registry is designed for model versioning, lifecycle management, and collaboration.
Vertex AI ML Metadata
Why wrong: ML Metadata can track lineage but does not provide approval workflows or easy version management.
Vertex AI Feature Store
Why wrong: Feature Store manages features, not models.
Vertex AI Vizier
Why wrong: Vizier is for hyperparameter tuning.
Answer choices
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
Option C is correct because Model Registry provides versioning, approval tracking, and integration with Vertex AI Pipelines. Option A is wrong because Feature Store stores features, not models. Option B is wrong because ML Metadata is lower-level and less user-friendly. Option D is wrong because Vizier is for hyperparameter tuning.
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.
Vertex AI Model Registry
Why this is correct
Model Registry is designed for model versioning, lifecycle management, and collaboration.
Related concept
Read the scenario before looking for a memorised answer.
Vertex AI ML Metadata
Why it's wrong here
ML Metadata can track lineage but does not provide approval workflows or easy version management.
Vertex AI Feature Store
Why it's wrong here
Feature Store manages features, not models.
Vertex AI Vizier
Why it's wrong here
Vizier is for hyperparameter tuning.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
The correct answer is: Vertex AI Model Registry — Option C is correct because Model Registry provides versioning, approval tracking, and integration with Vertex AI Pipelines. Option A is wrong because Feature Store stores features, not models. Option B is wrong because ML Metadata is lower-level and less user-friendly. Option D is wrong because Vizier is for hyperparameter tuning.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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