PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A team wants to enforce governance and compliance for all ML models across the organisation. They need a centralised repository that tracks model versions, deployment history, and evaluation metrics. Which service should they use?
⚠ Common exam trap
PMLE often tests the distinction between experimentation tracking (Vertex AI Experiments) and production model governance (Model Registry) — candidates pick Experiments because it also stores metrics, but it lacks deployment history and versioning for production models.
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
✓
Vertex AI Model Registry
Vertex AI Model Registry is a centralized repository that tracks model versions, lineage, deployment history, and evaluation metrics across the organization, making it the correct choice for governance and compliance. It integrates with Vertex AI Pipelines and Model Monitoring so that every model artifact has an auditable lifecycle. This directly addresses the requirement for a single source of truth for ML models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Storage
Why it's wrong here
Cloud Storage holds arbitrary objects and provides no model registry, versioning metadata, or deployment and evaluation tracking. It is tempting as cheap durable storage, and would be correct for storing model artefacts or datasets, but not for centralised governance lineage.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store manages feature definitions and serving for training and prediction, not model versions, deployment history, or evaluation metrics. It is tempting because it centralises ML assets, and would be correct for feature reuse and consistency, but it does not provide model governance lineage.
- ✗
Vertex AI Experiments
Why it's wrong here
Vertex AI Experiments tracks training runs, parameters, and metrics for comparison, but it does not maintain a governed model repository with versions and deployment history. It is tempting because it records evaluation metrics, and would be correct for experiment tracking, not organisation-wide model governance.
- ✓
Vertex AI Model Registry
Why this is correct
Vertex AI Model Registry provides a centralised, versioned catalogue tracking each model's versions, deployment history and evaluation metrics, giving the organisation-wide governance and compliance visibility the stem requires. It integrates with Vertex AI Pipelines and endpoints, so lineage is captured automatically.
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Same concept, more angles
1 more way this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is the primary benefit of using a centralised model registry in MLOps?
easy- ✓ A.Governance and version control of models
- B.Better hyperparameter tuning
- C.Faster model training
- D.Automatic model deployment
Why A: A centralised model registry provides governance, versioning, and lineage tracking, enabling collaboration and auditability.
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.