The answer is Vertex AI Model Registry and Cloud Storage buckets with object versioning enabled. Vertex AI Model Registry is the correct choice because it serves as a centralized, version-controlled repository that tracks model artifacts, metadata, and deployment history, integrating natively with Vertex AI endpoints and pipelines for consistent governance and lineage. Cloud Storage with object versioning complements this by preserving immutable snapshots of model artifacts, preventing accidental overwrites and enabling rollback. On the Google Professional Machine Learning Engineer exam, this tests your understanding of MLOps lifecycle management and the distinction between artifact storage and model metadata tracking. A common trap is choosing a single storage solution without recognizing that Model Registry handles versioning and governance while Cloud Storage provides durable artifact storage. Remember the pairing: Registry for metadata and lineage, Cloud Storage for the actual files.
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.
Which TWO options are recommended practices for managing model versions across teams in Google Cloud?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Vertex AI Model Registry to track model versions and their deployment history
Vertex AI Model Registry is the recommended service for managing model versions across teams because it provides a centralized repository to track model versions, their associated metadata, and deployment history. It integrates natively with Vertex AI endpoints and pipelines, enabling consistent governance and lineage tracking across the ML lifecycle.
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.
✗
Store all model files in a GitHub repository
Why it's wrong here
Git is not designed for large binary files and lacks model-specific metadata.
✗
Maintain a custom database to map model names to artifact locations
Why it's wrong here
Custom solutions are not scalable and miss integration with Vertex AI.
✗
Use AI Platform (Unified) Models as the primary model registry
Why it's wrong here
AI Platform (Unified) is now Vertex AI; the old name is less precise.
✓
Use Vertex AI Model Registry to track model versions and their deployment history
Why this is correct
Model Registry is the recommended service for managing model versions.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use Cloud Storage buckets with object versioning enabled to store model artifacts
Why this is correct
Object versioning provides an audit trail for model files.
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 legacy AI Platform (Unified) Models and the current Vertex AI Model Registry, expecting candidates to recognize that the registry is the recommended service for version management and deployment history, not just a generic model storage location.
Detailed technical explanation
How to think about this question
Vertex AI Model Registry uses a resource hierarchy where each model version is an immutable snapshot stored in Cloud Storage, with metadata including training pipeline ID, evaluation metrics, and deployment targets. Under the hood, the registry maintains a version graph that supports canary deployments and rollback by tracking which model version is serving on which endpoint, enabling fine-grained traffic splitting without manual artifact management.
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 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
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: Use Vertex AI Model Registry to track model versions and their deployment history — Vertex AI Model Registry is the recommended service for managing model versions across teams because it provides a centralized repository to track model versions, their associated metadata, and deployment history. It integrates natively with Vertex AI endpoints and pipelines, enabling consistent governance and lineage tracking across the ML lifecycle.
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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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.
Question Discussion
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