easyMultiple Choice
PDE Practice Question: Version its ML models and track lineage from…
A company wants to version its ML models and track lineage from training data to deployed model. Which Google Cloud service should they use?
⚠ Common exam trap
PDE often tests whether candidates confuse storage versioning (Cloud Storage), data cataloging (Data Catalog), and artifact storage (Artifact Registry) with ML-specific lineage tracking (Vertex AI ML Metadata) — the trap is picking a general-purpose service for an ML-specific requirement.
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 ML Metadata
Vertex AI ML Metadata is the Google Cloud service designed to track ML artifacts, executions, and contexts, providing model versioning and lineage from training data through to deployed models. It is the native metadata store for Vertex AI pipelines and experiments.
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 with object versioning
Why it's wrong here
Cloud Storage object versioning retains multiple revisions of individual objects, but it does not version ML models as deployable artefacts or record lineage from training data to deployed model. It suits protecting against accidental object overwrites, not model registry workflows.
- ✗
Data Catalog
Why it's wrong here
Data Catalog is a metadata management service for discovering and cataloguing data assets; it does not version ML models or track training-to-deployment lineage. It would be chosen for data discovery and governance, not model lifecycle tracking.
- ✗
Artifact Registry
Why it's wrong here
Artifact Registry stores and versions container images and language packages, but it does not track ML model lineage from training data through to deployment. It is the right choice for managing build artefacts and container images, not for end-to-end model provenance.
- ✓
Vertex AI ML Metadata
Why this is correct
Vertex AI ML Metadata stores and queries artefacts, executions and contexts, giving the lineage graph linking training datasets to models and deployments. It is the purpose-built tracking store, unlike generic Cloud Storage or BigQuery, satisfying the versioning and lineage requirement.
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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 PDE 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 PDE exam.