Quick Answer
The answer is Vertex AI ML Metadata, as it is the Google Cloud service specifically designed for ML model versioning and lineage tracking. This service captures and stores metadata about ML artifacts—such as datasets, training runs, and model versions—and records their relationships, enabling full traceability from raw training data to the deployed model. On the Google Professional Data Engineer exam, this question tests your understanding of Vertex AI’s managed ML operations tools, often appearing in scenarios about reproducibility and governance. A common trap is confusing Artifact Registry (for container images) or Cloud Storage (for raw files) with ML-specific lineage; remember that only ML Metadata tracks the provenance of ML artifacts and their transformations. A useful memory tip: think “ML Metadata” as the “family tree” for your models—it logs who begat whom, from data to deployment.
This PDE practice question tests your understanding of operationalizing machine learning 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 company wants to version its ML models and track lineage from training data to deployed model. Which Google Cloud service should they use?
Cloud Storage with object versioning
Why wrong: Cloud Storage lacks lineage tracking capabilities.
Data Catalog
Why wrong: Data Catalog is for metadata management of data assets.
Artifact Registry
Why wrong: Artifact Registry is for storing build artifacts and container images.
Vertex AI ML Metadata
ML Metadata tracks artifacts, lineage, and metadata for ML models.
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 ML Metadata
Option B is correct because Vertex AI ML Metadata manages lineage and artifacts. Option A (Cloud Storage) is for storage only. Option C (Artifact Registry) is for container images, not ML models. Option D (Data Catalog) is for data discovery.
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.
Cloud Storage with object versioning
Why it's wrong here
Cloud Storage lacks lineage tracking capabilities.
Data Catalog
Why it's wrong here
Data Catalog is for metadata management of data assets.
Artifact Registry
Why it's wrong here
Artifact Registry is for storing build artifacts and container images.
Vertex AI ML Metadata
Why this is correct
ML Metadata tracks artifacts, lineage, and metadata for ML models.
Related concept
Read the scenario before looking for a memorised answer.
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 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
Identify which PDE 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
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
The correct answer is: Vertex AI ML Metadata — Option B is correct because Vertex AI ML Metadata manages lineage and artifacts. Option A (Cloud Storage) is for storage only. Option C (Artifact Registry) is for container images, not ML models. Option D (Data Catalog) is for data discovery.
Identify which PDE 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 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.
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