PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
An organization wants to implement central governance for ML models across teams. Which TWO services should they use together to achieve model versioning, lineage, and deployment management? (Select 2)
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 (B) is correct because it provides a central repository for managing the lifecycle of ML models, including versioning, tracking model artifacts, and managing deployment to endpoints, which directly satisfies the model versioning and deployment management requirements. Vertex AI Metadata (C) is correct because it captures and stores metadata about artifacts, executions, and contexts, enabling lineage tracking across the ML workflow so teams can trace how models were produced and what data or training runs contributed to them. Together, Model Registry handles versioning and deployment while Metadata provides the lineage and governance context needed for central oversight. Vertex AI Feature Store (A) is for organizing, serving, and sharing feature data, not for model versioning or deployment management. Vertex AI Experiments (D) is for tracking and comparing training experiment runs and metrics, which supports experimentation but not centralized model deployment governance. Cloud Data Catalog (E) is a data discovery and metadata management service for data assets, not a model registry or ML lineage/deployment tool.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store serves feature ingestion, sharing and serving consistency across training and prediction, holding no model artefacts, versions or deployment records. It would be chosen when teams need a central curated feature repository to prevent training-serving skew, not the model registry and deployment governance this scenario requires.
- ✓
Vertex AI Model Registry
Why this is correct
Vertex AI Model Registry provides central model versioning, lineage tracking and deployment management across teams, satisfying the governance requirement. It organises models into versioned lineages and integrates with endpoints, giving one governed catalogue for all teams' models.
- ✓
Vertex AI Metadata
Why this is correct
Vertex AI Metadata stores artefacts, executions and contexts in a managed ML metadata store, giving the lineage and versioning the stem demands across teams. It records each model's training runs, datasets and parameters, so governance queries trace provenance. Deployment management, however, requires pairing it with Vertex AI Model Registry.
- ✗
Vertex AI Experiments
Why it's wrong here
Vertex AI Experiments tracks training runs, parameters, metrics and artefacts for comparison, not production model versions or deployment rollout. It suits experiment reproducibility during model development, whereas the requirement is central governance of deployed models across teams.
- ✗
Cloud Data Catalog
Why it's wrong here
Cloud Data Catalog discovers, tags and searches data assets for governance and lineage of datasets, not model versions or deployments. It fits cataloguing BigQuery tables and data lineage across an organisation, so it cannot supply the model registry and deployment management the stem demands.
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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.