PMLE Collaborating to manage data and models Practice Question
A team of ML engineers is collaborating on a project using Vertex AI. They want to ensure that only approved models are deployed to production. Which approach should they use?
⚠ Common exam trap
It's easy for candidates to confuse storage access control (IAM) with model lifecycle governance, or assume that any data pipeline tool (Dataflow) can manage model approvals, when in fact only a dedicated model registry with version aliases provides the required approval workflow and traceability.
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
✓
Use Vertex AI Model Registry with version aliases to manage model versions and promote them after approval.
Vertex AI Model Registry provides a centralized repository for managing model versions, with support for version aliases (e.g., 'champion', 'challenger') that allow teams to promote models to production only after approval. This ensures governance and traceability, meeting the requirement that only approved models are deployed.
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 models in a Cloud Storage bucket and manually control access via IAM permissions.
Why it's wrong here
Storing models in Cloud Storage with IAM grants object-level access control only; it provides no approval gate, lineage or deployment authorisation. Vertex AI Model Registry is designed for this, tracking versions and enabling approval before an endpoint deploys them. IAM on buckets suits raw artefact storage, not governed model release.
- ✗
Deploy models directly from training jobs to an endpoint without version tracking.
Why it's wrong here
Deploying straight from training jobs bypasses version tracking entirely, so no approval checkpoint exists and rollback is impossible. Vertex AI Model Registry records versions and lets you approve a specific one before deployment. Direct deployment fits rapid experimentation where governance is not required, not production release control.
- ✓
Use Vertex AI Model Registry with version aliases to manage model versions and promote them after approval.
Why this is correct
Vertex AI Model Registry with version aliases lets the team track model versions and control which alias points to an approved artefact, so only vetted models reach production. Promotion after approval enforces the governance gate the scenario requires.
- ✗
Use Cloud Dataflow to transform raw predictions and then store them in BigQuery for analysis.
Why it's wrong here
Dataflow and BigQuery handle prediction transformation and analytics, not deployment authorisation. They cannot gate which model versions reach an endpoint. This pipeline suits post-deployment monitoring or batch scoring analysis, whereas Vertex AI Model Registry supplies the approval and versioning control the scenario requires.
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