PMLE Collaborating to manage data and models Practice Question
A data science team uses BigQuery to store raw data and Vertex AI for model training. They want to ensure that only authorized users can access training data, and that model artifacts are automatically versioned and tracked. Which combination of Google Cloud services should they use?
⚠ Common exam trap
Google Cloud often tests the distinction between services that handle data processing (Dataflow, Cloud Composer) versus those that handle access control and metadata management (Feature Store, ML Metadata), leading candidates to confuse orchestration or CI/CD tools with versioning and access control solutions.
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 Feature Store with access control and Vertex AI ML Metadata for model versioning
Vertex AI Feature Store provides fine-grained access control to training data, ensuring only authorized users can access it. Vertex AI ML Metadata automatically tracks and versions model artifacts, lineage, and parameters, which aligns with the requirement for automated versioning and tracking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dataflow for data access control and Vertex AI Experiments for model tracking
Why it's wrong here
Dataflow is for data processing, not access control; Vertex AI Experiments is for tracking hyperparameters, not full model versioning.
- ✗
Cloud Storage with bucket-level IAM and Cloud Build for versioning
Why it's wrong here
Cloud Storage does not provide fine-grained access control for features, and Cloud Build is for CI/CD, not model versioning.
- ✗
Cloud Composer for data access control and Cloud Source Repositories for model versioning
Why it's wrong here
Cloud Composer is an orchestration tool, not for access control; Cloud Source Repositories is for code, not models.
- ✓
Vertex AI Feature Store with access control and Vertex AI ML Metadata for model versioning
Why this is correct
Vertex AI Feature Store provides controlled access to features, and ML Metadata tracks model artifacts and versions.
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