Model Registry — Google Cloud ML Engineer Practice Questions
A model registry is a centralized repository for storing, versioning, and managing trained machine learning models throughout their lifecycle. On the Google Cloud ML Engineer exam, Vertex AI Model Registry is the primary service for registering models so they can be tracked, evaluated, and deployed consistently. Candidates must understand how to register models produced by Vertex AI Pipelines or custom training jobs, manage model versions, and associate evaluation metrics with each version. This knowledge connects directly to deployment workflows and governance requirements tested throughout the exam.
Free questions on model registry
In MLOps, what is a model registry used for?
Free question · easy · full answer + explanation
More model registry questions in the full bank
- Your cross-functional team needs to document ML model decisions and lineage. Which tool is most appropriate? Unlock answer & explanation →
- When configuring the Vertex AI Model Registry for production deployments, you need to enforce that models undergo validation before promotion to the production endpoint. How should you implement this governance? Unlock answer & explanation →
- How does Vertex AI Model Registry help manage models? Unlock answer & explanation →