PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
What is the primary benefit of using a centralised model registry in MLOps?
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
✓
Governance and version control of models
A centralised model registry provides governance, versioning, and lineage tracking, enabling collaboration and auditability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Governance and version control of models
Why this is correct
A centralised model registry provides governance and version control, tracking each model's lineage, approvals, and deployment stage. This gives teams a single authoritative source for model artefacts, satisfying auditability and reproducibility requirements across the MLOps lifecycle.
- ✗
Better hyperparameter tuning
Why it's wrong here
A model registry stores versioned artefacts, metadata and lineage; it does not run hyperparameter searches, which belong to tuning tools such as Optuna or Vertex AI Vizier. It is tempting because registry metadata can inform tuning, but the registry itself performs no parameter optimisation.
- ✗
Faster model training
Why it's wrong here
A model registry catalogues and versions trained models; it does not accelerate training, which depends on compute, data pipelines and algorithm choice. It is tempting because a registry can trigger retraining pipelines, but the training speed itself is unaffected by where models are recorded.
- ✗
Automatic model deployment
Why it's wrong here
A registry records model versions and their metadata; deployment still requires a separate serving or CI/CD mechanism to push a model into production. It is tempting because registries often integrate with deployment tooling, but the registry alone does not perform the deployment action.
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