CERTIFIED-MACHINE-LEARNING-PROFESSIONAL · Question #51
CERTIFIED-MACHINE-LEARNING-PROFESSIONAL Question #51: Real Exam Question with Answer & Explanation
The correct answer is C: Model Serving. Note: The marked correct answer (C. Model Serving) appears to be an error in this question. The actual correct answer is B. Model Registry. Model Registry is the Databricks-managed MLflow capability that serves as a centralized model store - it provides a shared repository where
Question
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
Options
- AModels
- BModel Registry
- CModel Serving
- DFeature Store
- EExperiments
Explanation
Note: The marked correct answer (C. Model Serving) appears to be an error in this question. The actual correct answer is B. Model Registry.
Model Registry is the Databricks-managed MLflow capability that serves as a centralized model store - it provides a shared repository where teams can register, version, annotate, and manage the full lifecycle of ML models across stages (Staging, Production, Archived).
Why the distractors are wrong:
- A. Models - refers to individual logged model artifacts within a run, not a centralized store
- C. Model Serving - deploys registered models as REST API endpoints for real-time inference; it uses the registry but isn't the store itself
- D. Feature Store - a centralized store for features (input data), not models
- E. Experiments - organizes and tracks training runs and their metrics/parameters, not a model store
Memory tip: Think "Registry = warehouse." Just like a product registry tracks items in a catalog with versions and statuses, the Model Registry catalogs your models with versions and lifecycle stages (Staging → Production). Model Serving is what happens after you pull something off the shelf.
If you encounter this question on an exam, select B. Model Registry.
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