Databricks-ML-Assoc ML Workflows Practice Question
Which Databricks component is specifically designed to manage the full lifecycle of machine learning models, including registration, versioning, and stage transitions?
⚠ Common exam trap
Candidates often confuse MLflow Tracking with the Model Registry, failing to distinguish between tracking experiments and managing the formal lifecycle of production-ready model versions.
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
✓
MLflow Model Registry
The MLflow Model Registry provides a centralized model store, APIs, and UI to collaboratively manage the full lifecycle of MLflow models. It handles versioning, stage transitions (e.g., Staging to Production), and model annotations. This component is essential for operationalizing machine learning, as it provides a single source of truth for model artifacts and ensures that only validated models are promoted to production environments after passing necessary checks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delta Lake
Why it's wrong here
Delta Lake is an open-source storage layer that brings reliability to data lakes through ACID transactions and scalable metadata handling. While it stores the training data, it is not responsible for managing model versions, registry metadata, or transitions between staging and production environments for trained machine learning model artifacts.
- ✓
MLflow Model Registry
Why this is correct
The MLflow Model Registry is the designated tool for versioning, deploying, and managing the lifecycle of machine learning models in Databricks. It allows teams to track model lineage, manage stage transitions, and ensure that deployments are consistent, audited, and easily reversible, which is a fundamental requirement for production-grade MLOps pipelines.
- ✗
Unity Catalog
Why it's wrong here
Unity Catalog is a unified governance solution for data, analytics, and AI on the Databricks platform. While it provides centralized access control, it is not a dedicated tool for tracking model versioning, training metrics, or managing model stage transitions, which are functions specifically handled by the MLflow Model Registry integration.
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Databricks Feature Store
Why it's wrong here
The Databricks Feature Store is used for creating, sharing, and managing machine learning features to ensure consistency between training and inference. It does not manage the lifecycle of the model artifact itself; it focuses on the data preparation layer by storing feature tables and metadata for models to consume.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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