Databricks-ML-Pro Model Development Practice Question
Which Databricks feature is specifically designed to manage the lifecycle of a machine learning model, including versioning, stage transitions, and deployment tracking?
⚠ Common exam trap
Candidates confuse MLflow Tracking with the MLflow Model Registry, failing to realize that governance, versioning, and stage transitions are handled by the Registry.
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 is the definitive tool in Databricks for managing the model lifecycle. It allows teams to register models, track versions, and manage stage transitions (e.g., Staging to Production). By providing a centralized, audit-trailed repository, it ensures that only validated models are deployed into production, fulfilling the core requirements of MLOps for governance, reliability, and automated deployment pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Unity Catalog.
Why it's wrong here
While Unity Catalog manages data governance and lineage, it is not the primary tool for managing the lifecycle, versioning, and stage transitions of machine learning models. The MLflow Model Registry is the specialized tool for these model-specific tasks, though it integrates with Unity Catalog for broader data asset governance.
- ✓
MLflow Model Registry.
Why this is correct
The Model Registry is built exactly for the needs of model lifecycle management. It provides a centralized hub to track model versions, handle approvals, and manage deployments, which is essential for ensuring that ML models in production are stable, reproducible, and compliant with organizational standards for model deployment and auditing.
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Databricks Delta Lake.
Why it's wrong here
Delta Lake is used for data storage, versioning, and ACID transactions. It is not designed for model versioning or managing the lifecycle of ML artifacts. While it is a critical component of the data pipeline, it should not be confused with the MLflow tools designed for managing the machine learning lifecycle.
- ✗
Databricks Repos.
Why it's wrong here
Databricks Repos is for source code version control using Git. While it is essential for managing ML code, it does not manage the model lifecycle, artifacts, or deployment stages. It is a development tool, whereas the Model Registry is an operational and governance tool for model artifacts and versions.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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
This Databricks-ML-Pro practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-ML-Pro exam.