Databricks-ML-Pro ML Ops Practice Question
A data science team is transitioning from manual model training to automated pipelines in Databricks. They require a mechanism to track model lineage, versions, and stage transitions programmatically. Which Databricks component best satisfies this requirement?
⚠ Common exam trap
Candidates often confuse MLflow Tracking with MLflow Model Registry. They select Tracking because it records metrics, but it lacks the lifecycle management and stage transition capabilities required for formal model promotion.
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
MLflow Model Registry provides a centralized model store, set of APIs, and UI for managing the full lifecycle of MLflow models. It enables versioning, model stage transitions, and lineage tracking, which are critical for MLOps maturity. By using the Model Registry, teams ensure reproducibility and governance, allowing for seamless promotion of models from staging to production while maintaining an audit trail of changes and deployment history.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks Jobs Scheduler
Why it's wrong here
The Jobs Scheduler is primarily designed for orchestrating data pipelines and notebook executions. While it can trigger training runs, it lacks the specific metadata management, model staging workflows, and versioning capabilities required to manage the lifecycle of machine learning models effectively across different environments.
- ✓
MLflow Model Registry
Why this is correct
The Model Registry is specifically engineered to handle model versioning, stage transitions (e.g., Staging to Production), and lineage tracking. It provides the necessary APIs to automate these workflows within CI/CD pipelines, ensuring that model deployment follows a robust and governed process in Databricks environments.
- ✗
Databricks Repos
Why it's wrong here
Databricks Repos is used for version control of code using Git integration. While essential for managing source code, it does not provide the functionality to manage model artifacts, track training parameters, or handle the deployment lifecycle stages required for production-grade machine learning model management.
- ✗
Delta Lake
Why it's wrong here
Delta Lake is an open-source storage layer that brings reliability to data lakes through ACID transactions. Although it is excellent for storing training datasets and feature tables, it does not natively provide model lifecycle management features such as stage transitions, model registration, or automated model deployment tracking.
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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
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.