Databricks-ML-Assoc Model Development Practice Question
Which feature in Databricks allows a data scientist to version and manage the lifecycle of machine learning models in a centralized repository?
⚠ Common exam trap
Candidates often confuse MLflow Tracking with the Model Registry, failing to realize that while tracking records experiments, the registry is the specific tool for managing model lifecycle stages.
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
✓
Databricks Model Registry
The Model Registry is the central component in Databricks for managing the model lifecycle. It allows users to transition models through stages like Staging, Production, and Archived. This structure is vital for large teams to ensure that only validated models are deployed to production environments, providing a clear audit trail and simplifying deployment workflows through versioning and stage-based access controls.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
MLflow Experiments
Why it's wrong here
MLflow Experiments are for tracking the training process, including hyperparameters and metrics. They are not intended for managing the lifecycle of the resulting model artifacts, such as versioning, transitions between production stages, or deployment metadata. That role is specifically designated to the MLflow Model Registry feature within the platform.
- ✓
Databricks Model Registry
Why this is correct
The Model Registry provides a centralized location to manage the full lifecycle of MLflow models. It supports versioning, stage transitions, model annotations, and deployment triggers. This is the standard tool in Databricks for transitioning a model from experimentation to production, ensuring governance and reproducibility across the entire organization.
- ✗
Databricks Repos
Why it's wrong here
Databricks Repos is used for version control of source code, such as notebooks and Python files, using Git. While it helps track code changes, it does not provide the functionality needed to manage serialized model artifacts, their performance metrics, or their deployment stages in a production machine learning environment.
- ✗
Unity Catalog
Why it's wrong here
Unity Catalog is primarily used for governing data, analytics assets, and permissions across the workspace. While it can register models, the Model Registry is the specific feature set tailored for the machine learning model lifecycle, including versioning and stage transitions, making it the more accurate answer for this scenario.
About these practice questions
One of 319 original Databricks-ML-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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-Assoc 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-Assoc exam.