Databricks-ML-Pro ML Ops Practice Question
Which component of MLflow is responsible for keeping track of the different versions of a model as it moves from development to testing and production?
⚠ Common exam trap
Candidates often confuse the MLflow Tracking Server with the Model Registry, failing to distinguish between experiment logging and the centralized management of model production 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 is the central repository for model versioning. It allows teams to manage the lifecycle of a model by transitioning versions through different stages. This is a critical MLOps function because it ensures that production environments are always pointing to a known, stable version of the model, while allowing developers to continue iterating on new versions without disrupting the live service.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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MLflow Tracking
Why it's wrong here
MLflow Tracking is used for logging experiment runs, including metrics and parameters. While it tracks the history of individual runs, it does not manage the lifecycle of a model version across different stages like 'Staging' or 'Production'. It is meant for the development and experimental phase, not for deployment management.
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MLflow Projects
Why it's wrong here
MLflow Projects is a format for packaging data science code. It ensures that the code is reproducible by specifying dependencies. While it helps in the development phase, it does not provide the model versioning, registry, or lifecycle management capabilities required to manage model transitions across environments in a production setting.
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MLflow Model Registry
Why this is correct
The Model Registry provides a centralized store for managing the full lifecycle of MLflow models. It supports versioning, stage transitions, and annotations. It is the primary tool in Databricks for managing production deployments, ensuring that the correct model versions are used in the correct environments at all times.
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MLflow Recipes
Why it's wrong here
MLflow Recipes (formerly known as Pipelines) is a framework for structuring ML code into standard, modular steps. It assists in building repeatable pipelines but does not function as the versioning or lifecycle management system for model artifacts, which is the specific role of the MLflow Model Registry component.
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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.