Databricks-ML-Pro Model Development Practice Question
A machine learning engineer is training a model using MLflow on Databricks and wants to compare multiple runs to select the best hyperparameters. They need to view metrics across runs in a single interface. Which MLflow feature should they use?
⚠ Common exam trap
Test-takers frequently confuse the Model Registry with the Tracking UI; the Registry manages model versions, not experiment run comparisons.
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 Tracking UI
The MLflow Tracking UI is designed to display and compare runs, including metrics, parameters, and artifacts. It allows sorting and filtering, which is essential for selecting the best hyperparameters. Other MLflow components like Model Registry, Projects, and Recipes serve different purposes and do not provide a comparative run view. Thus, the Tracking UI is the correct tool.
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 Projects
Why it's wrong here
MLflow Projects package code and dependencies for reproducible runs, but they do not offer a UI for comparing metrics across runs. They are about packaging and execution, not visualization. Using Projects would not help the engineer compare hyperparameter runs. Thus, it does not meet the requirement.
- ✗
MLflow Model Registry
Why it's wrong here
The Model Registry manages model versions and stage transitions, but it does not provide a comparative view of metrics across runs. It is designed for lifecycle management, not experiment comparison. While you can see some metadata, it lacks the side-by-side run comparison features of the Tracking UI. Therefore, it is not the right tool for this task.
- ✓
MLflow Tracking UI
Why this is correct
The MLflow Tracking UI provides a visual interface to compare runs, including metrics, parameters, and artifacts. It allows sorting and filtering runs by metrics, making it easy to identify the best hyperparameters. This is the standard tool for experiment comparison in MLflow. It directly addresses the need to view and compare multiple runs.
- ✗
MLflow Recipes
Why it's wrong here
MLflow Recipes provides templates for building ML pipelines, but it does not include a run comparison interface. It focuses on structuring workflows, not on visualizing experiment results. While it may log to MLflow, the comparison would still require the Tracking UI. Therefore, it is not the correct choice for comparing runs.
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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.