Databricks-ML-Pro Model Development Practice Question
A data scientist is developing a model on Databricks and wants to use MLflow to compare multiple runs. They need to quickly identify the run with the lowest validation loss. Which MLflow UI feature allows them to sort and filter runs based on metrics?
⚠ Common exam trap
The trap here is thinking that the model registry or artifacts tab provides run comparison capabilities, when actually the experiment UI's runs table is designed for that purpose.
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
✓
The runs table in the MLflow experiment UI, where you can sort by metrics and apply filters.
The MLflow experiment UI includes a runs table that lists all runs with their metrics. You can sort by any metric, such as validation loss, to find the lowest value. Filters can also be applied to focus on specific runs. This is the standard way to compare runs visually.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The runs table in the MLflow experiment UI, where you can sort by metrics and apply filters.
Why this is correct
The MLflow experiment UI provides a runs table that displays all runs with their parameters, metrics, and tags. You can sort the table by any metric column, such as validation loss, and apply filters to narrow down runs. This makes it easy to identify the best run based on specific criteria.
- ✗
The artifacts tab of a run, where you can view logged metric files and manually sort them.
Why it's wrong here
The artifacts tab shows files logged to a run, such as metric files, but it does not provide interactive sorting or filtering across multiple runs. It is not designed for run comparison. The runs table is the appropriate feature for this task.
- ✗
The notebook's output cell, where you can print a DataFrame of runs and sort it.
Why it's wrong here
While you can programmatically retrieve runs using the MLflow API and sort them in a notebook, the question asks for an MLflow UI feature. The notebook output is not a UI feature of MLflow for run comparison. The runs table in the experiment UI is the built-in solution.
- ✗
The model registry page, where you can compare model versions by their metrics.
Why it's wrong here
The model registry is for managing model versions and stages, not for comparing experiment runs. While model versions may have metrics logged, the registry does not provide a comprehensive runs comparison view. The experiment UI is the correct place to compare runs.
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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.