Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer is using MLflow Tracking to compare multiple runs of a hyperparameter tuning experiment. The engineer wants to quickly identify the run that achieved the best validation accuracy and then promote that run's model to the Model Registry. Which MLflow feature allows the engineer to view and compare runs in a centralized UI?
⚠ Common exam trap
It's easy for candidates to confuse the Model Registry with the Tracking UI; the Registry manages model versions, while the Tracking UI compares runs.
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 log and compare runs, displaying metrics, parameters, and artifacts in a sortable table. It allows the engineer to identify the best run and then register its model to the Model Registry with a few clicks, streamlining the workflow.
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 Model Registry
Why it's wrong here
The Model Registry manages model versions and stages but does not compare training runs. It is used after a model is registered, not for exploring experiment results. While you can register a model from the Tracking UI, the Registry itself does not offer run comparison.
- ✗
MLflow Projects
Why it's wrong here
MLflow Projects is a packaging format for reproducible runs, not a UI for comparing runs. It defines dependencies and entry points but does not provide a visual comparison of metrics across runs. While Projects help standardize execution, they do not offer the centralized view required here.
- ✓
MLflow Tracking UI
Why this is correct
The MLflow Tracking UI provides a centralized interface to view experiments, runs, metrics, parameters, and artifacts. It allows sorting and filtering runs by metrics, making it easy to identify the best run. From the UI, you can also register a model directly to the Model Registry. This directly addresses the engineer's need to compare and promote.
- ✗
MLflow Models
Why it's wrong here
MLflow Models is a standard format for packaging models for deployment. It defines how models are saved and loaded, but it does not provide a UI for comparing runs or metrics. It is a deployment artifact, not an experiment tracking interface.
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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
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