Databricks-ML-Assoc ML Workflows Practice Question
A data scientist wants to compare the performance of three hyperparameter configurations for a Spark ML model. They need a central place to view metrics like RMSE and MAE across runs, and to filter runs by parameters. Which Databricks capability should they use?
⚠ Common exam trap
Watch out — candidates often confuse the Model Registry or Jobs with the experiment tracking UI, when only MLflow Tracking provides run comparison and metric visualization.
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 with the Databricks-hosted experiment UI
MLflow Tracking, accessed through the Databricks experiment UI, is the component that logs and visualizes run metrics, parameters, and artifacts. It enables filtering and side-by-side comparison of runs, which is exactly what the data scientist needs for evaluating hyperparameter configurations using RMSE and MAE.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks Jobs with task dependencies
Why it's wrong here
Jobs orchestrate tasks such as notebooks or Python scripts but do not store or visualize run metrics. They can trigger training runs, but comparing RMSE and MAE across configurations requires a tracking service. Jobs alone do not offer the experiment UI needed for metric comparison.
- ✓
MLflow Tracking with the Databricks-hosted experiment UI
Why this is correct
MLflow Tracking records parameters, metrics, and artifacts for each run. The Databricks-hosted MLflow experiment UI provides side-by-side run comparison, metric charts, and filtering by parameters. This directly satisfies the need to compare hyperparameter configurations using RMSE and MAE in a central place.
- ✗
Databricks Feature Store
Why it's wrong here
Feature Store manages feature tables and lineage for training and serving, not run comparison. It does not store per-run metrics or provide a UI to compare hyperparameter configurations. While it can log which features were used, it is not the tool for viewing RMSE and MAE across experiment runs.
- ✗
Databricks Model Registry
Why it's wrong here
The Model Registry manages model versions, stages, and deployment metadata. It does not provide run-level metric comparison or hyperparameter filtering. You can view a model version's source run, but the registry is not designed for exploring multiple runs' RMSE and MAE side by side.
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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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