Databricks-ML-Assoc ML Workflows Practice Question
A data scientist wants to compare the accuracy, F1 score, and training duration of several model training runs side by side in a single table, and visually inspect how a hyperparameter affected the metric across runs. Which MLflow capability should be used?
⚠ Common exam trap
It's easy for candidates to confuse the Model Registry, which manages versions and stages, with the experiment UI, which is built for comparing runs and metrics.
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 MLflow Experiment page, where runs can be selected and compared using the metrics table and chart views.
The MLflow Experiment page aggregates all runs with their parameters and metrics, supports selecting multiple runs for a side-by-side table, and offers chart views that plot metrics against parameters. This is the native way to compare accuracy, F1, and training duration and to visualize hyperparameter effects without writing custom code.
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 Databricks Jobs UI, where each task run records the metrics emitted by the notebook and displays them in a comparison chart.
Why it's wrong here
The Jobs UI shows task execution status, logs, and duration, not MLflow metrics such as accuracy or F1. It does not aggregate metrics across runs or plot them against hyperparameters. Comparing model quality requires the MLflow experiment view, not the job run history.
- ✗
The MLflow Tracking Server's REST API, which must be queried programmatically to retrieve and compare run metrics.
Why it's wrong here
The REST API can retrieve run data, but it returns JSON that must be parsed and visualized with external tooling. It does not offer an interactive table or chart view out of the box, so it does not directly satisfy the requirement to compare runs side by side and inspect hyperparameter effects visually.
- ✗
The Databricks Model Registry, where each model version lists its metrics and can be compared against other versions in a table.
Why it's wrong here
The Model Registry tracks versions of registered models and their stage transitions, along with limited metadata, but it is not designed for exploratory comparison of many runs or for plotting metrics against hyperparameters. It lacks the tabular run comparison and charting features that the experiment page provides.
- ✓
The MLflow Experiment page, where runs can be selected and compared using the metrics table and chart views.
Why this is correct
The MLflow Experiment UI lists all runs with their parameters, metrics, and tags, and supports selecting multiple runs for side-by-side comparison. It also provides chart views that plot a metric against a parameter, which directly answers the need to compare accuracy, F1, and duration and to see hyperparameter effects.
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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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