Databricks-ML-Assoc Model Development Practice Question
A machine learning engineer is using MLflow to track experiments. They want to compare multiple runs and identify the run that produced the best model based on a custom metric called 'weighted_f1'. They have logged this metric using mlflow.log_metric. Which MLflow UI feature allows them to sort and filter runs by this metric to quickly find the best run?
⚠ Common exam trap
Candidates often confuse manual documentation features like notes with automated comparison tools in the MLflow UI.
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 with column sorting and the filter box using metric.weighted_f1.
The MLflow UI runs table provides a sortable and filterable view of all runs in an experiment. By sorting on the 'weighted_f1' metric column or using a filter expression, the engineer can instantly locate the top-performing run. This is the standard way to compare runs and select the best model.
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 experiment notes field where you can manually record the best run ID.
Why it's wrong here
Experiment notes are free-text and not searchable or sortable by metric values. They do not provide a systematic way to compare runs. While useful for documentation, they cannot automatically identify the best run based on a metric.
- ✓
The runs table with column sorting and the filter box using metric.weighted_f1.
Why this is correct
The MLflow UI runs table allows sorting by any logged metric column and filtering using syntax like metrics.weighted_f1 > 0.8. This enables quick identification of the best run. The metric must be logged as a numeric value for sorting and filtering to work.
- ✗
The model registry's stage transitions, which automatically promote the run with the highest metric.
Why it's wrong here
The model registry does not automatically promote runs based on metrics; stage transitions are manual or triggered by custom code. It also requires the model to be registered first. This does not help compare runs within an experiment.
- ✗
The artifact viewer, which displays metric plots and allows sorting by value.
Why it's wrong here
The artifact viewer shows logged artifacts such as plots or files, but it does not provide a sortable table of runs. Metric plots are static images unless logged as interactive artifacts. Sorting runs by metric is done in the runs table, not the artifact viewer.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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