Databricks-ML-Assoc Model Development Practice Question
A machine learning engineer is using MLflow to track experiments on Databricks. They want to compare multiple runs and identify the best model based on a custom metric called 'weighted_f1'. They have logged this metric using mlflow.log_metric('weighted_f1', value) for each run. When viewing the experiment in the MLflow UI, they notice that the runs are not sorted by 'weighted_f1' and the metric does not appear in the runs table. What is the most likely cause?
⚠ Common exam trap
The trap here is assuming that any logged metric automatically appears in the runs table, when in fact the UI requires manual column selection for custom 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 metric was logged, but the MLflow UI runs table does not automatically display all metrics; the engineer must manually add the 'weighted_f1' column to the table.
The MLflow UI runs table does not show all logged metrics by default; it shows a default set. Custom metrics must be explicitly added as columns to be visible and sortable. The metric is likely logged correctly, but the UI configuration hides it. This is a frequent source of confusion when comparing runs with custom evaluation metrics.
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 metric name 'weighted_f1' contains an underscore, which is not allowed in MLflow metric names.
Why it's wrong here
MLflow metric names can contain underscores and other characters; there is no restriction against underscores. The issue is not the name but how the metric is logged. The metric should appear if logged correctly. Therefore, this is not the cause. Underscores are commonly used in metric names like 'f1_score' and are fully supported.
- ✗
The metric was not logged because the run was not active; mlflow.log_metric() only works within an active run context.
Why it's wrong here
This is a plausible cause: if the run was not active, the metric would not be logged. However, the scenario states that the engineer logged the metric for each run, implying they were within run contexts. If they were not, the metric would not be recorded, but then the runs would still exist with other logged data. The most specific cause is that the metric might have been logged with a different name or the UI requires column selection.
- ✓
The metric was logged, but the MLflow UI runs table does not automatically display all metrics; the engineer must manually add the 'weighted_f1' column to the table.
Why this is correct
This is correct because the MLflow UI runs table only displays a subset of metrics by default, typically those logged most frequently or a predefined set. To see a custom metric like 'weighted_f1', the user must click on the 'Columns' button and select it. Until then, it won't appear, and sorting by it is not possible. This is a common oversight when working with custom metrics.
- ✗
The metric was logged with a step value, causing it to be treated as a time-series metric and not displayed in the runs table by default.
Why it's wrong here
Logging with a step value creates a time-series metric, but it still appears in the runs table if it is the latest value. The runs table shows the last logged value for each metric. However, the scenario does not mention step. The absence from the table suggests the metric might not have been logged at all or the UI needs refreshing, but step is not the primary reason for it not appearing.
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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-Assoc 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-Assoc exam.