Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist is using MLflow to track experiments on Databricks. They notice that the metrics logged during a run are not appearing in the MLflow UI. The run is part of an experiment with many runs. What is the most likely cause for the missing metrics?
⚠ Common exam trap
The trap here is assuming that metrics are lost due to logging errors, when the most common issue is simply viewing the wrong experiment in the 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 metrics were logged to a different experiment than the one currently selected in the MLflow UI.
Metrics are associated with a specific run, and runs belong to an experiment. If the run was created under a different experiment than the one selected in the MLflow UI, its metrics will not be visible. This often happens when the experiment is changed mid-session or when using a different tracking URI. Checking the experiment ID of the run resolves the issue.
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 metrics were logged with a step value that is not monotonically increasing, causing MLflow to ignore them.
Why it's wrong here
MLflow allows logging metrics with any step value, including non-monotonic steps, and will store them. The UI may display them out of order, but they are not ignored. This is not a valid reason for metrics to be missing entirely. Thus, this option is incorrect.
- ✓
The metrics were logged to a different experiment than the one currently selected in the MLflow UI.
Why this is correct
If metrics are logged to a run under a different experiment, they will not appear when viewing the current experiment. The MLflow UI filters runs by experiment ID. The data scientist likely set a different experiment via mlflow.set_experiment or environment variable, so the metrics are stored elsewhere. This is the most common cause of missing metrics in the UI.
- ✗
The metrics were logged as strings instead of numeric values, so MLflow silently drops them.
Why it's wrong here
MLflow requires metrics to be numeric. If a non-numeric value is passed, MLflow raises an error rather than silently dropping it. The data scientist would see an exception during logging. Therefore, this would not result in missing metrics without any indication, making it an unlikely cause.
- ✗
The metrics were logged using mlflow.log_metric but the run was not properly ended, so they are still buffered.
Why it's wrong here
Metrics logged with mlflow.log_metric are sent to the tracking server immediately, even if the run is not ended. They should appear in the UI once logged, though the run status may show as running. Buffering is not a typical behavior. Therefore, this is unlikely to be the cause of missing metrics.
Visual reference
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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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