Databricks-ML-Pro Model Development Practice Question
A data scientist is training a model with scikit-learn on Databricks and wants to track the experiment using MLflow. They call mlflow.start_run() and then train the model. After training, they call mlflow.log_param() and mlflow.log_metric(), but later find that the run is not visible in the MLflow experiment UI. What is the most likely reason?
⚠ Common exam trap
The trap here is assuming that forgetting to end the run hides it, when in fact running runs are visible; the real issue is often misconfigured experiment or tracking URI.
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
✓
They did not set the MLflow tracking URI or experiment, so the run was logged to a different experiment or the default location.
The most common reason for not seeing a run in the expected experiment is that the tracking URI or experiment was not set correctly. MLflow defaults to a specific experiment, and if the user is looking at a different one, the run appears missing. Explicitly setting the experiment with mlflow.set_experiment() or the tracking URI ensures the run is logged to the intended location. Other options would typically cause errors or still show the run.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
They forgot to call mlflow.end_run() to close the run, so it remains in a running state and is not displayed.
Why it's wrong here
Runs in a running state are still visible in the MLflow UI; they are shown as active. Forgetting to call end_run() means the run stays open and may be automatically terminated later, but it would still appear. The UI typically shows running runs with a green dot. Therefore, this is not the reason for invisibility.
- ✓
They did not set the MLflow tracking URI or experiment, so the run was logged to a different experiment or the default location.
Why this is correct
If the tracking URI or experiment is not explicitly set, MLflow logs to the default experiment (often ID 0) or to a local file store. On Databricks, the default is usually the workspace's default experiment, but if the user changed the experiment or is using a different tracking server, the run may be in another experiment. This is the most common cause of runs not appearing where expected.
- ✗
They called mlflow.log_param() and mlflow.log_metric() outside of the active run context, so the data was discarded.
Why it's wrong here
If these calls are made outside an active run, MLflow raises an exception or logs to the last active run if one exists. In most cases, it would error out. Since the scenario implies the calls succeeded, they were likely within the run context. The issue is not the logging calls but where the run was recorded.
- ✗
The model training did not produce any metrics, so MLflow skipped creating the run.
Why it's wrong here
MLflow creates a run as soon as mlflow.start_run() is called, regardless of whether any metrics are logged. Even if no metrics are logged, the run exists with its parameters and tags. The absence of metrics does not prevent the run from appearing in the UI; it would simply show no metrics.
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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-Pro 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-Pro exam.