Databricks-ML-Assoc Model Development Practice Question
A data scientist is working in a Databricks notebook and wants to view the results of their MLflow runs, including metrics and parameters, directly within the notebook. Which MLflow function should they use?
⚠ Common exam trap
Candidates often confuse functions that list experiments or retrieve single runs with the one that returns a comprehensive table of runs for analysis.
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
✓
mlflow.search_runs()
mlflow.search_runs() is designed to query runs within an experiment and return a pandas DataFrame with columns for run ID, parameters, metrics, and other metadata. This makes it straightforward to display and analyze multiple runs in a notebook. Other functions either retrieve single runs or list experiments without providing run-level details in a tabular format.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
mlflow.search_runs()
Why this is correct
mlflow.search_runs() returns a pandas DataFrame containing run information such as parameters and metrics from the specified experiment. It can be used within a notebook to programmatically inspect and analyze runs. This function is ideal for displaying results directly in the notebook environment, as it provides a structured view of all runs.
- ✗
mlflow.get_experiment_by_name()
Why it's wrong here
mlflow.get_experiment_by_name() retrieves an experiment object by its name, including its ID and metadata. It does not provide run-level metrics or parameters. This function is typically used to obtain the experiment ID for further operations, but it does not directly show run results in the notebook.
- ✗
mlflow.get_run()
Why it's wrong here
mlflow.get_run() retrieves metadata for a single run by its run ID. While it can be used to fetch details, it does not provide a tabular view of multiple runs. To view results across runs in a notebook, a more comprehensive function like search_runs is needed. get_run is better for retrieving a specific run's details.
- ✗
mlflow.list_experiments()
Why it's wrong here
mlflow.list_experiments() lists all experiments in the tracking server, but it does not return run metrics or parameters. It is useful for discovering experiments, not for viewing run results. To see metrics and parameters, you need to query runs within an experiment, which is done with search_runs.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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