Databricks-ML-Pro Model Development Practice Question
A data scientist wants to use MLflow to track a scikit-learn model training run on Databricks. They call `mlflow.sklearn.autolog()` before training. Which of the following will MLflow automatically log for this run?
⚠ Common exam trap
The trap here is assuming that autologging captures everything about the training process, including source code and custom visualizations, when it actually focuses on parameters, metrics, and the model artifact.
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 model signature, input examples, and the trained model artifact.
`mlflow.sklearn.autolog()` automatically logs parameters, metrics, the model signature, input examples, and the trained model artifact. It does not log source code, Git commit hash, hyperparameter search spaces, or custom plots like feature importance. This automation streamlines experiment tracking and ensures that key model metadata is captured without manual intervention.
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 source code of the training script and the Git commit hash.
Why it's wrong here
MLflow does not automatically log source code or Git commit hash unless explicitly configured with `mlflow.log_artifact` or using `mlflow.source.git.commit` tag. Autologging focuses on model parameters, metrics, and artifacts. While MLflow can capture Git information if the code is in a Git repository, it is not part of `mlflow.sklearn.autolog()` by default.
- ✓
The model signature, input examples, and the trained model artifact.
Why this is correct
`mlflow.sklearn.autolog()` automatically logs the model signature, input examples, and the trained model artifact, along with parameters and metrics. This reduces manual logging and ensures reproducibility. The signature and input examples are inferred from the training data. This is the primary benefit of autologging for scikit-learn models.
- ✗
The hyperparameter tuning search space and the best parameters from a grid search.
Why it's wrong here
Autologging does not automatically log the search space or best parameters from hyperparameter tuning. It logs the parameters passed to the model constructor and the resulting metrics. If you use `mlflow.sklearn.autolog()` with a grid search, it may log each fit, but the search space itself is not captured. You would need to log that manually.
- ✗
The feature importance plot and confusion matrix for classification models.
Why it's wrong here
MLflow autologging does not automatically generate or log feature importance plots or confusion matrices. These are model-specific visualizations that require additional code to compute and log. Autologging captures standard metrics like accuracy, precision, recall, and the model artifact. Visualizations must be logged manually using `mlflow.log_figure`.
About these practice questions
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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.