Databricks-ML-Pro Model Development Practice Question
A machine learning engineer is training a model using scikit-learn on Databricks and wants to track the model's hyperparameters, metrics, and artifacts automatically without adding explicit logging calls. Which MLflow feature should they use?
⚠ Common exam trap
It's easy for candidates to confuse MLflow tracking APIs like MlflowClient or log_artifact with autologging, which is the only feature that automatically captures scikit-learn training details.
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.sklearn.autolog()
mlflow.sklearn.autolog() is the correct choice because it automatically logs scikit-learn model parameters, metrics, and artifacts. The other options are either low-level APIs that require manual logging or functions that only set experiment context or log individual artifacts, none of which provide automatic tracking.
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.tracking.MlflowClient()
Why it's wrong here
MlflowClient is a low-level API for interacting with the MLflow tracking server. It allows you to create runs, log parameters, and log metrics, but it requires explicit calls for each item. It does not automatically capture anything, so it does not meet the requirement of automatic logging without added code.
- ✗
mlflow.set_experiment()
Why it's wrong here
mlflow.set_experiment() sets the active experiment for subsequent runs. It does not enable any automatic logging. It is used to organize runs into experiments, but parameters, metrics, and models must still be logged manually or via autologging, so it does not fulfill the requirement.
- ✗
mlflow.log_artifact()
Why it's wrong here
mlflow.log_artifact() logs a single file as an artifact to the current run. It does not capture parameters, metrics, or the model automatically. The engineer would still need to manually log hyperparameters and metrics, and call log_artifact for each file, which is not automatic tracking.
- ✓
mlflow.sklearn.autolog()
Why this is correct
mlflow.sklearn.autolog() automatically logs parameters, metrics, and the model artifact for scikit-learn models. It captures hyperparameters from the estimator, metrics from scoring functions, and saves the trained model. This eliminates the need for manual logging calls and is the standard way to track scikit-learn experiments in Databricks.
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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
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.