Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist is training a scikit-learn model on Databricks and wants to automatically log parameters, metrics, and models without writing explicit MLflow logging code. Which approach should they use?
⚠ Common exam trap
Many exam-takers confuse Databricks AutoML with MLflow autologging; AutoML builds models, while autologging tracks them.
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
✓
Enable MLflow autologging by calling mlflow.sklearn.autolog() before training.
MLflow autologging for scikit-learn is enabled by calling mlflow.sklearn.autolog() before training. This automatically logs parameters, metrics, and the model without explicit logging calls. It is the standard way to achieve automatic tracking in Databricks. Other options either require manual code, are for different purposes, or are not valid configurations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the Databricks AutoML feature to train the model.
Why it's wrong here
Databricks AutoML automates model selection and hyperparameter tuning, but it is a separate tool that generates notebooks and logs results. It does not automatically log a user's custom scikit-learn training code. The scenario specifies training a scikit-learn model manually, so AutoML is not the direct solution for automatic logging of that code.
- ✗
Manually log parameters and metrics using mlflow.log_param and mlflow.log_metric, and log the model with mlflow.sklearn.log_model.
Why it's wrong here
This approach requires explicit logging code, which contradicts the requirement to avoid writing logging code. While it is a valid method, it is not automatic. The question asks for an approach that logs without explicit code, so manual logging is not the best answer.
- ✓
Enable MLflow autologging by calling mlflow.sklearn.autolog() before training.
Why this is correct
MLflow autologging for scikit-learn automatically captures parameters, metrics, and models when you call fit(). Calling mlflow.sklearn.autolog() enables this for all subsequent scikit-learn training runs. This satisfies the requirement without manual logging code. Autologging works seamlessly within Databricks notebooks and is the recommended approach for quick experimentation.
- ✗
Configure the cluster to use MLflow integration by setting the environment variable MLFLOW_AUTOLOGGING=true.
Why it's wrong here
There is no such environment variable for MLflow autologging. Autologging is enabled programmatically per library (e.g., mlflow.sklearn.autolog()) or via the MLflow UI settings. Setting an environment variable is not a supported method and would not enable autologging.
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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-Assoc 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-Assoc exam.