Databricks-ML-Assoc Databricks Machine Learning Practice Question
A machine learning engineer is training a scikit-learn model on a Databricks cluster and wants every hyperparameter, evaluation metric, and the fitted estimator itself to be captured automatically without writing custom logging code. The engineer uses MLflow with autologging enabled. Which statement best describes what MLflow autologging records for this scikit-learn run?
⚠ Common exam trap
The trap here is assuming autologging only captures parameters, when it also serializes the fitted model and metrics automatically.
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
✓
It logs hyperparameters, training metrics, the model signature, and the fitted model artifact automatically.
Scikit-learn autologging hooks into the training call and records hyperparameters, metrics, the inferred signature, and the fitted model artifact. Because the integration operates at the estimator level, the engineer does not need to add explicit logging calls to capture the model or its evaluation results, which directly fulfills the requirement of automatic capture during training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It logs the environment dependencies only, allowing reproduction but not artifact capture.
Why it's wrong here
Dependency capture is part of MLflow run tracking generally, but scikit-learn autologging additionally logs parameters, metrics, and the serialized model artifact. Claiming it logs only dependencies would omit the trained estimator and evaluation results the engineer explicitly wants. That makes this description incomplete and inaccurate for the scenario's requirement of capturing the model automatically.
- ✓
It logs hyperparameters, training metrics, the model signature, and the fitted model artifact automatically.
Why this is correct
MLflow scikit-learn autologging instruments the fit call and captures the estimator's hyperparameters, evaluation metrics computed during fitting, the inferred model signature, and serializes the fitted model as an artifact. This satisfies the engineer's goal of capturing everything without manual logging code, because the integration hooks into the training call itself rather than requiring explicit log_param or log_metric invocations.
- ✗
It logs only the hyperparameters passed to the estimator constructor and nothing else.
Why it's wrong here
Autologging for scikit-learn goes well beyond constructor arguments. It also records metrics produced during fitting and serializes the trained model, plus the signature. Limiting capture to constructor hyperparameters would leave the engineer without the fitted estimator artifact required for later deployment or scoring, so this does not meet the stated requirement of capturing the model itself.
- ✗
It logs metrics and artifacts but requires a separate call to register the model in Unity Catalog.
Why it's wrong here
Autologging does not automatically register models into Unity Catalog; that is a separate step performed with the MLflow API or the registry UI. However, the question asks what autologging records during the run. Registration is unrelated to the recording behavior, and autologging still captures metrics, parameters, signature, and the model artifact without extra logging code.
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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-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.