Databricks-ML-Assoc Model Development Practice Question
A data scientist is training a scikit-learn model in a Databricks notebook and wants to automatically log parameters, metrics, and the model artifact to an MLflow experiment without writing explicit log calls. They have already installed the required libraries. Which approach should they use?
⚠ Common exam trap
The trap here is assuming that autologging can be enabled through a cluster-level Spark setting, when it actually requires a library-specific call in the notebook.
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
✓
Call mlflow.sklearn.autolog() before starting the training run.
Autologging in MLflow, when invoked for a specific library like scikit-learn, automatically records parameters, metrics, and the model artifact for each run. It requires calling the appropriate autolog function before training. This is the intended method in Databricks to reduce boilerplate and ensure consistent tracking. The other options either involve manual steps or non-existent 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.
- ✗
Register the model in the MLflow Model Registry immediately after training, which will retroactively populate the experiment with all training details.
Why it's wrong here
Registering a model in the Model Registry does not retroactively log parameters or metrics to the experiment. The registry stores model versions and metadata, but it does not capture training-time details. This option confuses model registration with experiment tracking, and it would not achieve the automatic logging requirement.
- ✗
Use the MLflow UI to create an experiment and then manually log each parameter and metric using mlflow.log_param and mlflow.log_metric.
Why it's wrong here
This approach requires manual logging calls, which contradicts the goal of avoiding explicit log statements. While it is a valid way to track experiments, it does not provide automatic logging. The scenario specifically asks for automatic capture without writing log calls, so manual logging is not the right solution.
- ✗
Set the Spark configuration spark.databricks.mlflow.autolog to true in the cluster settings.
Why it's wrong here
There is no Spark configuration property named spark.databricks.mlflow.autolog that enables MLflow autologging. Autologging is enabled programmatically per library via mlflow.<library>.autolog(), not through cluster-level Spark settings. This option misleads by suggesting a cluster-wide toggle that does not exist for this purpose.
- ✓
Call mlflow.sklearn.autolog() before starting the training run.
Why this is correct
MLflow's autologging for scikit-learn captures parameters, metrics, and the fitted model automatically when autolog is enabled before training. In Databricks, this integrates with the active experiment, eliminating manual logging calls. The scenario requires minimal code changes, and autolog satisfies that by hooking into the training process.
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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.