Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer is building a Databricks Job that trains a model with scikit-learn and needs to capture hyperparameters, evaluation metrics, and the resulting model artifact for each run. The team wants to compare runs visually in the workspace and later promote the best model to the Model Registry. Which MLflow capability should the engineer use to record these per-run details?
⚠ Common exam trap
The trap here is assuming that the Model Registry or Feature Store performs experiment tracking, when in fact only MLflow Tracking logs the per-run parameters, metrics, and artifacts.
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 Tracking with autologging enabled for scikit-learn
Recording per-run parameters, metrics, and model artifacts is the job of MLflow Tracking, and the scikit-learn autologging integration captures these automatically for standard estimators. That gives the team comparable runs in the workspace experiment UI and a logged model artifact that can subsequently be registered and promoted through the Model Registry.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks Feature Store with online store enabled
Why it's wrong here
The Feature Store publishes feature tables and lets training code retrieve features consistently, but it does not record hyperparameters, evaluation metrics, or the trained model artifact for a run. Enabling an online store is about low-latency serving of feature values, not experiment tracking, so it cannot satisfy the comparison and promotion requirement described.
- ✓
MLflow Tracking with autologging enabled for scikit-learn
Why this is correct
MLflow Tracking records parameters, metrics, tags, and artifacts for each run, and the scikit-learn autologging integration automatically captures estimator hyperparameters, evaluation metrics, and the fitted model artifact. This satisfies the need to compare runs in the workspace UI and gives a model artifact that can be registered afterward, with no manual logging calls required for standard estimators.
- ✗
Databricks Jobs task dependencies with a condition task
Why it's wrong here
Task dependencies and condition tasks control execution order and branching inside a workflow, for example to run evaluation only after training succeeds. They do not store hyperparameters, metrics, or model artifacts and provide no run comparison UI, so they are the wrong construct for capturing per-run experiment information.
- ✗
MLflow Model Registry stage transitions
Why it's wrong here
Stage transitions such as Staging or Production operate on model versions already registered; they do not capture the parameters, metrics, or artifacts produced during training. Without tracking data first, there is nothing meaningful to compare in the workspace UI, so registry transitions alone cannot fulfill the requirement to record and compare per-run details.
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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.