Databricks-ML-Assoc ML Workflows Practice Question
A data scientist is using Databricks AutoML to train a classification model on a dataset with a binary target. They want to understand which features contributed most to the model's predictions and need a human-readable summary. Which AutoML output should they examine?
⚠ Common exam trap
The trap here is assuming that feature importance is stored in a generic artifact or the Model Registry, when AutoML provides it in the generated interpretability 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
✓
The AutoML-generated notebook that includes a section on model interpretability with SHAP values.
AutoML generates a notebook for each trial that includes model interpretability using SHAP. This notebook contains summary plots and feature importance rankings that are human-readable and explain the model's predictions. Examining this notebook is the correct way to understand feature contributions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The MLflow run's artifact directory, specifically the 'feature_importance.json' file.
Why it's wrong here
AutoML does not generate a file named feature_importance.json. While some models may log feature importances, the standard human-readable summary is provided in a notebook. The artifact directory contains model files and evaluation plots, but not a generic JSON summary of feature contributions.
- ✓
The AutoML-generated notebook that includes a section on model interpretability with SHAP values.
Why this is correct
Databricks AutoML generates a notebook for each trial that includes data exploration, model training, and interpretability using SHAP. The notebook provides summary plots and feature importance rankings, which are human-readable and explain how features affect predictions.
- ✗
The Databricks Feature Store UI, which shows feature lineage and importance scores.
Why it's wrong here
Feature Store UI shows lineage, metadata, and online/offline tables, but it does not compute or display feature importance for a specific model. Feature importance is model-specific and is provided by AutoML's interpretability notebook, not by Feature Store.
- ✗
The Model Registry's version description, which is automatically populated with feature importance by AutoML.
Why it's wrong here
The Model Registry stores model versions and metadata, but AutoML does not automatically populate version descriptions with feature importance. The registry is for managing model lifecycle, not for exploratory interpretability. The detailed feature importance is in the AutoML notebook.
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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
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