Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist is using Databricks AutoML to train a classification model on a dataset with a highly imbalanced target variable. They want to ensure the model evaluation focuses on the minority class. Which evaluation metric should they prioritize when interpreting AutoML results?
⚠ Common exam trap
The trap here is choosing accuracy or AUC because they are commonly used, without considering their insensitivity to class imbalance.
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
✓
F1 score
F1 score balances precision and recall, making it suitable for imbalanced classification where the minority class is of interest. AUC and accuracy can be misleading, and MSE is for regression. Prioritizing F1 ensures the model captures the minority class effectively.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Mean squared error (MSE)
Why it's wrong here
MSE is a regression metric and is not applicable to classification tasks. Using it for classification would be incorrect. Even if adapted, it would not directly address class imbalance. Thus, it is not a valid choice for evaluating a classification model.
- ✗
Accuracy
Why it's wrong here
Accuracy measures the proportion of correct predictions but is highly misleading for imbalanced data. A model that always predicts the majority class can achieve high accuracy while completely failing on the minority class. Therefore, accuracy is not appropriate here.
- ✓
F1 score
Why this is correct
F1 score is the harmonic mean of precision and recall, making it sensitive to both false positives and false negatives. For imbalanced datasets, it better reflects performance on the minority class because it emphasizes correctly identifying positive cases. Thus, prioritizing F1 helps ensure the model performs well on the minority class.
- ✗
Area under the ROC curve (AUC)
Why it's wrong here
AUC measures the overall ability to distinguish between classes, but it can be misleading for highly imbalanced datasets because it considers both classes equally. A high AUC may still correspond to poor minority class performance. Therefore, it is not the best metric to prioritize when focusing on the minority class.
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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.