AI Associate Ethical Considerations of AI Practice Question
A company uses Einstein Prediction Builder to predict customer churn. They notice the model is less accurate for a certain segment. What is the best approach to mitigate bias?
⚠ Common exam trap
Salesforce often tests the misconception that bias is a technical problem solvable by adding complexity or features, when in fact it is a data representation issue requiring balanced training data.
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
✓
Retrain with balanced data
Retraining with balanced data directly addresses the root cause of bias: an imbalanced training set where the model underperforms for a specific segment. By ensuring the segment is adequately represented, the model learns more equitable patterns, reducing bias without sacrificing overall accuracy. This aligns with ethical AI practices in Einstein Prediction Builder, where data quality and representation are critical for fair predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase model complexity
Why it's wrong here
Complexity does not directly address bias from imbalanced data.
- ✗
Add more features
Why it's wrong here
More features may or may not reduce bias; the core issue is data imbalance.
- ✗
Remove the segment from training
Why it's wrong here
Removing the segment excludes it entirely, which is not fair.
- ✓
Retrain with balanced data
Why this is correct
Correct. Balanced data helps the model perform consistently across segments.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.