This AI Associate practice question tests your understanding of ethical considerations of ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Increase model complexity
Why wrong: Complexity does not directly address bias from imbalanced data.
Add more features
Why wrong: More features may or may not reduce bias; the core issue is data imbalance.
Remove the segment from training
Why wrong: Removing the segment excludes it entirely, which is not fair.
Retrain with balanced data
Correct. Balanced data helps the model perform consistently across segments.
Answer choices
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
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
Under the hood, Einstein Prediction Builder uses automated machine learning (AutoML) with gradient-boosted trees or logistic regression, where class imbalance can skew decision thresholds toward the majority class. Retraining with balanced data (e.g., via oversampling the minority segment or undersampling the majority) adjusts the prior probabilities, leading to more calibrated probability estimates for the underrepresented segment. In a real-world scenario, a telecom company might use SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic examples of the churning segment, ensuring the model learns genuine patterns rather than ignoring them.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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What to study next
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FAQ
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Read the scenario before looking for a memorised answer..
The correct answer is: Retrain with balanced data — Option D is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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.
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