AI Associate Ethical Considerations of AI Practice Question
A sales team uses Einstein Lead Scoring. They notice leads from certain industries are always low-scored. What should they do?
⚠ Common exam trap
Salesforce often tests the misconception that retraining or replacing the AI system is the solution to bias, when in fact the root cause lies in the training data, not the model or its update frequency.
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
✓
Review training data for bias
Low scores for specific industries often indicate bias in the training data, where historical lead data may have underrepresented or mislabeled those industries. Reviewing the training data for bias allows the team to identify and correct such imbalances, ensuring the Einstein Lead Scoring model produces fair and accurate predictions across all segments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retrain the model weekly
Why it's wrong here
Retraining without addressing bias perpetuates the problem.
- ✗
Ignore the scores
Why it's wrong here
Ignoring scores wastes the investment in AI.
- ✗
Use a different AI system
Why it's wrong here
Switching systems does not guarantee fairness if the issue lies in data.
- ✓
Review training data for bias
Why this is correct
Bias in training data can cause unfair scoring across industries.
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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.