AI Associate Ethical AI and Data Privacy Practice Question
A sales operations team notices that an Einstein Lead Scoring model assigns lower scores to leads from a particular geographic region, even though those leads have historically converted at a higher rate. What is the most likely cause of this discrepancy?
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 model has been trained on biased historical data that underrepresents or undervalues leads from that region
The model was trained on historical data that may have reflected past biases (e.g., underinvestment in that region). The biased training data leads to unfair predictions. The correct action is to audit the model for bias.
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The model is using outdated lead source fields
Why it's wrong here
Outdated fields could degrade accuracy but wouldn't necessarily cause a systematic regional bias.
- ✗
The model's threshold for lead conversion is set too high
Why it's wrong here
Threshold adjustment affects overall score distribution, not a systematic bias against a specific region.
- ✓
The model has been trained on biased historical data that underrepresents or undervalues leads from that region
Why this is correct
Biased training data is the classic cause of unfair predictions; the model learned patterns from historical decisions that may have been biased.
- ✗
The model is overfitted to noise in the data
Why it's wrong here
Overfitting typically causes poor performance on new data, not a systematic bias against a specific region.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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