Question 914 of 753
AI Associate Ethical Considerations of AI Practice Question
A company uses Einstein Prediction Builder to recommend products. They notice the model often recommends high-priced items to users in affluent areas, potentially excluding others. What should the AI Associate do first?
⚠ Common exam trap
Salesforce often tests the misconception that adding more features or immediately removing the model is the right fix, when the correct first step is always to audit the training data for bias and representation.
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
✓
Check the training data for representation and bias.
The correct first step is to check the training data for representation and bias because the model's tendency to recommend high-priced items to affluent areas suggests the training data may be skewed or contain historical biases. Einstein Prediction Builder relies on historical data to learn patterns, and if the data over-represents affluent users or under-represents others, the model will perpetuate those biases. Auditing the data for fairness and representation is the foundational step before any remediation, as per responsible AI practices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the model from production immediately.
Why it's wrong here
Removing without analysis may be unnecessary and disrupt business.
- ✗
Ignore the issue because the model predictions are accurate overall.
Why it's wrong here
Accurate overall does not mean fair; disparity is still a concern.
- ✗
Add more features about customer income.
Why it's wrong here
Adding income features could exacerbate bias, not reduce it.
- ✓
Check the training data for representation and bias.
Why this is correct
Addressing data bias is the first step per Salesforce ethical AI guidelines.
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Last reviewed: Jun 30, 2026
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