AI Associate Ethical AI and Data Privacy Practice Question
When a Salesforce admin enables 'Score Factors' for an AI prediction, what does this provide to end users?
⚠ Common exam trap
A common misconception is that 'Score Factors' reveals the underlying model formula or provides statistical measures like confidence intervals, but it only shows the most influential fields and their impact.
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
✓
A list of the most influential fields and their contribution to the prediction
When 'Score Factors' is enabled for an AI prediction in Salesforce, end users see a breakdown of the most influential fields that contributed to the prediction, along with their relative contribution (e.g., positive or negative impact). This provides transparency into why a specific prediction was made, helping users trust and act on the AI's output without exposing the underlying model logic.
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 exact formula used by the model
Why it's wrong here
The formula is not typically exposed.
- ✗
A confidence interval for the prediction
Why it's wrong here
Confidence intervals indicate uncertainty, not explanation.
- ✗
A histogram of all prediction values
Why it's wrong here
Histograms show distribution, not explanation.
- ✓
A list of the most influential fields and their contribution to the prediction
Why this is correct
Score factors show which fields drove the prediction.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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