AI Associate Salesforce Einstein AI Features Practice Question
An organization uses Einstein Lead Scoring and notices that leads with a score above 80 are being sent to the sales team too quickly, overwhelming them. The admin wants to adjust when leads are automatically assigned. What should the admin do?
⚠ Common exam trap
Candidates often think the solution involves modifying the scoring model itself (e.g., reducing features or disabling it) rather than simply adjusting the assignment rule threshold, which is the direct and minimal-change fix.
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
✓
Modify the lead assignment rule to only assign leads with scores above a higher threshold
Einstein Lead Scoring assigns a score (0–100) to each lead based on predictive models. The default assignment rule triggers when a lead's score exceeds a threshold (e.g., 80). To reduce the volume of leads sent to sales, the admin should raise that threshold in the lead assignment rule so only higher-scored leads are automatically assigned. This directly controls the flow without altering the scoring model itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Modify the lead assignment rule to only assign leads with scores above a higher threshold
Why this is correct
Assignment rules can be based on the lead score field; raising the threshold ensures only higher-scored leads are assigned.
- ✗
Reduce the number of features used in scoring
Why it's wrong here
Reducing features changes the model but does not directly control assignment threshold.
- ✗
Disable Einstein Lead Scoring and use a custom scoring model
Why it's wrong here
Disabling Einstein Lead Scoring removes the predictive machine-learning model that analyses lead engagement and demographic data, yet the scenario requires only adjusting the threshold at which a score triggers automatic assignment—not replacing the scoring engine itself. A custom scoring model would be correct if the organisation needed to define its own lead attributes and weightings because the default model lacked relevant fields, but here the existing model’s score is already accurate; the problem is purely the assignment trigger point, which can be modified in the scoring settings without disabling the feature.
- ✗
Create a new lead queue and manually review all leads
Why it's wrong here
Manual review contradicts the goal of automation and efficiency.
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