- A
Modify the lead assignment rule to only assign leads with scores above a higher threshold
Assignment rules can be based on the lead score field; raising the threshold ensures only higher-scored leads are assigned.
- B
Reduce the number of features used in scoring
Why wrong: Reducing features changes the model but does not directly control assignment threshold.
- C
Disable Einstein Lead Scoring and use a custom scoring model
Why wrong: Disabling the feature is unnecessary; adjusting the threshold is simpler and retains AI scoring.
- D
Create a new lead queue and manually review all leads
Why wrong: Manual review contradicts the goal of automation and efficiency.
Question 890 of 1,000
AI Associate Salesforce Einstein AI Features Practice Question
This AI Associate practice question tests your understanding of salesforce einstein ai features. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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 the feature is unnecessary; adjusting the threshold is simpler and retains AI scoring.
- ✗
Create a new lead queue and manually review all leads
Why it's wrong here
Manual review contradicts the goal of automation and efficiency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Einstein Lead Scoring uses a gradient-boosted machine learning model that outputs a probability score between 0 and 100. The assignment rule evaluates this score in real time via a formula field (e.g., `Lead_Score__c > 80`). By increasing the threshold to, say, 90, only leads with a higher predicted conversion likelihood are auto-assigned, reducing sales team overload while still benefiting from AI prioritization. This approach preserves the model's training and feature set, requiring only a metadata change to the assignment rule.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Salesforce Einstein AI Features — This question tests Salesforce Einstein AI Features — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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