Quick Answer
The answer is to regularly review and provide feedback on lead conversions. This is correct because Einstein Lead Scoring relies on a balanced training dataset containing both converted and unconverted leads to learn the positive and negative patterns that distinguish high-quality prospects from those unlikely to close. Without feedback on unconverted leads, the model cannot identify negative signals, resulting in biased predictions and poor accuracy. On the Salesforce AI Associate exam, this concept tests your understanding of supervised machine learning fundamentals—specifically that predictive models require labeled examples of both outcomes to avoid overfitting to conversions alone. A common trap is assuming the model can self-correct without human input, but Einstein Lead Scoring explicitly depends on ongoing user feedback to refine its scoring logic. For best practices, remember the “both sides” rule: always feed the model converted and unconverted leads, and treat feedback as a continuous loop, not a one-time setup. A useful memory tip is “no negative, no learning”—without unconverted leads, the model cannot learn what to avoid.
This AI Associate practice question tests your understanding of ai fundamentals. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
A sales team is implementing Einstein Lead Scoring. Which two actions should they take to ensure the model is effective? (Choose 2)
Include only demographic data for scoring.
Why wrong: Demographic only is insufficient.
Set a fixed score threshold for all users.
Why wrong: Threshold should be adjustable.
Disable the model for low-volume leads.
Why wrong: Disabling reduces coverage.
Ensure the training data includes both converted and unconverted leads.
Balanced data is necessary.
Regularly review and provide feedback on lead conversions.
Feedback retrains the model.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Ensure the training data includes both converted and unconverted leads.
Option D is correct because Einstein Lead Scoring requires training data that includes both converted and unconverted leads to build a predictive model that can distinguish between leads likely to convert and those that are not. Without unconverted leads, the model cannot learn the negative patterns, leading to biased predictions and poor accuracy.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Include only demographic data for scoring.
Why it's wrong here
Demographic only is insufficient.
Set a fixed score threshold for all users.
Why it's wrong here
Threshold should be adjustable.
Disable the model for low-volume leads.
Why it's wrong here
Disabling reduces coverage.
Ensure the training data includes both converted and unconverted leads.
Why this is correct
Balanced data is necessary.
Related concept
Read the scenario before looking for a memorised answer.
Regularly review and provide feedback on lead conversions.
Why this is correct
Feedback retrains the model.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Salesforce often tests the misconception that Einstein Lead Scoring can work effectively with only positive examples (converted leads), but the model fundamentally requires both positive and negative examples to learn the difference between high- and low-quality leads.
Detailed technical explanation
Einstein Lead Scoring uses a gradient-boosted decision tree algorithm trained on historical lead records, where each lead is labeled as converted or unconverted. The model automatically selects the most predictive features from standard and custom fields, including activity data, and outputs a score between 1 and 99. A common subtlety is that the model requires a minimum of 500 converted and 500 unconverted leads in the training dataset to achieve statistical significance; otherwise, it may fall back to a rule-based scoring approach.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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FAQ
AI Fundamentals — This question tests AI Fundamentals — Read the scenario before looking for a memorised answer..
The correct answer is: Ensure the training data includes both converted and unconverted leads. — Option D is correct because Einstein Lead Scoring requires training data that includes both converted and unconverted leads to build a predictive model that can distinguish between leads likely to convert and those that are not. Without unconverted leads, the model cannot learn the negative patterns, leading to biased predictions and poor accuracy.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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