AI Associate Salesforce Einstein AI Features Practice Question
A sales operations manager wants to improve the accuracy of Einstein Opportunity Scoring. Which TWO actions should they take? (Choose two.)
⚠ Common exam trap
Watch out — candidates often think more data is always better (Option C) or that manual adjustments can improve accuracy (Option E), but Salesforce specifically tests the understanding that model accuracy depends on balanced, high-quality training data and automated feature selection.
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
✓
Ensure that historical opportunity data includes both won and lost records
Einstein Opportunity Scoring is a predictive model that learns from historical opportunity data to identify patterns that lead to wins or losses. Including both won and lost records ensures the model has a balanced training set, which is essential for accurately distinguishing between likely wins and losses. Without lost records, the model would be biased and unable to effectively predict negative outcomes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ensure that historical opportunity data includes both won and lost records
Why this is correct
The model needs examples of both outcomes to learn effectively.
- ✗
Use Einstein Discovery to analyze the same data
Why it's wrong here
Discovery analyzes but does not improve the scoring model directly.
- ✗
Increase the number of records by duplicating existing opportunities
Why it's wrong here
Duplicating records does not add new information and can skew results.
- ✓
Select only the most relevant fields as factors in the model setup
Why this is correct
Choosing relevant features helps the model focus on important predictors.
- ✗
Manually override scores for high-value opportunities
Why it's wrong here
Manual overrides do not improve model accuracy.
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