AI Associate Data for AI Practice Question
A data scientist is preparing data for Einstein Discovery. The dataset has 10,000 records with 5 predictors and one outcome. The outcome is binary (1/0). What is the minimum number of positive outcomes typically required for a reliable model?
⚠ Common exam trap
Salesforce often tests the 10 events per predictor variable (EPV) rule, but the trap here is that candidates mistakenly apply the EPV rule directly (50 for 5 predictors) without considering the additional requirement for a minimum of 250 positive outcomes to ensure model reliability in Einstein Discovery.
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
✓
250
For binary classification with 10,000 records and 5 predictors, a common rule of thumb in predictive modeling (including Einstein Discovery) is to have at least 10 events per predictor variable (EPV). With 5 predictors, you need at least 50 positive outcomes, but to ensure model stability and reliable training, a minimum of 250 positive outcomes (5% of 10,000) is typically required. This aligns with best practices for avoiding overfitting and achieving adequate statistical power.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
250
Why this is correct
50 per predictor * 5 predictors = 250 positive outcomes.
- ✗
500
Why it's wrong here
500 is more than necessary; can be okay but not the minimum.
- ✗
100
Why it's wrong here
100 is insufficient for 5 predictors.
- ✗
50
Why it's wrong here
50 is too low; it is the minimum per predictor, not total.
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