Quick Answer
The answer is 250, as this is the minimum number of positive outcomes typically required for a reliable Einstein Discovery model. This threshold is based on the rule of thumb that you need at least 10 events per predictor variable (EPV) to avoid overfitting, but with 10,000 records and 5 predictors, best practice demands a more robust 5% positive outcome rate—250 cases—to ensure statistical power and model stability. On the Salesforce AI Associate exam, this question tests your understanding of data preparation requirements for binary classification, often appearing as a trap where candidates mistakenly choose the lower EPV-derived number like 50. The key insight is that Einstein Discovery, like most predictive models, needs a sufficient absolute count of positive outcomes, not just a ratio. Remember the memory tip: “Five percent for stability”—when in doubt, aim for 5% of your total records as the minimum positive outcome floor.
This AI Associate practice question tests your understanding of data for ai. 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.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
250
50 per predictor * 5 predictors = 250 positive outcomes.
500
Why wrong: 500 is more than necessary; can be okay but not the minimum.
100
Why wrong: 100 is insufficient for 5 predictors.
50
Why wrong: 50 is too low; it is the minimum per predictor, not total.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
250
Option A is correct because 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.
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.
250
Why this is correct
50 per predictor * 5 predictors = 250 positive outcomes.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
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.
Detailed technical explanation
Under the hood, Einstein Discovery uses logistic regression or gradient-boosted trees, and the 10 events per predictor variable (EPV) rule is a heuristic to prevent overfitting and ensure the model generalizes. In real-world scenarios, if the outcome is rare (e.g., fraud detection with <1% positive rate), the dataset may need to be resampled or synthetic data generated to meet the minimum positive outcome threshold. The 250 minimum is also tied to the Central Limit Theorem, ensuring the sampling distribution of the coefficients is approximately normal.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
The correct answer is: 250 — Option A is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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