The answer is that the positive examples (180) are insufficient for the number of fields (8). Einstein Discovery enforces a minimum of 50 positive examples per predictor field, so with eight fields, you need at least 400 positive examples—far more than the 180 provided. This requirement ensures the model has enough signal to learn meaningful patterns without overfitting. On the Salesforce AI Associate exam, this error tests your understanding of data readiness prerequisites for classification models, often appearing as a trap where candidates overlook the per-field multiplier. A common mistake is to assume only a total minimum count matters, but the rule scales with feature complexity. Remember the memory tip: “Fifty per field, or your model won’t yield.”
AI Associate Data for AI Practice Question
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.
Refer to the exhibit. A data scientist sees this error when training an Einstein Discovery model for customer churn prediction. What is the most likely reason for the error?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The positive examples (180) are insufficient for the number of fields (8).
Einstein Discovery requires at least 50 positive examples per predictor field. With 8 fields, at least 400 positive examples are needed. Only 180 were provided, causing the error.
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.
✗
The field count (8) exceeds the maximum of 5 allowed fields.
Why it's wrong here
Einstein Discovery allows up to 10 fields.
✓
The positive examples (180) are insufficient for the number of fields (8).
Why this is correct
50 per field * 8 = 400; 180 is below the threshold.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The model name contains a version number, which is not allowed.
Why it's wrong here
Model names can include version numbers.
✗
The dataset has too few records (3200) for 8 fields.
Why it's wrong here
3200 records is generally sufficient; the issue is positive example count.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The positive examples (180) are insufficient for the number of fields (8). — Einstein Discovery requires at least 50 positive examples per predictor field. With 8 fields, at least 400 positive examples are needed. Only 180 were provided, causing the error.
What should I do if I get this AI Associate question wrong?
Identify which AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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