AI Associate Data for AI Practice Question
Exhibit
{
"model": "EinsteinDiscovery_Churn_v2",
"status": "TRAINING_FAILED",
"errorCode": "INSUFFICIENT_POSITIVE_EXAMPLES",
"fieldCount": 8,
"recordCount": 3200,
"positiveExamples": 180
}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?
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
✓
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.
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.
- ✗
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.
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