AI Associate Salesforce Einstein AI Features Practice Question
A company uses Einstein Prediction Builder to predict which leads will convert. They have a binary outcome field 'Converted__c' which is true for 8% of leads. After training, the model shows high accuracy (95%) but very low precision for the positive class. What is the most likely cause?
⚠ Common exam trap
The trap here is that candidates see 'high accuracy' and assume the model is performing well, overlooking that accuracy is misleading in imbalanced datasets, and they may incorrectly attribute the issue to field configuration or dataset size.
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 data is imbalanced favoring the negative class
The dataset is imbalanced: only 8% of leads are positive (Converted__c = true), while 92% are negative. In such a scenario, a model can achieve 95% accuracy by simply predicting the majority class (negative) for all leads, but this yields very low precision for the positive class because it rarely predicts positive correctly. Einstein Prediction Builder, like most ML models, is sensitive to class imbalance, and without techniques like oversampling or threshold tuning, the model will favor the majority class.
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 prediction field is not a binary field
Why it's wrong here
The field is binary (true/false) as stated.
- ✓
The data is imbalanced favoring the negative class
Why this is correct
Imbalanced data causes the model to predict majority class most of the time, yielding high accuracy but low positive precision.
- ✗
The prediction score field is not configured correctly
Why it's wrong here
Prediction score field configuration does not affect model training accuracy/precision.
- ✗
The dataset is too small for training
Why it's wrong here
Small dataset may cause overfitting, but the described symptoms point to class imbalance.
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