AI Associate AI Fundamentals Practice Question
A data scientist is evaluating the performance of an Einstein Discovery model. They observe that the model has high accuracy but low precision for a specific prediction class. What does this indicate?
⚠ Common exam trap
Salesforce often tests the distinction between precision and recall, trapping candidates who confuse false positives (low precision) with false negatives (low recall) when interpreting accuracy metrics.
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 model correctly predicts most instances but has many false positives for that class.
High accuracy with low precision for a specific class indicates that while the model correctly classifies the majority of instances overall, it produces a high number of false positives for that class. Precision measures the proportion of positive identifications that were actually correct, so low precision means many of the predicted positive cases are false alarms. In Einstein Discovery, this trade-off is critical when optimizing for business outcomes where false positives are costly.
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 model is overfitted to the training data.
Why it's wrong here
Overfitting doesn't specifically cause low precision.
- ✓
The model correctly predicts most instances but has many false positives for that class.
Why this is correct
Low precision indicates many false positives.
- ✗
The model correctly predicts most instances but has many false negatives for that class.
Why it's wrong here
Low recall indicates false negatives.
- ✗
The model rarely predicts that class, leading to high accuracy.
Why it's wrong here
Rare class can inflate accuracy.
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