AI Associate AI Fundamentals Practice Question
A company uses an NLP model to detect customer intent from chat messages. The model correctly identifies 'billing question' 90% of the time for actual billing questions, but also flags many non-billing messages as billing (false positives). Which metric should the team prioritize to reduce false alarms?
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
✓
Precision
Precision focuses on the proportion of positive identifications that are correct; improving precision reduces false positives.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Accuracy
Why it's wrong here
Accuracy may be high if billing is rare; misleading for imbalance.
- ✗
F1 score
Why it's wrong here
F1 balances precision and recall; but if false positives are the issue, precision is the direct metric.
- ✓
Precision
Why this is correct
Correct. Precision = TP/(TP+FP); higher precision means fewer false alarms.
- ✗
Recall
Why it's wrong here
Recall measures true positives among actual positives; does not address false positives.
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