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NCA-AIIO · Question #51
NCA-AIIO Question #51: Real Exam Question with Answer & Explanation
The correct answer is B: ROC-AUC is insensitive to class imbalance and decision thresholds. ROC-AUC measures ranking ability rather than decision quality at a fixed threshold. In real deployment, class imbalance and threshold selection can severely degrade actual performance.
NVIDIA Certified Associate (NCA) Core AI Concepts
Question
A deep learning model achieves very high ROC-AUC but performs poorly in real-world deployment. Which is the MOST plausible explanation?
Options
- AThe model has too many parameters
- BROC-AUC is insensitive to class imbalance and decision thresholds
- CThe optimizer converged too fast
- DThe dataset is too large
Explanation
ROC-AUC measures ranking ability rather than decision quality at a fixed threshold. In real deployment, class imbalance and threshold selection can severely degrade actual performance.
Topics
#Model Evaluation#ROC-AUC#Class Imbalance#Decision Thresholds
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