Microsoft Azure AI Engineer Associate AI-102 Practice Question

CLU always reserves a portion of the data (20 percent by default) as a blind test set. Because the CancelAppointment intent has fewer than the recommended 15 training examples, only a handful of test utterances are available after the split. With so few samples, a single misclassification can drop recall to 0 percent or raise it sharply in another run, making the reported metrics volatile. Adding more labeled utterances-especially for intents with fewer than 15 examples-will give the model more reliable training data and provide a larger test set, stabilizing the evaluation scores. The other options are incorrect because CLU does not switch to k-fold cross-validation, entity labels are not required for intent metrics, and standard training metrics are deterministic once the data volume is adequate.