Question

You operate a fraud-detection model on an Azure Machine Learning managed online endpoint. Confirmed fraud labels arrive weeks after scoring, so ground-truth accuracy cannot be evaluated in near real time. Leadership wants an early warning when the model's behavior shifts, without waiting for labels. Which monitoring signal should you configure in the Azure ML model monitor?

Accepted Answer

A prediction drift signal that compares the distribution of the model's output predictions against a baseline reference dataset

Answer

A model performance signal computing precision and recall against the incoming scoring requests

Answer

A data quality signal that checks null rates and type mismatches on input features

Answer

A feature attribution drift signal comparing SHAP importance values on the labeled test set