ML technique that identifies unusual or atypical patterns in data that deviate significantly from expected behavior. Can be supervised (with anomaly examples) or unsupervised.
In AI-901 it appears as a machine learning concept, not as a service: Azure AI Anomaly Detector is retired on October 1, 2026 and Microsoft recommends anomaly detection in Microsoft Fabric (Real-Time Intelligence). Use cases: detecting fraudulent transactions, failures in industrial equipment, or anomalous spikes in server metrics. Question: automatically detect fraudulent transactions → anomaly detection.
Anomaly detection is a concept that can show up on the exam. Check the full guide to see it in context.