Built-in Functions for Anomaly Detection ML Model Integration with Azure Stream Analytics

Correct Answer: A.

The correct answer for the integration of Anomaly detection ML model with Azure Stream Analytics is A. AnomalyDetection_SpikeAndDip and AnomalyDetection_ChangePoint functions.

Azure Stream Analytics is a cloud-based service that enables real-time processing of streaming data from various sources. It allows users to create real-time analytics solutions that can process and analyze large volumes of data streams in real-time. Azure Stream Analytics provides built-in functions that can be used to perform various types of data analysis, including anomaly detection.

Anomaly detection is the process of identifying unusual patterns or events in data that deviate from expected behavior. Machine learning algorithms can be used to identify such anomalies in real-time data streams. Azure Stream Analytics supports the integration of machine learning models for anomaly detection using two built-in functions:

1. AnomalyDetection_SpikeAndDip function: This function detects anomalies in data streams by identifying sudden spikes or dips in the data. It is useful for detecting sudden changes in data patterns.

2. AnomalyDetection_ChangePoint function: This function detects anomalies in data streams by identifying change points in the data. A change point is a time point where the data distribution changes significantly. This function is useful for detecting gradual changes in data patterns.

Both of these functions take input from a data stream and apply machine learning algorithms to identify anomalies in real-time. They can be used in Azure Stream Analytics queries to detect anomalies in data streams from various sources, including IoT devices, social media feeds, and more.

Option B, AnomalyDetection_Windows() and AnomalyDetection_Session() functions are not correct because these functions are not related to anomaly detection. AnomalyDetection_Windows() is used to specify the time window for a query, while AnomalyDetection_Session() is used to specify the session window for a query.

Option C, CreateLineString and CreatePoint functions are not related to anomaly detection or Azure Stream Analytics. These functions are related to spatial data analysis.

Option D, ST_DISTANCE and ST_OVERLAPS functions are related to spatial data analysis and not anomaly detection or Azure Stream Analytics. ST_DISTANCE is used to calculate the distance between two spatial objects, while ST_OVERLAPS is used to check if two spatial objects overlap.