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How would you implement advanced anomaly detection and alerting on streaming metrics in a Databricks pipeline monitoring real-time IoT device data?
A
Sending streaming data to Azure Event Hubs and using Azure Stream Analytics with Azure Machine Learning for anomaly detection
B
Implementing Spark Structured Streaming with built-in statistical functions for real-time anomaly detection
C
Configuring static thresholds in Azure Monitor for alerting on key metrics
D
Utilizing Azure Databricks' MLflow to train and deploy a machine learning model that predicts anomalies based on historical data
