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Answer: Streaming logs to Azure Event Hubs, processing them with Azure Databricks to identify anomalies using machine learning models, and visualizing results in Power BI
The optimal approach involves: 1. **Streaming logs to Azure Event Hubs** for real-time processing, crucial for timely anomaly detection. 2. **Processing logs with Azure Databricks**, leveraging its analytics and machine learning capabilities to identify anomalies. 3. **Visualizing results in Power BI** for actionable insights. This method combines real-time data ingestion, advanced analytics, and visualization for a comprehensive anomaly detection system.
Author: LeetQuiz Editorial Team
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How would you design a custom anomaly detection system to identify unusual patterns in your data pipeline processing logs within Azure Databricks?
A
Implementing a custom logging framework in Azure Databricks notebooks to detect anomalies and alert via email
B
Using Azure Monitor for basic anomaly detection without custom model implementation
C
Streaming logs to Azure Event Hubs, processing them with Azure Databricks to identify anomalies using machine learning models, and visualizing results in Power BI
D
Capturing logs in Azure Blob Storage, using Databricks for periodic analysis with custom models, and reporting anomalies through Azure Logic Apps
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