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Answer: Utilize a machine learning model within the streaming query to continuously update detection thresholds based on incoming data.
Option C is the most suitable for advanced analysis of streaming data anomalies using Spark Structured Streaming for real-time anomaly detection. By leveraging a machine learning model within the streaming query, the system can dynamically adjust detection thresholds based on incoming data. This method allows for real-time updates to thresholds, adapting to new data patterns and trends without manual intervention. Machine learning models excel in learning from historical data and adjusting to evolving conditions, making them ideal for detecting anomalies in streaming data. This approach ensures that the system remains responsive to changes in data patterns, enhancing the accuracy and efficiency of anomaly detection.
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For real-time anomaly detection in streaming data using Spark Structured Streaming, which technique enables the dynamic adjustment of detection thresholds based on historical data patterns?
A
Store historical data in a Delta Lake table, periodically reading it to update a broadcast variable with new thresholds.
B
Implement a static threshold that is manually adjusted on a regular schedule based on historical trends.
C
Utilize a machine learning model within the streaming query to continuously update detection thresholds based on incoming data.
D
Broadcast a dataset containing historical anomalies and use a UDF to compare streaming data against this static dataset.
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