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Answer: Implement a custom timestamp policy.
Implementing a custom timestamp policy in Azure Stream Analytics allows you to define how the system should handle timestamps for late-arriving data. This ensures that the system can process late data without delaying the processing of current data, maintaining the accuracy of real-time analytics.
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You are working on a project where the data arrives with varying latency, including late-arriving data that can affect the accuracy of real-time analytics. How would you configure your Azure Stream Analytics job to handle late-arriving data without causing delays in processing current data?
A
Increase the watermark delay threshold.
B
Use a tumbling window to group data.
C
Implement a custom timestamp policy.
D
Filter out late-arriving data.