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A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data. Streaming DataFrame df has the following schema: "device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT" Code block: Choose the response that correctly fills in the blank within the code block to complete this task.
Correct Answer: A
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References: * Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html * Windowed aggregations: https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html