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Databricks Certified Data Engineer Professional · Databricks
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Correct: B - The 'window()' function creates time-based intervals for grouping streaming data. When you specify window("event_time", "5 minutes"), Spark automatically divides the event timeline into non-overlapping 5-minute buckets and groups events that fall within each bucket. Incorrect: A - 'to_interval' doesn't exist as a standard Spark function for windowing. C - Using just 'event_time' would group by exact timestamps instead of intervals. D - This uses 10 minutes instead of the required 5 minutes. E - The 'lag' function is for accessing previous row values, not for creating time windows.
non-overlapping 5 minute interval --> so use window("event_time", "5 minutes")
Suretly B