Crucial Exams

GCP Professional Data Engineer Practice Question

A retail company is implementing a streaming Dataflow pipeline that aggregates purchase events into fixed 60-minute windows keyed by store ID. Analysts need rolling insights: interim counts every 5 minutes while the window is still open, and one final, complete result as soon as the event-time watermark passes the window end. Events that arrive more than 10 minutes late must be silently dropped. Which Beam trigger configuration and pane accumulation setting best satisfy these requirements with minimal custom logic?

  • Use AfterWatermark past end-of-window with early firings every 5 minutes based on processing time, set allowed lateness to 10 minutes, and enable accumulating fired panes.

  • Use AfterWatermark past end-of-window without early firings, add late firings up to 10 minutes, and keep discarding fired panes.

  • Use Repeatedly(AfterCount(1000)) with accumulating fired panes and unlimited allowed lateness.

  • Use Repeatedly(AfterProcessingTime.pastFirstElement(5 minutes)) with allowed lateness 10 minutes and discarding fired panes.

GCP Professional Data Engineer
Ingesting and processing the data
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