Microsoft Fabric Data Engineer Associate DP-700 Practice Question
A manufacturing company ingests 5 000 sensor readings per second into Microsoft Fabric. The engineering team must calculate 10-second tumbling-window averages for each production line and expose the results to Power BI analysts with less than 2 seconds of end-to-end latency, while avoiding custom code and cluster management. Which Fabric component should you implement to meet these requirements?
Use a Spark Structured Streaming notebook to write the windowed aggregates into a Delta table.
Build a Dataflow Gen2 to calculate the averages and configure it for continuous refresh.
Configure an eventstream with a tumbling-window aggregate transform and output the results to a Real-Time Analytics table that Power BI reads in DirectQuery mode.
Create a KQL materialized view in a Real-Time Analytics database that computes the 10-second windowed averages.
An eventstream can apply a built-in tumbling-window aggregate transform to the incoming data, producing 10-second averages in near real time. The transformed stream can be written to a Real-Time Analytics (Kusto) table or a push dataset that Power BI queries by using DirectQuery or the Real-Time data hub, without additional infrastructure or custom code. KQL materialized views currently do not support windowing functions; Spark Structured Streaming notebooks require code and cluster management; and Dataflow Gen2 is optimized for batch workloads and cannot meet the sub-second latency target.
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Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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