Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You are authoring a streaming KQL query in Real-Time Analytics on Microsoft Fabric. The requirement is to compute the average temperature for each device over 10-second windows, refreshing every 5 seconds so that each event can participate in several overlapping windows. Which KQL windowing function should you apply to meet the requirement?
In Kusto Query Language, overlapping (sliding) windows are produced with the hop() windowing function. hop() takes a timestamp column, a hop size (the period between window starts), and a window duration. In this scenario, hop(ts, 5s, 10s) would create 10-second windows that start every 5 seconds, letting each event appear in multiple windows.
The tumble() function generates non-overlapping, fixed-size windows, so it would not satisfy the requirement for overlapping windows. session_window() groups events into dynamic sessions based on periods of inactivity, not fixed time spans. "slidingwindow()" is not a valid KQL function, so it cannot be used.
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Microsoft Fabric Data Engineer Associate DP-700
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