Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You are designing a Spark Structured Streaming query in Microsoft Fabric to calculate the total number of orders in fixed, non-overlapping 10-minute intervals as the data arrives. Which type of window should you apply to the event-time column to meet the requirement?
Hopping window
Sliding window with a 10-minute duration and a 5-minute slide
A tumbling window breaks the event-time axis into consecutive, equal-sized buckets that do not overlap, so every event belongs to one and only one window. This matches the requirement for fixed 10-minute intervals with no overlap. Hopping and sliding windows both create overlapping windows when the slide is shorter than the window length, while session windows create dynamically sized windows based on periods of activity separated by inactivity gaps, none of which satisfy the non-overlapping 10-minute interval requirement.
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What is a tumbling window in Spark Structured Streaming?
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What are session windows, and why are they not suitable for this requirement?
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Microsoft Fabric Data Engineer Associate DP-700
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