Which windowing function should you use for each requirement?

HOTSPOT
You are implementing Azure Stream Analytics functions.
Which windowing function should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

Correct Answer:

Explanation/Reference:
Box 1: Tumbling
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.

Box 2: Hoppping
Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap, so events can belong to more than one Hopping window result set. To make a Hopping window the same as a Tumbling window, specify the hop size to be the same as the window size.

Box 3: Sliding
Sliding window functions, unlike Tumbling or Hopping windows, produce an output only when an event occurs. Every window will have at least one event and the window continuously moves forward by an (epsilon). Like hopping windows, events can belong to more than one sliding window.

References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions