Answer: hopping

## Window Type Selection Analysis For this Azure Stream Analytics scenario, the requirement is to calculate a **running average of shopper counts during the previous 15 minutes, calculated at five-minute intervals**. ### Why Hopping Window (Option C) is Correct: - **Hopping windows** are specifically designed for scenarios where you need to compute aggregates at regular intervals over a fixed time range - The key parameters match perfectly: - **Window size**: 15 minutes (the time range for the running average) - **Hop size**: 5 minutes (the calculation interval) - This creates overlapping windows where each calculation includes data from the previous 15 minutes, but results are emitted every 5 minutes - The hopping window function in Azure Stream Analytics would be implemented as `HOPPINGWINDOW(minute, 15, 5)` ### Why Other Options Are Incorrect: - **Snapshot (A)**: Captures events at specific points in time but doesn't support the required 15-minute running average calculation - **Tumbling (B)**: Creates non-overlapping, contiguous time windows of fixed size. A 15-minute tumbling window would only output results every 15 minutes, not every 5 minutes as required - **Sliding (D)**: While sliding windows can produce overlapping results, they output events continuously as events occur rather than at fixed intervals. The requirement specifies "calculated at five-minute intervals," which aligns with hopping windows' fixed interval behavior ### Best Practice Justification: Hopping windows are the optimal choice when you need periodic reporting of aggregates over a fixed historical period. The 15-minute window with 5-minute hops ensures the running average is updated regularly while maintaining the required historical context, making it ideal for monitoring retail shopper patterns.

Author: LeetQuiz Editorial Team