A famous online taxi service is setting up its ride searching application with the help of Azure Stream analytics. There have few requirements as follows. A driver should be able to see nearby passengers who are looking for rides in the application.
Data about no of cars in each circle is sent to the Azure event hub every 2 minutes for further analysis.
Solution must be cost-effective.
Which of the following tells about the right services to be used here?

Answer options:

A.Use tumbling window and Geospatial aggregation.
B.Use tumbling window and Geofencing.
C.Sliding windows and Geospatial aggregation.
D.Sliding windows and Geofencing.

Correct Answer: A
The first requirement says that the nearby potential customers should be identified. This is geospatial aggregation, and Azure Stream analytics supports low-latency real-time geospatial aggregation. Again there is windowing which tells about sending data which is collected in 2 mins timeframes. So, function taken against a timeframe is a perfect scenario of tumbling window, making it more cost-effective than other windowing options.
Option A is correct: Since Geospatial aggregation enables drivers to find the nearby customers, and tumbling window can send the data in fixed timeframes.
Options B, C and D are incorrect:Geofencing is kind of controlling the location within a specific perimeter, and we can set up alerts when the GPS device leaves this perimeter. Sliding window output events only for points in time when the content of the window actually changes.
Reference: 
To know more, please refer to the docs below: 

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