You operate an IoT solution for a plant manufacturing ceramic filter inserts for the automotive industry. The critical part of the technology where the products are being baked in high-temperature ovens. A large number of temperature data is collected from several ovens by sensors. It is important when the average temperature exceeds a certain threshold, an alarm has to be triggered and a reset command to the alert output if the average machine temperature in a 30-second window reaches 200 degrees. 
<pre class="brush:java;">SELECT
`reset` AS command
INTO
 alert
FROM
 temperature TIMESTAMP BY timeCreated
GROUP BY TumblingWindow(second,30)
HAVING Avg(machine.temperature) &gt; 200
</pre>
Will this code fit your purpose?

Answer options:

A.Yes
B.No

Correct Answer: A
Option A is CORRECT because tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. It is the best option for cases when data coming at regular intervals must be aggregated at fixed length periods.
Option B is incorrect because tumbling windows are the right selection for the current scenario.
References: 

https://docs.microsoft.com/en-us/azure/iot-edge/tutorial-deploy-stream-analytics?toc=https%3A%2F%2Fdocs.microsoft.com%2Fen-us%2Fazure%2Fstream-analytics%2Ftoc.json&bc=https%3A%2F%2Fdocs.microsoft.com%2Fen-us%2Fazure%2Fbread%2Ftoc.json&view=iotedge-2018-06#configure-your-job
https://docs.microsoft.com/en-us/stream-analytics-query/windowing-azure-stream-analytics