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. You have two solution candidates: 
# OPTION2 
<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>
# OPTION1 
<pre class="brush:java;">SELECT
`reset` AS command
INTO
 alert
FROM
 temperature TIMESTAMP BY timeCreated
GROUP BY HoppingWindow(second,30,30)
HAVING Avg(machine.temperature) &gt; 200
</pre>
Which one of the above code should you use?

Answer options:

A.Only OPTION1
B.Only OPTION2
C.Either OPTION1 or OPTION2
D.Neither OPTION1 nor OPTION2

Correct Answer: C
Option A is incorrect because the two codes produce exactly the same result. OPTION2 is also correct.
Option B is incorrect because the two codes produce exactly the same result. OPTION1 is also correct.
Option C 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. On the other hand, a tumbling window is a special hopping window, with its ‘hop’ size being equal to its ‘size’.
Option D is incorrect because both code segments are correct and implement windowing functions which deliver the required result.
References: 

https://docs.microsoft.com/en-us/stream-analytics-query/windows-azure-stream-analytics
https://docs.microsoft.com/en-us/stream-analytics-query/hopping-window-azure-stream-analytics#time-consideration