You have an Azure stream analytics environment. You are working on tuning the stream analytics query to increase throughput for streaming analytics jobs. You need to design the solution following the guide to scale your job to growing loads and utilize maximum resources. Your queries are inherently fully parallelizable across input partitions. What will be your first step in this design process?

Answer options:

A.Use PARTITION BY keyword for authoring your query to be embarrassingly parallel.
B.Initially , to avoid partitioning complexity, start with a query with not PARTITION BY.
C.Do not use PARTITION BY.
D.Do not Partition.

Correct Answer: A
There is a main point we should notice first. The queries are inherently fully parallelizable across input partitions. Based on this, we have to decide on the partition. So, in this case, we need to use PARTITION BY keyword for authoring your query to be embarrassingly parallel first.
Option A is correct: It`s the right solution.
Option B is incorrect: This works if your query is not embarrassingly parallel.
Option C is incorrect: This works if you are running lots of independent queries in a job.
Option D is incorrect: It is completely removing the partitioning which is not correct.
Reference: 
To know more, please refer to the docs below: 

https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-scale-jobs