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You have an embarrassingly parallel or distributed batch job on a large amount of data...

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You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?

A.

Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required

B.

Create the job in Data Science Jobs and then start the number of simultaneous job runs required for your workload

C.

Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel workloads

D.

Create a new job for every job run that you have to run in parallel, because the Data Science Jobs service can have only one job run per job

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