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For this question, refer to the TerramEarth case study.

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For this question, refer to the TerramEarth case study.

TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US. Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?

A.

Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.

B.

Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job.

C.

Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multi region bucket and use a Dataproc cluster to finish the job.

D.

Launch a cluster in each region to preprocess and compress the raw data, then move the data into a region bucket and use a Cloud Dataproc cluster to finish the jo

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For this question, refer to the TerramEarth case study.
For this question, refer to the TerramEarth case study.
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