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Question 4/70

For this question refer to the TerramEarth case study
Operational parameters such as oil pressure are adjustable on each of TerramEarth's vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field How can you accomplish this goal?

Recent Comments (The most recent comments are at the top.)

Ozmobike - Sep 21, 2018

Exactly, best answer will be D, as we&#39;re dealing with unconnected vehicles..

Ross - Sep 12, 2018

it requires to &quot;increase the operating efficiency of all 20 million cellular and unconnected vehicles&quot;, for unconnected vehicles, it is not possible to use GCP ML, thus I choose D

Bro - Sep 11, 2018

I agree that answer D is correct (Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically).

Explanation
Solution requires machine learning so it will be either answer C or D.
But the case study points that TerramEarth have over 500 dealers and service centers in 100 countries. The documentation for ML platform states that it is available only in few regions of the world, hence it will not work for hundred of countries.
https://cloud.google.com/ml-engine/docs/tensorflow/regions

Andrew - Sep 02, 2018

Why not C? Wouldn&#39;t hosting in ML Platform be better option than running locally?

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