Discuss Google Professional Cloud Architect (PR000213) Exam Topic 1 Question 123

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?

AHave your engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically.

BCapture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically.

CImplement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically.

DCapture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically.

Suggested Answer: B

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19 comments · newest first

Donte 13 days ago

Yes, let’s focus on real-time adjustments!

Dortha 18 days ago

Overall, B seems to balance efficiency and safety well.

Alesia 23 days ago

True, but I prefer keeping data close to the vehicles.

Jerry 28 days ago

Still, GCM in option C could streamline communication.

Donte 1 month ago

Exactly! Plus, less latency with local adjustments.

Dortha 1 month ago

Good point! Local models might be safer.

Alesia 1 month ago

But what about data privacy with option D?

Jerry 2 months ago

Option D sounds solid too. Cloud hosting has its advantages.

Donte 2 months ago

I agree! Machine learning can optimize efficiency effectively.

Dortha 2 months ago

I think option B is the best. Local adjustments are crucial.

Ilene 2 months ago

C seems complicated, but could be effective if set up right.

Melvin 2 months ago

Not sure about D, hosting in the cloud could introduce delays.

Glynda 2 months ago

Surprised that no one mentioned real-time adjustments!

Catrice 3 months ago

I think A is too manual, we need automation.

Cheryl 3 months ago

Option B sounds solid, machine learning is the way to go!

Cordie 3 months ago

Option D seems appealing since it combines machine learning with cloud hosting, but I’m not entirely clear on how that would work in real-time adjustments.

Kiera 3 months ago

I feel like option C is a bit technical for what we need, but using Google Cloud Dataflow sounds familiar from our cloud computing module.

Iluminada 3 months ago

I think option B makes sense because machine learning can adapt to different conditions, but I wonder if training those models would take too long for 20 million vehicles.

Arthur 4 months ago

I remember discussing the importance of data patterns in class, so option A seems like a solid start, but I'm not sure if it's the most efficient method for all vehicles.

Google Professional Cloud Architect (PR000213) Exam - Topic 1 Question 123 Discussion