Google Exam Professional Cloud Architect Topic 5 Question 77 Discussion

Actual exam question for Google's Professional Cloud Architect exam

Question #: 77
Topic #: 5

[All Professional Cloud Architect Questions]

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.

Show Suggested Answer

Suggested Answer: A

The Data Transfer appliance is a Google-provided hardware device that can be used to transfer large amounts of data from on-premises environments to Cloud Storage. It is suitable for scenarios where the bandwidth between the on-premises environment and Google Cloud is low or insufficient, and the data size is large. The Data Transfer appliance can minimize the time it takes to complete the migration, the overall cost and database load, by avoiding network bottlenecks and reducing bandwidth consumption. The Data Transfer appliance also encrypts the data at rest and in transit, ensuring data security and privacy. The other options are not optimal for this scenario, because they either require a high-bandwidth network connection (B, C, D), or incur additional costs and complexity (B, C). Reference:

https://cloud.google.com/data-transfer-appliance/docs/overview

https://cloud.google.com/blog/products/storage-data-transfer/introducing-storage-transfer-service-for-on-premises-data

by Van at Mar 10, 2024, 07:38 AM

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Roslyn

11 days ago

I bet the engineers at TerramEarth are already working on Option A. They're probably sitting around a table, drawing flow charts and muttering about 'if-then-else' statements. Old school, but gets the job done.

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Ling

12 days ago

Option B is the way to go - Machine learning is the future, and running it locally means you don't have to worry about network latency. Plus, no need to train the models in the cloud, that's just showing off.

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Caprice

14 days ago

As an engineer, Option A appeals to me - creating rules-based algorithms seems more straightforward than training ML models. Though the automated adjustments in the other options are tempting.

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Justine

16 days ago

I'm not sure I'd want to rely on a streaming job and messaging service in Option C. Sounds a bit complex and prone to network issues. The local ML in Option B feels more robust.

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Justine

28 days ago

Option D is interesting, leveraging the scalability and capabilities of the Google Cloud ML Platform. Centralized ML models could provide more sophisticated optimization than local adjustments.

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Lawanda

29 days ago

Option B seems the most comprehensive approach, using machine learning to optimize the vehicle operations based on real-world data. I like how it automates the adjustments locally without relying on a central server.

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