Google Professional Cloud Architect Exam - 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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Pa

3 months ago

D is solid too, but I think B has the edge with local processing.

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Jessenia

3 months ago

C sounds cool, but is it really necessary to use Google Cloud for this?

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Herman

4 months ago

Wait, can we really trust ML models to make these adjustments?

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Casie

4 months ago

I agree, machine learning is the way to go!

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Kimberely

4 months ago

Option B seems the most efficient for real-time adjustments.

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Kandis

4 months ago

I recall a practice question that emphasized local processing, so option B aligns with that, but I'm unsure about the specifics of the implementation.

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Ressie

4 months ago

I feel like option D could be beneficial since it leverages Google Cloud's capabilities, but I wonder if it would be too complex to implement.

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Malcolm

5 months ago

I'm not entirely sure, but I think option A might be too simplistic compared to the machine learning approaches we studied.

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Kimberlie

5 months ago

I remember we discussed the importance of real-time data analysis in class, so option B seems like a strong choice for efficiency.

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Youlanda

5 months ago

The Google Cloud Dataflow and GCM option in Option C is intriguing, but I'm not as familiar with those services. I'd want to make sure that approach would scale to 20 million vehicles before committing to it.

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Gabriele

5 months ago

Capturing all the operating data and using machine learning to identify the ideal operations sounds like a solid strategy. I like that Options B and D both mention this approach. I'd need to think through the implementation details, but those seem promising.

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Gail

5 months ago

I'm a bit confused by all the cloud service options presented. I'm not sure which one would be the most appropriate for this use case. Maybe I should research the capabilities of each one more before deciding.

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Alonso

5 months ago

This seems like a straightforward optimization problem. I'd start by analyzing the data patterns and creating an algorithm to make the adjustments automatically. Option A looks like the best approach.

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Youlanda

5 months ago

Okay, let's see. I'm pretty confident about options C and F, but I'm not sure about the others. I'll need to double-check the details.

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Olive

5 months ago

I remember practicing a question like this, and I think PQ might be one of the answers, but it doesn't seem quite right.

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Vallie

5 months ago

The Selector Content Store sounds like it might be the right answer, but I'm not 100% sure. I'll review the content store details to see if that matches the description in the question.

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Catrice

5 months ago

I recall a question about governance models and how they rely on consensus, which sounds like it would also involve the users.

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Evan

5 months ago

I'm a bit confused on this one. I know the Triple-A security principles, but I'm not sure how time-based access restrictions fit into them. I'll have to review my notes and see if I can figure out the right answer.

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Roslyn

9 months 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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Rolland

8 months ago

Yeah, they seem to know what they're doing. It's all about efficiency.

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Shawna

8 months ago

I agree, they probably have a good handle on the data and can create a solid algorithm.

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Sabina

8 months ago

Option A sounds like the way to go. Let the engineers handle it.

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Ling

10 months 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

10 months 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

10 months 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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Rebbecca

8 months ago

But training machine learning models locally in Option B sounds like a solid choice.

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Annamae

8 months ago

Having engineers inspect data for patterns in Option A could also be effective.

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Theodora

9 months ago

I agree, relying on a streaming job and messaging service could be risky.

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Lynda

9 months ago

Option B does seem more reliable with local ML models.

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Justine

10 months 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

10 months 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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