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Exam Professional Cloud Architect topic 8 question 11 discussion

Actual exam question from Google's Professional Cloud Architect
Question #: 11
Topic #: 8
[All Professional Cloud Architect Questions]

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?

  • A. Have you engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically
  • B. Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically
  • C. Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically
  • D. Capture 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 Hide Answer
Suggested Answer: B 🗳️
by JoeShmoe at Nov. 15, 2019, 10:31 a.m.

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JoeShmoe
Highly Voted 4 years, 5 months ago
B is correct. only 200k vehicle's are connected so need to run updates locally
upvoted 35 times
exampanic
4 years, 3 months ago
In my view, option B says "run locally" referring to the machine learning models. "Machine learning models" is the subject of the sentence. Nowhere in the sentence says run "updates" locally. So running machine learning models would only make sense in Google's ML platform, not locally. Because of this reason, I believe the correct answer should be "D".
upvoted 32 times
tartar
3 years, 8 months ago
B is ok
upvoted 13 times
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cetanx
3 years, 9 months ago
Both B and D starts with "Capture all operating data, train machine learning models that identify ideal operations, ..." so they are offering the same method for training the data. The keypoint here is "make operational adjustments" such as adjusting the oil pressure so if we host in GCP-ML, how are we going to instruct vehicles on field to adjust their oil pressure if they have no internet connection? There is no way to use GCP-ML model generated parameters to command the "not connected" field vehicles to make operational adjustments automatically. Therefore, I believe running it locally on the servers sitting in the vehicles is the only option. My answer: B
upvoted 28 times
Vika
3 years, 1 month ago
Making operational adjustments is an operational problem after recommendations are made by ML. In my mind, new data will keep feeding and total operational data changes every day for model and which would impact model performance over time. Monitoring model performance to achieve required efficiency levels would need some sort of centralization of efforts, as every machine environment condition might be different and there might be a need to create multiple models and test and operate them. (one shoe doesn't fit all).
upvoted 2 times
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techalik
3 years, 4 months ago
What you think about D? there is a hint there :D and host in "Google Cloud Machine Learning" (ML)
upvoted 4 times
nandoD
1 year ago
how will the automatic opeartional adjustments be done to the unconnected/offline vehicles?
upvoted 3 times
kapa900
9 months, 4 weeks ago
end of day
upvoted 2 times
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nitinz
3 years, 1 month ago
B is correct.
upvoted 2 times
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nick_name_1
1 year, 1 month ago
B says "Capture all operating data". This is not right. You don't need ALL operating data to create an efficiency algorithm. The Answer is A.
upvoted 1 times
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dabrat
Highly Voted 4 years, 5 months ago
B)=> unconnected vehicles in the field.
upvoted 8 times
Rafaa
3 years, 10 months ago
Unconnected vehicles does not mean their data is not on GCP. you would still do ML on GCP and can use that to improve operational performance via maintenance port.
upvoted 4 times
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didek1986
Most Recent 2 months, 4 weeks ago
Selected Answer: D
It is D
upvoted 1 times
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e5019c6
3 months, 3 weeks ago
Selected Answer: D
I don't understand why everyone votes for B. In the replies I see, it seems to me that people are expecting the ML model to run on the vehicles and make changes offline. For this to be true, the vehicles would need a powerful computer, at least if this model is anywhere close to the AI models around... So, in this case, the ML model would have to be uploaded to all 20 million vehicles for them to change this parameters offline? That seems kind of crazy, but maybe I'm lacking some kind of info? Maybe the ML models are very portable and don't require much processing power?
upvoted 2 times
parthkulkarni998
3 months, 3 weeks ago
Exactly. And considering the newly added data, the model would be updated, which would result in multiple versions of the model. Better alternative would be to centrally host the model and access it via API/offline via port
upvoted 1 times
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red_panda
9 months, 3 weeks ago
Selected Answer: B
Also for me it's B. Maybe it might not seem so straightforward when reading the answer, but personally I understood it as 'running the result of ML training on devices'. Understanding it this way it certainly can only be B as from the scenario description we have most of the machines not connected to a cellular network and therefore it is impossible to pass data to them.
upvoted 2 times
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Arlima
1 year, 2 months ago
B is correct. only 200k vehicle's are connected so need to run updates locally, means ML Edge
upvoted 3 times
dija123
1 week ago
Yes, Exactly it is ML Edge.
upvoted 1 times
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ale_brd_
1 year, 3 months ago
Selected Answer: B
Answer is B
upvoted 1 times
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megumin
1 year, 5 months ago
Selected Answer: B
ok for B
upvoted 1 times
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Nirca
1 year, 7 months ago
Selected Answer: B
B is correct. only 200k vehicle's are connected so need to run updates locally
upvoted 1 times
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AzureDP900
1 year, 9 months ago
B is right , Agreed with detailed discussions !
upvoted 1 times
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Danny2021
2 years, 4 months ago
B. Train model in the cloud and deploy model to the edge for local prediction. This is typical in IoT.
upvoted 7 times
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joe2211
2 years, 4 months ago
Selected Answer: B
vote B
upvoted 2 times
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Nik22
2 years, 7 months ago
from exam point of view, I doubt these questions would be part of new exam. Terram earth case study has changed.
upvoted 3 times
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VishalB
2 years, 8 months ago
Answer : B Google has announced two new products aimed at helping customers develop and deploy intelligent connected devices at scale: Edge TPU, a new hardware chip, and Cloud IoT Edge, a software stack that extends Google Cloud’s powerful AI capability to gateways and connected devices. This lets you build and train ML models in the cloud, then run those models on the Cloud IoT Edge device through the power of the Edge TPU hardware accelerator. - https://cloud.google.com/blog/products/gcp/bringing-intelligence-edge-cloud-iot
upvoted 3 times
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victory108
2 years, 9 months ago
B. Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically
upvoted 2 times
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capitaine
2 years, 9 months ago
Run Locally -> That's exactly what gcp ca, provide: https://cloud.google.com/blog/products/gcp/bringing-intelligence-edge-cloud-iot
upvoted 2 times
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MamthaSJ
2 years, 9 months ago
Answer is B
upvoted 3 times
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