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Google Professional Data Engineer Exam - Topic 4 Question 30 Discussion

You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?
B) Use Cloud TPUs after implementing GPU kernel support for your customs ops.
A) Use Cloud TPUs without any additional adjustment to your code.
C) Use Cloud GPUs after implementing GPU kernel support for your customs ops.
D) Stay on CPUs, and increase the size of the cluster you're training your model on.

Google Professional Data Engineer Exam - Topic 4 Question 30 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 30
Topic #: 4
[All Professional Data Engineer Questions]

You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?

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Suggested Answer: B

by Junita at May 07, 2022, 04:15 PM

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Amie
10 months ago
D is a waste of resources, just upgrade to TPUs or GPUs instead!
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Lorean
10 months ago
Wait, can you really just switch to TPUs without any changes? Sounds risky!
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Erasmo
11 months ago
A seems too optimistic, you’ll need adjustments for TPUs.
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Temeka
11 months ago
I think C could be a good option too, but it might be slower.
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Sherell
11 months ago
Definitely go with B, TPUs need those custom ops to work well!
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Annabelle
11 months ago
Staying on CPUs seems like a bad idea, but I wonder if increasing the cluster size could help at all. It feels like a last resort option.
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Huey
11 months ago
I'm leaning towards using Cloud GPUs since they might be easier to integrate with custom ops, but I’m not completely confident about the performance compared to TPUs.
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Rebecka
11 months ago
I think we practiced a question where we had to implement GPU kernel support for custom ops before using TPUs. That might be necessary here too.
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Nichelle
11 months ago
I remember we discussed how TPUs can significantly speed up training, but I'm not sure if they can work with custom C++ ops without modifications.
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Stephen
11 months ago
I think the key here is that the R programmers are tasked with copying the data. That sounds like the Extract phase to me, where they'd use their R skills to pull the data from the source system.
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Chantay
11 months ago
Ah, this is a good one. I remember discussing subscriber keys and data deduplication in class. I'm feeling confident I can nail this.
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Jesusa
11 months ago
I recall a similar question where the focus was on application settings, and I'm pretty confident that it was about using the global policy.
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