Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 50 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 50
Topic #: 1

[All Professional Machine Learning Engineer Questions]

Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?

A. AVM on Compute Engine and 1 TPU with all dependencies installed manually.
B. AVM on Compute Engine and 8 GPUs with all dependencies installed manually.
C. A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.
D. A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-installed.

Show Suggested Answer

Suggested Answer: C 🗳️

by inder0007 at July 6, 2021, 6:43 a.m.

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celia20200410

Highly Voted 3 years, 11 months ago

ANS: C to support CNN, you should use GPU. for preliminary experiment, pre-installed pkgs/libs are good choice. https://cloud.google.com/deep-learning-vm/docs/cli#creating_an_instance_with_one_or_more_gpus https://cloud.google.com/deep-learning-vm/docs/introduction#pre-installed_packages

upvoted 17 times

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Paul_Dirac

Highly Voted 3 years, 10 months ago

Code without manual device placement => default to CPU if TPU is present or to the lowest order GPU if multiple GPUs are present. => Not A, B. D: already using CPU and needing GPU for CNN. Ans: C

upvoted 15 times

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RyanTan

Most Recent 3 months, 1 week ago

Selected Answer: A

C is wrong because n1‐standard‐2 is too small for GPUs.

upvoted 1 times

Begum

1 month, 2 weeks ago

The question says "you want to experiment with google VMs.." Start with base configs.. N1 family ( Gen purpose VMs are more flexible to attach GPU)

upvoted 1 times

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IrribarraC

3 months, 3 weeks ago

Selected Answer: C

swapping CPU for GPU will speed up the training of a CNN a lot. Using preinstalled librearies is incurring in less risks which means speeding up time-to-market

upvoted 1 times

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Pau1234

6 months, 3 weeks ago

Selected Answer: A

Option A is better because it is better to go with 1 TPU than 8 GPUs, especially when you don't have any manual placements.

upvoted 2 times

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PhilipKoku

1 year ago

Selected Answer: C

C) GPU and all pre-installed libraries.

upvoted 1 times

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gscharly

1 year, 2 months ago

Selected Answer: C

Agree with celia20200410 - C

upvoted 1 times

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Sum_Sum

1 year, 7 months ago

Selected Answer: C

Agree with celia20200410 - C

upvoted 2 times

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Mickey321

1 year, 7 months ago

Selected Answer: D

keyword: Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction.

upvoted 1 times

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Liting

1 year, 11 months ago

Selected Answer: C

Should use the deep learning VM with GPU. TPU should be selected only if necessary, coz it incurs high cost. GPU in this case is enough.

upvoted 1 times

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M25

2 years, 1 month ago

Selected Answer: C

Went with C

upvoted 1 times

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Melampos

2 years, 2 months ago

Selected Answer: A

thinking in fastest way

upvoted 1 times

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SergioRubiano

2 years, 2 months ago

Selected Answer: C

You should use GPU.

upvoted 1 times

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BenMS

2 years, 3 months ago

Selected Answer: D

Critical sentence: Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. So only answer we have. it's D.

upvoted 3 times

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shankalman717

2 years, 3 months ago

Critical sentece: Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. So only answer we have. it's D.

upvoted 3 times