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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.
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Suggested Answer: C 🗳️

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celia20200410
Highly Voted 3 years, 9 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 16 times
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Paul_Dirac
Highly Voted 3 years, 8 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 13 times
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RyanTan
Most Recent 1 month, 1 week ago
Selected Answer: A
C is wrong because n1‐standard‐2 is too small for GPUs.
upvoted 1 times
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IrribarraC
1 month, 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
4 months, 2 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
10 months, 2 weeks ago
Selected Answer: C
C) GPU and all pre-installed libraries.
upvoted 1 times
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gscharly
12 months ago
Selected Answer: C
Agree with celia20200410 - C
upvoted 1 times
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Sum_Sum
1 year, 5 months ago
Selected Answer: C
Agree with celia20200410 - C
upvoted 2 times
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Mickey321
1 year, 5 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, 9 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
1 year, 11 months ago
Selected Answer: C
Went with C
upvoted 1 times
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Melampos
1 year, 12 months ago
Selected Answer: A
thinking in fastest way
upvoted 1 times
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SergioRubiano
2 years ago
Selected Answer: C
You should use GPU.
upvoted 1 times
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BenMS
2 years, 1 month 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, 1 month 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
tavva_prudhvi
1 year, 9 months ago
Option D provides a more powerful CPU but does not include a GPU, which may not be optimal for deep learning training.
upvoted 2 times
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ares81
2 years, 3 months ago
Selected Answer: C
It's C.
upvoted 1 times
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suresh_vn
2 years, 7 months ago
"has not been wrapped in Estimator model-level abstraction" How you can use GPU? D in my opinion, E-family using for high CPU tasks
upvoted 3 times
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C (25%)
B (20%)
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