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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #21

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…

The correct answer is C. A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed. 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…

Submitted by ricky.ec· Apr 18, 2026ML model development

Question

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?

Options

  • AAVM on Compute Engine and 1 TPU with all dependencies installed manually.
  • BAVM on Compute Engine and 8 GPUs with all dependencies installed manually.
  • CA Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.
  • DA Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-

How the community answered

(38 responses)
  • A
    8% (3)
  • B
    3% (1)
  • C
    74% (28)
  • D
    16% (6)

Explanation

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

Topics

#GPU acceleration#Deep Learning VMs#Model training optimization#Google Cloud infrastructure

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