nerdexam
Google

PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #182

You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of…

The correct answer is D. Configure a n1-standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use. To both accelerate TensorFlow model training and maintain ease of debugging, configure a VM with powerful NVIDIA GPUs, allowing direct SSH access for development.

Submitted by weili_xi· Apr 18, 2026ML model development

Question

You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?

Options

  • AConfigure a v3-8 TPU VM. SSH into the VM to train and debug the model.
  • BConfigure a v3-8 TPU node. Use Cloud Shell to SSH into the Host VM to train and debug the
  • CConfigure a n1 -standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use
  • DConfigure a n1-standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use

How the community answered

(28 responses)
  • A
    7% (2)
  • B
    4% (1)
  • C
    11% (3)
  • D
    79% (22)

Why each option

To both accelerate TensorFlow model training and maintain ease of debugging, configure a VM with powerful NVIDIA GPUs, allowing direct SSH access for development.

AConfigure a v3-8 TPU VM. SSH into the VM to train and debug the model.

While TPU VMs offer high performance for TensorFlow, debugging on TPUs can sometimes be more complex due to their specialized architecture and distributed nature, potentially reducing the ease of debugging compared to GPUs.

BConfigure a v3-8 TPU node. Use Cloud Shell to SSH into the Host VM to train and debug the

A TPU node, often referring to older TPU architectures, might offer less integrated debugging capabilities than a TPU VM, and Cloud Shell may not be the optimal environment for complex, interactive debugging sessions.

CConfigure a n1 -standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use

This option is identical to D in description, assuming D refers to the complete choice including SSH access for training and debugging on a GPU VM. If only one answer is correct, D is the designated choice.

DConfigure a n1-standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and useCorrect

Configuring a VM with NVIDIA GPUs (like P100s) provides significant acceleration for TensorFlow model training, reducing training time. SSH access to such a VM offers a familiar Linux environment where developers can use standard debugging tools directly within the GPU-accelerated setup, maintaining ease of debugging compared to specialized hardware like TPUs.

Concept tested: TensorFlow training, GPU vs. TPU, debugging environments

Source: https://cloud.google.com/vertex-ai/docs/training/create-custom-job-gpu-tpu

Topics

#ML Infrastructure#TensorFlow#GPU Acceleration#Model Debugging

Community Discussion

No community discussion yet for this question.

Sign up to join the discussion
Full PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice