Google Cloud Professional Machine Learning Engineer — Question 168

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?

Answer options

• A. Configure a v3-8 TPU VM. SSH into the VM to train and debug the model.
• B. Configure a v3-8 TPU node. Use Cloud Shell to SSH into the Host VM to train and debug the model.
• C. Configure a n1 -standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use ParameterServerStraregv to train the model.
• D. Configure a n1-standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use MultiWorkerMirroredStrategy to train the model.

Correct answer: D

Explanation

Option D is the correct choice as MultiWorkerMirroredStrategy is designed to improve training speed and efficiency across multiple GPUs while maintaining ease of debugging. Options A and B utilize TPUs which may not support the same level of debugging ease as a VM with NVIDIA GPUs. Option C, while it mentions using ParameterServerStrategy, does not leverage the benefits of MultiWorkerMirroredStrategy, making it less optimal for reducing training time.