Exam Professional Machine Learning Engineer topic 1 question 311 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 311
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You lead a data science team that is working on a computationally intensive project involving running several experiments. Your team is geographically distributed and requires a platform that provides the most effective real-time collaboration and rapid experimentation. You plan to add GPUs to speed up your experimentation cycle, and you want to avoid having to manually set up the infrastructure. You want to use the Google-recommended approach. What should you do?

A. Configure a managed Dataproc cluster for large-scale data processing. Configure individual Jupyter notebooks on VMs that each team member uses for experimentation and model development.
B. Use Colab Enterprise with Cloud Storage for data management. Use a Git repository for version control.
C. Use Vertex AI Workbench and Cloud Storage for data management. Use a Git repository for version control.
D. Configure a distributed JupyterLab instance that each team member can access on a Compute Engine VM. Use a shared code repository for version control.

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Suggested Answer: C 🗳️

by f4ccd7a at April 30, 2025, 4:37 a.m.

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hit_cloudie

4 weeks, 1 day ago

Selected Answer: C

C Vertex AI Workbench provides: Pre-configured JupyterLab Native GPU support Seamless integration with Git and GCS No manual infra setup Google-recommended for collaborative ML workflows

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f4ccd7a

1 month, 2 weeks ago

Selected Answer: C

Co-lab is better for real-time collaboration

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