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Answer: Use Vertex AI Workbench and Cloud Storage for data management. Use a Git repository for version control.
The question emphasizes real-time collaboration, rapid experimentation, GPU acceleration, and avoiding manual infrastructure setup. While both Colab Enterprise (Option B) and Vertex AI Workbench (Option C) support these requirements, Vertex AI Workbench is Google's recommended managed notebook solution for collaborative ML workflows. It provides pre-configured JupyterLab with native GPU support, seamless Git and Cloud Storage integration, and requires no manual infrastructure setup. Although some community comments suggest Colab for real-time collaboration, Vertex AI Workbench is specifically designed for enterprise ML teams and is Google's recommended approach for this scenario. Option A (Dataproc + individual notebooks) lacks real-time collaboration and requires manual setup. Option D (distributed JupyterLab on VM) requires manual infrastructure management.
Author: LeetQuiz Editorial Team
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Your geographically distributed data science team is working on a computationally intensive project that requires running multiple experiments. You need a platform that enables effective real-time collaboration and rapid experimentation. To accelerate the experimentation cycle, you plan to add GPUs and want to avoid manual infrastructure setup. What is the Google-recommended approach to achieve this?
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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