Google Professional Machine Learning Engineer Question 151
Single answerGoogle Cloud PlatformYou are building a machine learning workflow for your company, and you need to provide data scientists with a collaborative environment to train models. The environment should support distributed processing for large datasets, enable easy integration with other Google Cloud services, and allow fine-grained resource control. Which option would best meet your requirements?
- A
Google Cloud Workbench
- B
Colab Enterprise
- C
Notebooks on Dataproc
- D
Google Sheets with App Scripts
Show answer and explanation
Correct answer: C
Explanation
Notebooks on Dataproc is the best choice for a machine learning workflow that requires distributed processing of large datasets, collaboration, and integration with Google Cloud services. Dataproc's ability to manage clusters for distributed computing makes it a powerful option for such use cases, and its notebook environment further facilitates collaboration.
- A. Incorrect.
Google Cloud Workbench is used for managing ML workflows and pipelines, but it is not specifically designed for large-scale distributed processing or collaborative notebook environments.
- B. Incorrect.
Colab Enterprise supports collaboration and integration with Google Cloud services, but it is not optimized for distributed processing of large datasets.
- C. Correct.
Notebooks on Dataproc are ideal for distributed processing of large datasets. They allow fine-grained resource control, integrate with other Google Cloud services, and support collaborative environments.
- D. Incorrect.
Google Sheets with App Scripts is not suitable for machine learning workflows requiring distributed processing or large-scale datasets.