Google Professional Machine Learning Engineer Question 149
Select 3Google Cloud PlatformYour team is building a machine learning model using Jupyter notebooks and wants to ensure that the development environment is scalable, collaborative, and integrates seamlessly with Google Cloud services. The team also needs the ability to process large datasets using distributed computing. Which environment(s) should you recommend?
- A
Google Cloud Workbench
- B
Colab Enterprise
- C
Jupyter notebooks on Dataproc
- D
On-premises Jupyter notebook setup
Show answer and explanation
Correct answers: A, B, C
Explanation
For scalable, collaborative, and cloud-native machine learning development, Google Cloud Workbench, Colab Enterprise, and Jupyter notebooks on Dataproc are well-suited options. Google Cloud Workbench and Colab Enterprise provide managed environments with strong integration into Google Cloud services, while Jupyter notebooks on Dataproc allow for distributed data processing using Apache Spark. An on-premises Jupyter setup does not meet the scalability or integration requirements.
- A. Correct.
Google Cloud Workbench provides a managed environment for Jupyter notebooks, offering seamless integration with Google Cloud services and scalability for machine learning workflows.
- B. Correct.
Colab Enterprise is a collaborative notebook environment that supports team-based development and integration with Google Cloud resources while maintaining enterprise-grade security.
- C. Correct.
Jupyter notebooks on Dataproc enable distributed processing of large datasets using managed Apache Spark clusters, making it suitable for large-scale machine learning tasks.
- D. Incorrect.
An on-premises Jupyter notebook setup lacks the scalability and seamless integration with Google Cloud services required for this scenario, making it a suboptimal choice.