Google Professional Machine Learning Engineer Question 190
Single answerGoogle Cloud PlatformYou are leading the development of a machine learning model for your organization. The model needs to be developed collaboratively by a team of data scientists and machine learning engineers. The team will require access to Jupyter notebooks for prototyping, scalable compute resources for training, and integration with other managed Google Cloud services such as BigQuery. Which Google Cloud environment should you choose for this development?
- A
AI Platform Training
- B
Vertex AI Workbench
- C
Google Cloud Shell
- D
Compute Engine
Show answer and explanation
Correct answer: B
Explanation
Vertex AI Workbench is the most appropriate environment for this scenario because it is specifically designed for machine learning development. It provides managed Jupyter notebook environments for prototyping, integrates with Google Cloud services such as BigQuery, and supports scalable compute resources for training. This makes it ideal for collaborative work by a team of data scientists and engineers.
- A. Incorrect.
AI Platform Training is primarily used for distributed training and machine learning model deployment, not for collaborative development or prototyping with Jupyter notebooks.
- B. Correct.
Vertex AI Workbench provides managed Jupyter notebook environments, seamless integration with Google Cloud services (like BigQuery), and scalable compute resources, making it ideal for collaborative model development.
- C. Incorrect.
Google Cloud Shell is a lightweight environment for managing Google Cloud resources but is not designed for machine learning development or Jupyter notebook use.
- D. Incorrect.
Compute Engine provides virtual machines for general-purpose compute workloads but does not offer the managed Jupyter notebook environment or integrations specific to machine learning workflows.