Google Professional Machine Learning Engineer Question 147
Single answerGoogle Cloud PlatformYou are a machine learning engineer setting up a Jupyter Notebook environment for your team to develop and experiment with machine learning models on Google Cloud. The team requires a scalable environment with access to GPUs for training large models, built-in integration with Google Cloud services like BigQuery, and seamless deployment to Vertex AI. Which Jupyter backend should you choose?
- A
Vertex AI Workbench (Managed Notebooks)
- B
Dataproc JupyterLab
- C
Local Jupyter installation on a Compute Engine VM
- D
AI Platform Notebooks
Show answer and explanation
Correct answer: A
Explanation
Vertex AI Workbench (Managed Notebooks) is the most suitable choice for this scenario as it offers a fully managed, scalable environment with GPU support, integration with Google Cloud services like BigQuery, and seamless deployment to Vertex AI. These features align with the team's requirements for developing and deploying machine learning models efficiently.
- A. Correct.
Vertex AI Workbench (Managed Notebooks) provides a scalable, managed environment with built-in GPU support, integration with Google Cloud services like BigQuery, and deployment capabilities to Vertex AI.
- B. Incorrect.
Dataproc JupyterLab is primarily designed for distributed data processing workflows using Hadoop or Spark, not for scalable machine learning model development and deployment.
- C. Incorrect.
A local Jupyter installation on a Compute Engine VM requires significant manual setup and management. It lacks the seamless integration and scalability provided by Vertex AI Workbench.
- D. Incorrect.
AI Platform Notebooks is an older service and has been consolidated into Vertex AI Workbench. It is not the recommended choice for new projects.