Google Professional Machine Learning Engineer Question 189
Single answerGoogle Cloud PlatformYou are tasked with building a machine learning model for a retail company on Google Cloud. The development process involves frequent experimentation, collaboration between multiple data scientists, and the need for managed Jupyter notebooks with easy access to data stored in BigQuery. Which Google Cloud environment should you choose for this development process?
- A
Vertex AI Workbench
- B
Google Cloud Dataproc
- C
Google Kubernetes Engine (GKE)
- D
Cloud Shell
Show answer and explanation
Correct answer: A
Explanation
Vertex AI Workbench is specifically designed for machine learning development. It provides managed Jupyter notebooks, integrates with other Google Cloud services like BigQuery for seamless data access, and supports collaboration, making it the ideal choice for this scenario.
- A. Correct.
Vertex AI Workbench is the correct choice because it provides managed Jupyter notebooks, integrates with BigQuery, and is optimized for collaborative machine learning development and experimentation.
- B. Incorrect.
Google Cloud Dataproc is primarily used for big data processing with Hadoop and Spark. It is not designed for machine learning experimentation or managed notebook environments.
- C. Incorrect.
Google Kubernetes Engine (GKE) is a container orchestration platform. While it can be used to deploy machine learning models, it is not ideal for collaborative development or experimentation.
- D. Incorrect.
Cloud Shell is a lightweight command-line tool for managing Google Cloud resources. It does not support managed Jupyter notebooks or large-scale data science workflows.