Google Professional Machine Learning Engineer Question 153
Single answerGoogle Cloud PlatformYou are a Machine Learning Engineer working for a retail company. Your team needs to prototype a recommendation model using a collaborative environment that supports Jupyter notebooks and provides enterprise-grade security while integrating seamlessly with Google Cloud services. Scalability for large datasets and the ability to execute distributed processing are key requirements. Which environment would best meet your needs?
- A
Google Cloud Workbench
- B
Colab Enterprise
- C
Notebooks on Dataproc
- D
Vertex AI Prediction
Show answer and explanation
Correct answer: C
Explanation
Notebooks on Dataproc are the ideal choice for this scenario because they combine the collaborative features of Jupyter notebooks with the scalability and distributed processing capabilities of Dataproc. This makes them well-suited for handling large datasets and integrating with other Google Cloud services, fulfilling the requirements of the team.
- A. Incorrect.
Google Cloud Workbench is a versatile tool for managing and orchestrating machine learning workflows, but it does not provide the distributed processing capabilities or scalability needed for large datasets.
- B. Incorrect.
Colab Enterprise is a collaborative environment with enterprise-grade security but is not optimized for distributed processing of large datasets. It is more suitable for prototyping and smaller-scale tasks.
- C. Correct.
Notebooks on Dataproc provide a collaborative Jupyter notebook environment integrated with Google Cloud services, and they are specifically designed for distributed processing and scalability by utilizing Apache Spark and Hadoop.
- D. Incorrect.
Vertex AI Prediction is used for deploying machine learning models for online or batch predictions. It is not designed for prototyping or collaborative notebook-based development.