Google Professional Machine Learning Engineer Question 152
Single answerGoogle Cloud PlatformA data science team at your organization is working on a machine learning project that requires collaborative development, scalability for large datasets, and integration with other Google Cloud services. They also want to ensure proper resource isolation due to sensitive data being processed. Which environment would best meet their requirements?
- A
Google Cloud Workbench
- B
Colab Enterprise
- C
Notebooks on Dataproc
- D
Local Jupyter Notebook on a VM
Show answer and explanation
Correct answer: C
Explanation
Notebooks on Dataproc are the best choice because they offer a robust environment for collaborative data science and machine learning development. They can handle large-scale datasets by leveraging the scalability of Dataproc clusters, integrate seamlessly with other Google Cloud services like BigQuery and Cloud Storage, and provide proper resource isolation for processing sensitive data. This makes them well-suited to meet the requirements outlined in the scenario.
- A. Incorrect.
Google Cloud Workbench provides an environment for managing ML pipelines and experimentation, but it is not specifically designed for handling large-scale collaborative notebook environments or sensitive data processing with proper resource isolation.
- B. Incorrect.
Colab Enterprise supports collaboration and integrates with Google Cloud services, but it is less suited for large-scale data processing and resource isolation compared to Dataproc Notebooks.
- C. Correct.
Notebooks on Dataproc are ideal for collaborative development, scalability with large datasets, and seamless integration with other Google Cloud services. They also allow for proper resource isolation by leveraging Dataproc clusters.
- D. Incorrect.
A local Jupyter Notebook on a VM does not provide the scalability, collaboration, or integration features required for this scenario. It is also not an ideal solution for handling sensitive data with proper resource isolation.