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Answer: Use the Databricks workspace's Library management feature to install the library at the cluster scope.
Option B is the correct approach as it ensures that the library is installed at the cluster scope, making it available to all notebooks running on that cluster. This is more efficient and scalable than installing the library manually in each notebook or local environment.
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Your team is working on a machine learning project and needs to use a specific Python library that is not available in the default Databricks Runtime for Machine Learning. You have been asked to install this library so that it can be used across all notebooks in the cluster. What steps should you take to achieve this?
A
Install the library using pip in the Databricks notebook and hope it works for all notebooks.
B
Use the Databricks workspace's Library management feature to install the library at the cluster scope.
C
Instruct each team member to install the library in their local development environment.
D
Add the library to the Databricks Runtime for Machine Learning's base image and rebuild the runtime.
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