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Answer: Install the library using the Databricks Library management system at the cluster scope after creating the cluster.
Option C is the most efficient and scalable approach. It allows you to install the library at the cluster scope using the Databricks Library management system, ensuring that it is available to all notebooks without the need for manual installation in each one. This approach is preferable to using init scripts or custom runtimes, which may be more complex and less flexible.
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You are setting up a new Databricks environment for a machine learning project and need to ensure that the Databricks Runtime for Machine Learning is configured correctly. Additionally, you need to install a specific Python library that is not available in the default environment. What steps should you take to achieve this?
A
Create a cluster with the Databricks Runtime for Machine Learning, and use the init script feature to install the library.
B
Create a custom Databricks Runtime for Machine Learning that includes the library in its base image.
C
Install the library using the Databricks Library management system at the cluster scope after creating the cluster.
D
Manually install the library in each notebook using a pip command.
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