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Answer: Use the Databricks Library management system to install the library at the cluster scope with a specific version number.
Option B is the best practice as it ensures that all team members are using the same library version by installing it at the cluster scope. This approach avoids discrepancies that may arise from different local installations and simplifies environment management.
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In a collaborative machine learning project, you need to ensure that all team members have access to the same Python library versions across different notebooks in the Databricks Runtime for Machine Learning. What is the best practice to achieve this?
A
Instruct each team member to install the library using pip in their local environment and commit the installation to version control.
B
Use the Databricks Library management system to install the library at the cluster scope with a specific version number.
C
Add the library installation command to the notebook's initialization script and share the notebook with the team.
D
Create a Docker image with the required library and use it to create a custom Databricks Runtime for Machine Learning.
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