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Answer: Use the Databricks Library management system to install the library at the workspace scope, ensuring all team members have access to it.
Option B is the recommended approach as it centralizes the library management process, ensuring all team members have access to the required library without the need for individual installations. This method reduces the risk of version inconsistencies and simplifies environment management.
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As a data scientist working on a machine learning project in Databricks, you need to share a specific Python library with your team members who are also working on the same project. The library is crucial for preprocessing steps in your machine learning pipeline. What is the recommended way to make this library available to all team members?
A
Instruct each team member to install the library in their local development environment and include the installation step in the project documentation.
B
Use the Databricks Library management system to install the library at the workspace scope, ensuring all team members have access to it.
C
Add the library installation command to the beginning of each notebook and ask team members to run it before executing the rest of the notebook.
D
Create a custom Databricks Runtime for Machine Learning that includes the library and deploy it for all team members to use.
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