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Answer: Create a cluster with the Databricks Runtime for Machine Learning, then use the Databricks library management system to install the required Python library.
Option B is the correct approach as it leverages the Databricks Runtime for Machine Learning, which is optimized for machine learning workloads. Additionally, using the Databricks library management system ensures that the library is available to all notebooks without manual installation in each one.
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You are tasked with setting up a new Databricks environment for a machine learning project. Your team requires a cluster that can handle the computational demands of training large-scale models. How would you go about creating a cluster with the Databricks Runtime for Machine Learning and ensuring that a specific Python library is available to all notebooks?
A
Create a standard Databricks cluster and manually install the library in each notebook.
B
Create a cluster with the Databricks Runtime for Machine Learning, then use the Databricks library management system to install the required Python library.
C
Use the Databricks Runtime for Machine Learning to create a cluster and assume that all necessary libraries are pre-installed.
D
Install the Python library on your local machine and push the changes to the Databricks environment.
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