Explanation:
The most effective method to ensure a specific version of the numpy library is used across all notebooks in a Databricks workspace is to add numpy== to the cluster's library dependencies. This approach guarantees that the specified version of numpy is installed and consistently available in all notebooks attached to the cluster, without the need for manual installations or environment variable adjustments.
Thus, adding the library to the cluster's dependencies is the recommended approach for consistency and reliability.
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A machine learning team is working on a project that requires a specific version of the numpy library. They aim to ensure this version is used across all notebooks in their Databricks workspace. What is the best approach to achieve this?
A
Run %pip install numpy== once on any notebook attached to the cluster.
B
Edit the cluster to use the Databricks Runtime for Machine Learning.
C
Add numpy== to the cluster's library dependencies.
D
Set the PYTHON_VERSION variable in the cluster configuration to the required version.
E
There is no way to enforce a specific version of numpy on a cluster.
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