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How can you effectively diagnose and resolve dependency conflicts in a multi-notebook Databricks project where different notebooks import conflicting library versions?
A
Implement a custom library management layer that dynamically adjusts library paths based on notebook execution.
B
Use %sh pip list in each notebook to identify conflicts and manually adjust library versions.
C
Restrict all notebooks to use a single, project-wide library version defined at the cluster level.
D
Utilize Databricks Repos to manage notebook dependencies through Git submodules, isolating conflicting dependencies.
