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Answer: Restrict all notebooks to use a single, project-wide library version defined at the cluster level.
Enforcing one consistent library version cluster-wide can help avoid conflicts, though it may be inflexible if different notebooks require different versions. This approach is coarse and doesn't aid in diagnosing which notebook caused conflicts, making it usually not the best or most practical solution. Using shell commands to inspect installed packages (Option B) can help find version conflicts but is manual and doesn't solve the root cause for dependency isolation. Implementing a custom library management layer (Option A) is complex and typically unnecessary, adding maintenance overhead. While Databricks Repos integrates with Git for source control (Option D), it doesn't inherently isolate library dependencies per notebook, and Git submodules manage code dependencies, not Python library versions.
Author: LeetQuiz Editorial Team
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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.
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