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A data science team is collaborating on a project in Databricks and needs to implement version control for their notebooks to track changes effectively. What is the best approach to achieve this in Databricks?
A
Use Databricks MLflow Tracking to automatically log notebook versions.
B
Enable the built-in version control feature in Databricks notebooks.
C
Export and commit notebooks to an external version control system like Git.
D
Create snapshots of notebooks at regular intervals using Databricks Jobs.
