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Answer: Databricks notebooks support change tracking and versioning
The correct answer is that Databricks notebooks support automatic change tracking and versioning. By checking the version history on the right side while editing, you can view all changes made, as every edit is captured and saved automatically.
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Your colleague is manually copying notebooks with '_bkp' to save previous versions. Which feature would you recommend as a better alternative?
A
Databricks notebooks can be exported into dbc archive files and stored in a data lake
B
Databricks notebooks should be copied to a local machine and set up with source control locally to version the notebooks
C
Databricks notebooks support change tracking and versioning
D
Databricks notebook can be exported as HTML and imported at a later time
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