Databricks Certified Data Engineer - Associate
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A data engineer is overwriting data in a table by deleting the table and recreating the table. Another data engineer suggests that this is inefficient and the table should simply be overwritten instead. Which of the following reasons to overwrite the table instead of deleting and recreating the table is incorrect?
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Explanation:
Explanation
Let's analyze each option:
A. Overwriting a table is efficient because no files need to be deleted. - INCORRECT When you overwrite a table in Databricks, the underlying files are indeed deleted and replaced with new files. This is not more efficient than deleting and recreating in terms of file operations. Both operations involve file deletion and creation.
B. Overwriting a table results in a clean table history for logging and audit purposes. - CORRECT Overwriting maintains a cleaner table history as it's a single operation in the transaction log, making audit trails more straightforward.
C. Overwriting a table maintains the old version of the table for Time Travel. - CORRECT Databricks Time Travel allows you to access previous versions of tables when using operations like OVERWRITE, preserving historical data.
D. Overwriting a table is an atomic operation and will not leave the table in an unfinished state. - CORRECT Overwrite operations are atomic, meaning they either complete fully or not at all, preventing partial table states.
E. Overwriting a table allows for concurrent queries to be completed while in progress. - CORRECT Overwrite operations in Databricks are designed to allow concurrent reads to continue using the previous version while the new version is being written.
Key Point: Option A is incorrect because overwriting a table does require deleting the existing files and replacing them with new files, making it no more efficient than delete-and-recreate in terms of file operations.
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