What describes Delta Lake's Auto Compaction feature?

Study for the Databricks Data Engineering Professional Exam. Engage with multiple choice questions, each offering hints and in-depth explanations. Prepare effectively for your exam today!

Delta Lake's Auto Compaction feature is designed to improve the performance and efficiency of data storage by managing the sizes of the data files generated during write operations. When new data is written to a Delta Lake table, the Auto Compaction feature automatically triggers after the write operation is completed. The primary goal is to optimize the size of the files to ensure better performance during reads and writes.

The chosen answer indicates that after a write operation, the Auto Compaction feature optimizes files toward a target size of 128 MB. This size is significant because it strikes a balance between having files that are not too small, which would lead to a larger number of files and increased overhead, and not too large, which could potentially slow down read operations.

Other options do not accurately capture the workings of Delta Lake's Auto Compaction. For example, stating that it optimizes files toward 1 GB is incorrect, as the target size for Delta Lake’s Auto Compaction is specifically around 128 MB. Options involving running OPTIMIZE on all tables before a job cluster terminates misrepresent the purpose and trigger of the Auto Compaction feature, and simply appending new rows to an unbounded table does not describe the optimization aspect. Thus, detailing the function and size

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