A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream…

Question

Options

• AParse the Delta Lake transaction log to identify all newly written data files.
• BExecute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for
• CExecute a query to calculate the difference between the new version and the previous version
• DParse the Spark event logs to identify those rows that were updated, inserted, or deleted.

• A
  5% (2)
• B
  2% (1)
• C
  80% (33)
• D
  12% (5)

Explanation

Delta Lake automatically maintains a versioned transaction log for every table operation. After an overwrite, the new version is version N and the previous is version N-1. Delta's time travel feature lets you query any prior version using VERSION AS OF syntax. You can therefore execute a query that compares SELECT * FROM customer_parsams VERSION AS OF N against SELECT * FROM customer_parsams VERSION AS OF N-1 using set operations (EXCEPT, UNION, etc.) to compute the exact diff. Option A (parsing raw transaction log files) is error-prone and unnecessary given built-in SQL support. Option B references DESCRIBE HISTORY, which gives operation-level metadata and row counts but not row-level diffs. Option D (Spark event logs) captures execution details, not row-level data changes.

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