A data engineer is cleaning a Bronze table that receives the same customer records from...

Option D uses the business-key columns as the subset for duplicate comparison. Databricks documents that DataFrame.dropDuplicates can optionally accept a list of columns and then removes rows that are duplicates with respect to only those columns. Therefore, differing ingestion_timestamp values do not prevent records with the same customer_id and email from being treated as duplicates. Calling distinct() compares complete rows, so timestamp differences would preserve multiple records. Selecting only customer_id and email before distinct would remove every other column from the Silver result. The groupBy and max expression calculates a latest timestamp per key, but it changes the output shape and does not preserve a complete source record unless additional logic is added. For the stated requirement, option D is the direct and correct operation.

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