Option D is correct because Amazon Redshift stored procedures are designed to encapsulate a sequence of SQL statements and business logic inside the database. AWS documentation states that stored procedures are commonly used for data transformation, data validation, and business-specific logic, and that they can combine multiple SQL steps into one procedure. AWS also documents the standard Redshift pattern for deleting stale rows and inserting fresh rows from a staging table, which is exactly the requirement here. Keeping the operation inside Redshift is the most direct way to maintain an up-to-date orders table for downstream consumers.

Option A is incorrect because Redshift Spectrum is for querying external data in S3, not for performing this in-place Redshift table-maintenance pattern. Option B adds unnecessary unload and reload steps, creating delay and operational complexity. Option C is also unsuitable because Athena federated queries are not the right mechanism for transactional maintenance of Redshift tables. The correct DEA-C01-style answer is to use Redshift-native procedural SQL to delete stale rows and insert current rows from staging.