Which two actions should you include in the data loading pattern for a Type 1 slowly changing dimension to reflect changes in attributes?

Which two actions should you include in the data loading pattern for a Type 1 slowly changing dimension to reflect changes in attributes?

• A. Update existing rows when non-key attributes have changed and delete old records when the natural key value changes
• B. Update rows when the non-key attributes have changed and insert new records when the natural key is a new value in the table
• C. Insert new rows for every change and never update existing rows
• D. Delete existing rows when attributes change and insert identical copies

Answer: (B)

Type 1 slowly changing dimension overwrites the existing attribute values in a row, keeping no history of previous values. When an incoming record has the same natural key as an existing row, you replace the non-key attributes in that row with the new values. If the incoming record carries a new natural key value, you insert a new row for that business key. This is why the pattern involves updating rows when non-key attributes change and inserting a new row when the natural key is a new value. The approach preserves a single, current version per business key without retaining history. Other patterns would either delete data, create duplicates, or never update existing rows, which doesn’t reflect current attributes in a single-row-per-key representation. For example, if a product’s color changes, update that row in place; if a completely new product (new natural key) arrives, insert a new row for it.

Type 1 slowly changing dimension overwrites the existing attribute values in a row, keeping no history of previous values. When an incoming record has the same natural key as an existing row, you replace the non-key attributes in that row with the new values. If the incoming record carries a new natural key value, you insert a new row for that business key. This is why the pattern involves updating rows when non-key attributes change and inserting a new row when the natural key is a new value. The approach preserves a single, current version per business key without retaining history. Other patterns would either delete data, create duplicates, or never update existing rows, which doesn’t reflect current attributes in a single-row-per-key representation. For example, if a product’s color changes, update that row in place; if a completely new product (new natural key) arrives, insert a new row for it.