To minimize the number of rows added to the Orders table during refreshes, which solution component should be included?

• A. An Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderID column in the destination lakehouse
• B. A Spark job that reads the entire Orders dataset and appends it
• C. A Power BI refresh task
• D. An SQL Agent job that truncates and reloads the table

Answer: (A)

Incremental loading by using the highest OrderID already in the destination lets the system pull only new rows. The Azure Data Factory pipeline with a dataflow can read the current max OrderID from the destination lakehouse, then load only orders with OrderID greater than that value. This keeps the destination table sized by only new data, avoids reloading existing rows, and reduces processing and bandwidth compared to re-reading the entire dataset. In contrast, reading the whole Orders dataset and appending would duplicate existing data; a Power BI refresh task isn’t an ETL step for updating the lakehouse, and truncating and reloading wipes and rebuilds the table instead of updating it incrementally.