Question

A Fabric lakehouse ingests a JSON file where each row represents an order and contains an array column named LineItems, with each element holding a product SKU and quantity. In a PySpark notebook, you need to transform this into a flattened table where each order line becomes its own row before loading it into a Delta table for star-schema modeling. Which Spark transformation should you apply to the LineItems array column?

Accepted Answer

Use explode() to generate one output row per element in the LineItems array.

Answer

Use collect_list() to gather the LineItems elements into a single grouped row.

Answer

Use pivot() on the LineItems column to spread elements into separate columns.

Answer

Use concat_ws() to join the LineItems elements into a delimited string.