The explode function in Spark SQL is specifically designed to transform array or map columns into multiple rows, which is exactly what's needed when converting nested JSON structures into a tabular format with multiple rows.

Why EXPLODE is correct:

• When JSON data contains nested arrays, each element in the array needs to become a separate row in the resulting DataFrame
• The explode function takes an array column and creates a new row for each element in the array
• This is essential for flattening hierarchical JSON structures into relational tables
• For example, if a JSON object contains an array of items, explode will create one row per item while duplicating the other fields

Why other options are incorrect:

• FILTER: Used to select rows based on conditions, not for transforming nested structures into multiple rows
• COALESCE: Returns the first non-null value from a list of expressions, used for handling null values, not for array expansion
• EXTRACT: Used for pattern matching and extracting substrings from strings, not for converting nested JSON arrays into multiple rows

In Azure Databricks Spark jobs, explode is the standard function for this transformation when working with nested JSON data that contains arrays.