Databricks Certified Data Engineer - Associate

Explanation

The correct answer is PIVOT.

Why PIVOT is correct:

1. PIVOT operation: In SQL, the PIVOT operation is specifically designed to transform data from a long format (where each row represents a single observation) to a wide format (where multiple observations are represented as columns).
2. Spark SQL support: Databricks Spark SQL supports the PIVOT clause, which allows you to rotate rows into columns, aggregating data as needed.
3. Common use case: PIVOT is commonly used for creating cross-tabulation reports, transforming normalized data into denormalized formats for reporting and analysis.

Why other options are incorrect:

• TRANSFORM: This is not a standard SQL keyword for converting table formats. In some contexts, it might refer to data transformation operations, but not specifically for long-to-wide conversion.
• SUM: This is an aggregation function used to calculate totals, not for restructuring table formats.
• CONVERT: This is typically used for data type conversion (e.g., converting strings to dates) or character set conversion, not for restructuring table layouts.

Example usage in Spark SQL:

SELECT *
FROM sales_data
PIVOT (
  SUM(amount)
  FOR product_category IN ('Electronics', 'Clothing', 'Books')
)

SELECT *
FROM sales_data
PIVOT (
  SUM(amount)
  FOR product_category IN ('Electronics', 'Clothing', 'Books')
)

This would transform rows with different product categories into columns with aggregated sales amounts.