Databricks Data Engineer Associate Question 190
Single answerYou are working with a dataset in Databricks that tracks monthly sales for different products in a long format. The dataset has the following columns: product_id, month, and sales. You want to transform this dataset into a wide format where each month becomes a column, with the sales values as the corresponding column values for each product_id. Which approach should you use?
- A
Use the PIVOT clause to transform the dataset into a wide format.
- B
Use the GROUP BY clause to aggregate the dataset and create one row per product_id.
- C
Use the JOIN clause to combine rows for each product_id into a single row.
- D
Use the FILTER clause to select only the required rows for each product_id.
Show answer and explanation
Correct answer: A
Explanation
The PIVOT clause is the correct approach for transforming a dataset from a long format (e.g., rows for each month) to a wide format (e.g., columns for each month). It allows you to convert unique values in one column into multiple columns, which is exactly the requirement in this scenario. Other clauses like GROUP BY, JOIN, or FILTER do not provide this functionality.
- A. Correct.
The PIVOT clause is specifically designed to transform a dataset from a long format to a wide format by turning unique values from one column (e.g.,
month) into multiple columns and using another column's values (e.g.,sales) to populate those columns. - B. Incorrect.
The GROUP BY clause is used to aggregate data but does not pivot it into a wide format. It cannot be used to turn column values into column headers.
- C. Incorrect.
The JOIN clause is used to combine rows from different tables or datasets based on a key, but it cannot transform data into a wide format.
- D. Incorrect.
The FILTER clause is used to filter rows based on a condition. It does not perform any transformation or reshaping of data.