Opper

SQL

Natural language to SQL query generation evaluates text-to-query fidelity and schema reasoning. This task is particularly relevant for analytics chat assistants and simplified database interfaces where users need to query data using natural language. Models must understand both the intent behind the question and the structure of the underlying database schema.

SQL – gcp/gemini-2.5-pro Task | Opper AI

Input Tokens

0

Output Tokens

0

Duration

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Cost

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Context

Input

For each dealership, rank employees by total sales amount in descending order. In the results I want to see dealership_name, employee_name , total_sales and sales_rank

Expected output

SELECT d.name AS dealership_name, e.name AS employee_name, SUM(s.sale_price) AS total_sales, RANK() OVER (PARTITION BY d.branch_id ORDER BY SUM(s.sale_price) DESC) AS sales_rank FROM sales s JOIN employees e ON s.employee_id = e.employee_id JOIN dealerships d ON s.branch_id = d.branch_id GROUP BY d.name, e.name, d.branch_id;

Model output

WITH EmployeeTotalSales AS (
    SELECT
        employee_id,
        SUM(sale_price) AS total_sales
    FROM
        sales
    GROUP BY
        employee_id
)
SELECT
    d.name AS dealership_name,
    e.name AS employee_name,
    ets.total_sales,
    RANK() OVER (PARTITION BY d.name ORDER BY ets.total_sales DESC) AS sales_rank
FROM
    employees e
JOIN
    dealerships d ON e.branch_id = d.branch_id
JOIN
    EmployeeTotalSales ets ON e.employee_id = ets.employee_id
ORDER BY
    dealership_name,
    sales_rank;