Engineering vs Marketing Peak Salary Gap

Easy

A compensation analytics team compares top-end pay across key departments. Table: Salaries emp_name: employee name department: business function label salary: salary amount Data guarantees: (emp_name, department) is unique. There is at least one row for department = Engineering. There is at least one row for department = Marketing. Task: Compute one metric called salary_difference: highest_engineering_salary = maximum salary among Engineering rows highest_marketing_salary = maximum salary among Marketing rows salary_difference = absolute(highest_marketing_salary - highest_engineering_salary) Return exactly one row with one column: salary_difference Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| Kathy    | Engineering | 50000  |
| Roy      | Marketing   | 30000  |
| Charles  | Engineering | 45000  |
| Jack     | Engineering | 85000  |
| Benjamin | Marketing   | 34000  |
| Anthony  | Marketing   | 42000  |
| Edward   | Engineering | 102000 |
| Terry    | Engineering | 44000  |
| Evelyn   | Marketing   | 53000  |
| Arthur   | Engineering | 32000  |

Output

[
  {"salary_difference":49000}
]

Explanation

Top Engineering salary is 102000 and top Marketing salary is 53000, so the absolute difference is 49000.

Example 2

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| Ana      | Engineering | 78000  |
| Ben      | Engineering | 91000  |
| Cara     | Marketing   | 125000 |
| Drew     | Marketing   | 103000 |
| Eli      | Sales       | 220000 |

Output

[
  {"salary_difference":34000}
]

Explanation

Only Engineering and Marketing are compared. Sales does not affect the metric.

Example 3

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| P1       | Engineering | 60000  |
| P2       | Marketing   | 60000  |
| P3       | Engineering | 51000  |
| P4       | Marketing   | 58000  |

Output

[
  {"salary_difference":0}
]

Explanation

Both departments share the same highest salary (60000), so the absolute difference is 0.

Constraints

2 <= number of rows in Salaries <= 1200
(emp_name, department) is unique
1 <= salary <= 1000000
department is a non-empty string
emp_name is a non-empty string
At least one row exists with department = Engineering
At least one row exists with department = Marketing
Return exactly one row and one column: salary_difference
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with exactly one column named salary_difference

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Engineering vs Marketing Peak Salary Gap

Easy

A compensation analytics team compares top-end pay across key departments. Table: Salaries emp_name: employee name department: business function label salary: salary amount Data guarantees: (emp_name, department) is unique. There is at least one row for department = Engineering. There is at least one row for department = Marketing. Task: Compute one metric called salary_difference: highest_engineering_salary = maximum salary among Engineering rows highest_marketing_salary = maximum salary among Marketing rows salary_difference = absolute(highest_marketing_salary - highest_engineering_salary) Return exactly one row with one column: salary_difference Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| Kathy    | Engineering | 50000  |
| Roy      | Marketing   | 30000  |
| Charles  | Engineering | 45000  |
| Jack     | Engineering | 85000  |
| Benjamin | Marketing   | 34000  |
| Anthony  | Marketing   | 42000  |
| Edward   | Engineering | 102000 |
| Terry    | Engineering | 44000  |
| Evelyn   | Marketing   | 53000  |
| Arthur   | Engineering | 32000  |

Output

[
  {"salary_difference":49000}
]

Explanation

Top Engineering salary is 102000 and top Marketing salary is 53000, so the absolute difference is 49000.

Example 2

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| Ana      | Engineering | 78000  |
| Ben      | Engineering | 91000  |
| Cara     | Marketing   | 125000 |
| Drew     | Marketing   | 103000 |
| Eli      | Sales       | 220000 |

Output

[
  {"salary_difference":34000}
]

Explanation

Only Engineering and Marketing are compared. Sales does not affect the metric.

Example 3

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| P1       | Engineering | 60000  |
| P2       | Marketing   | 60000  |
| P3       | Engineering | 51000  |
| P4       | Marketing   | 58000  |

Output

[
  {"salary_difference":0}
]

Explanation

Both departments share the same highest salary (60000), so the absolute difference is 0.

Constraints

2 <= number of rows in Salaries <= 1200
(emp_name, department) is unique
1 <= salary <= 1000000
department is a non-empty string
emp_name is a non-empty string
At least one row exists with department = Engineering
At least one row exists with department = Marketing
Return exactly one row and one column: salary_difference
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with exactly one column named salary_difference