Employee Record Completeness Audit

EASY10 pts

A workforce data platform ingests employee identity details and compensation records from separate systems. Data quality checks must identify IDs where one side is missing.

Table: employee_directory

employee_id: unique employee identifier
name: employee display name

Table: payroll

employee_id: unique employee identifier
salary: employee base salary value

Data guarantees:

employee_directory.employee_id is unique.
payroll.employee_id is unique.

Incomplete profile definition: An employee record is incomplete when employee_id exists in exactly one table.

Task: Return every employee_id that has incomplete profile coverage.

Output requirements:

Return exactly one column:
- employee_id
Sort by employee_id in ascending order.

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

employee_directory:
| employee_id | name     |
|-------------|----------|
| 2           | Crew     |
| 4           | Haven    |
| 5           | Kristian |

payroll:
| employee_id | salary |
|-------------|--------|
| 5           | 76071  |
| 1           | 22517  |
| 4           | 63539  |

Output

[
  {"employee_id":1},
  {"employee_id":2}
]

Explanation

Employee 1 appears only in payroll, and employee 2 appears only in employee_directory. IDs 4 and 5 exist in both sources and are complete.

Example

Input

employee_directory:
| employee_id | name    |
|-------------|---------|
| 10          | Alex    |
| 11          | Bianca  |

payroll:
| employee_id | salary |
|-------------|--------|
| 10          | 80000  |
| 11          | 90000  |

Output

[]

Explanation

Every employee_id appears in both tables, so there are no incomplete records.

Example

Input

employee_directory:
| employee_id | name      |
|-------------|-----------|
| 100         | Priya     |
| 200         | Omar      |
| 350         | Linh      |
| 700         | Gabriel   |

payroll:
| employee_id | salary |
|-------------|--------|
| 50          | 72000  |
| 100         | 81000  |
| 700         | 95000  |
| 900         | 66000  |

Output

[
  {"employee_id":50},
  {"employee_id":200},
  {"employee_id":350},
  {"employee_id":900}
]

Explanation

The result is the symmetric difference of IDs across both tables.

Accepted0/0·0% Acceptance

Constraints

0 <= rows in employee_directory <= 250000
0 <= rows in payroll <= 250000
1 <= employee_id <= 1000000000
1 <= length(name) <= 255
0 <= salary <= 10000000
employee_directory.employee_id is unique
payroll.employee_id is unique
Return exactly one column: employee_id
Sort by employee_id ascending
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with column: employee_id

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

payroll =

[{"employee_id":5,"salary":76071},{"employee_id":1,"salary":22517},{"employee_id":4,"salary":63539}]

employee_directory =

[{"employee_id":2,"name":"Crew"},{"employee_id":4,"name":"Haven"},{"employee_id":5,"name":"Kristian"}]

Employee Record Completeness Audit

Hints

Employee Record Completeness Audit

Hints