Employee Identity Bridge Report

Easy

A workforce analytics platform stores core employee records and a separate mapping table that assigns an optional enterprise-wide unique identifier. Table: employees id: primary employee identifier name: employee display name Table: employee_uni id: employee identifier unique_id: enterprise-wide unique identifier for that employee Task: Return a directory view that includes every employee from employees and shows their mapped unique_id when present. For employees without a mapping row in employee_uni, return unique_id as null. Output requirements: Return exactly two columns in this order: unique_id, name Include one row per employee Result row order is not important Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

employees:
| id | name     |
|----|----------|
| 1  | Alice    |
| 7  | Bob      |
| 11 | Meir     |
| 90 | Winston  |
| 3  | Jonathan |

employee_uni:
| id | unique_id |
|----|-----------|
| 3  | 1         |
| 11 | 2         |
| 90 | 3         |

Output

[
  {"unique_id":null,"name":"Alice"},
  {"unique_id":null,"name":"Bob"},
  {"unique_id":2,"name":"Meir"},
  {"unique_id":3,"name":"Winston"},
  {"unique_id":1,"name":"Jonathan"}
]

Explanation

Only three employees are mapped in employee_uni. Others remain in the output with unique_id = null.

Example 2

Input

employees:
| id | name   |
|----|--------|
| 10 | Nora   |
| 11 | Luis   |
| 12 | Priya  |

employee_uni:
| id | unique_id |
|----|-----------|
| 10 | 9010      |
| 11 | 9011      |
| 12 | 9012      |

Output

[
  {"unique_id":9010,"name":"Nora"},
  {"unique_id":9011,"name":"Luis"},
  {"unique_id":9012,"name":"Priya"}
]

Explanation

Every employee has a unique identifier mapping, so no null values appear.

Example 3

Input

employees:
| id | name  |
|----|-------|
| 40 | Omar  |
| 41 | Omar  |
| 42 | Lina  |

employee_uni:
| id | unique_id |
|----|-----------|
| 41 | 7001      |

Output

[
  {"unique_id":null,"name":"Omar"},
  {"unique_id":7001,"name":"Omar"},
  {"unique_id":null,"name":"Lina"}
]

Explanation

Mapping is done by id, not by name. Two employees can share the same name but have different mapping outcomes.

Constraints

0 <= number of rows in employees <= 200000
0 <= number of rows in employee_uni <= 200000
employees.id is unique
employee_uni.id is unique
employee_uni.unique_id is unique
1 <= employees.id, employee_uni.id, employee_uni.unique_id <= 1000000000
name is a non-empty string
Return exactly: unique_id, name
Include all employees even when no mapping exists
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with columns [unique_id, name]

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Employee Identity Bridge Report

Easy

A workforce analytics platform stores core employee records and a separate mapping table that assigns an optional enterprise-wide unique identifier. Table: employees id: primary employee identifier name: employee display name Table: employee_uni id: employee identifier unique_id: enterprise-wide unique identifier for that employee Task: Return a directory view that includes every employee from employees and shows their mapped unique_id when present. For employees without a mapping row in employee_uni, return unique_id as null. Output requirements: Return exactly two columns in this order: unique_id, name Include one row per employee Result row order is not important Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

employees:
| id | name     |
|----|----------|
| 1  | Alice    |
| 7  | Bob      |
| 11 | Meir     |
| 90 | Winston  |
| 3  | Jonathan |

employee_uni:
| id | unique_id |
|----|-----------|
| 3  | 1         |
| 11 | 2         |
| 90 | 3         |

Output

[
  {"unique_id":null,"name":"Alice"},
  {"unique_id":null,"name":"Bob"},
  {"unique_id":2,"name":"Meir"},
  {"unique_id":3,"name":"Winston"},
  {"unique_id":1,"name":"Jonathan"}
]

Explanation

Only three employees are mapped in employee_uni. Others remain in the output with unique_id = null.

Example 2

Input

employees:
| id | name   |
|----|--------|
| 10 | Nora   |
| 11 | Luis   |
| 12 | Priya  |

employee_uni:
| id | unique_id |
|----|-----------|
| 10 | 9010      |
| 11 | 9011      |
| 12 | 9012      |

Output

[
  {"unique_id":9010,"name":"Nora"},
  {"unique_id":9011,"name":"Luis"},
  {"unique_id":9012,"name":"Priya"}
]

Explanation

Every employee has a unique identifier mapping, so no null values appear.

Example 3

Input

employees:
| id | name  |
|----|-------|
| 40 | Omar  |
| 41 | Omar  |
| 42 | Lina  |

employee_uni:
| id | unique_id |
|----|-----------|
| 41 | 7001      |

Output

[
  {"unique_id":null,"name":"Omar"},
  {"unique_id":7001,"name":"Omar"},
  {"unique_id":null,"name":"Lina"}
]

Explanation

Mapping is done by id, not by name. Two employees can share the same name but have different mapping outcomes.

Constraints

0 <= number of rows in employees <= 200000
0 <= number of rows in employee_uni <= 200000
employees.id is unique
employee_uni.id is unique
employee_uni.unique_id is unique
1 <= employees.id, employee_uni.id, employee_uni.unique_id <= 1000000000
name is a non-empty string
Return exactly: unique_id, name
Include all employees even when no mapping exists
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with columns [unique_id, name]