Senior-Priority Budget Hiring Plan

Hard

A recruiting team is filling open roles from a candidate pool and must obey a strict staffing policy tied to a fixed monthly compensation budget. Table: Candidates employee_id: unique candidate identifier experience: candidate band, either "Senior" or "Junior" salary: expected monthly salary Budget policy: Total salary budget is exactly 70000. Phase 1: hire as many Senior candidates as possible. Phase 2: after seniors are finalized, spend only the remaining budget to hire as many Junior candidates as possible. Important interpretation details: "As many as possible" means maximizing headcount, not maximizing salary spend. Within each experience group, selecting the lowest salaries first yields maximal count. The chosen set of juniors depends on budget left after the optimal senior selection. Task: Return the final hiring counts for both experience levels. Output requirements: Return exactly two columns: experience accepted_candidates Include two experience labels in output: Senior Junior Output row order can be arbitrary. Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Candidates:
| employee_id | experience | salary |
|-------------|------------|--------|
| 1           | Junior     | 10000  |
| 9           | Junior     | 10000  |
| 2           | Senior     | 20000  |
| 11          | Senior     | 20000  |
| 13          | Senior     | 50000  |
| 4           | Junior     | 40000  |

Output

[
  {"experience":"Senior","accepted_candidates":2},
  {"experience":"Junior","accepted_candidates":2}
]

Explanation

The best senior choice is salaries 20000 and 20000, spending 40000 and leaving 30000. With that remainder, two juniors with salary 10000 each can be hired.

Example 2

Input

Candidates:
| employee_id | experience | salary |
|-------------|------------|--------|
| 1           | Junior     | 10000  |
| 9           | Junior     | 10000  |
| 2           | Senior     | 80000  |
| 11          | Senior     | 80000  |
| 13          | Senior     | 80000  |
| 4           | Junior     | 40000  |

Output

[
  {"experience":"Senior","accepted_candidates":0},
  {"experience":"Junior","accepted_candidates":3}
]

Explanation

No senior is affordable under the 70000 budget. The full budget is therefore available to juniors, allowing all three junior candidates to be hired.

Example 3

Input

Candidates:
| employee_id | experience | salary |
|-------------|------------|--------|
| 101         | Senior     | 35000  |
| 102         | Senior     | 35000  |
| 103         | Senior     | 35000  |
| 201         | Junior     | 12000  |
| 202         | Junior     | 15000  |

Output

[
  {"experience":"Senior","accepted_candidates":2},
  {"experience":"Junior","accepted_candidates":0}
]

Explanation

Two seniors consume the full budget (35000 + 35000 = 70000), leaving no budget for junior hiring.

Constraints

1 <= number of rows in Candidates <= 10000
1 <= employee_id <= 1000000000
employee_id is unique
experience is either Senior or Junior
1 <= salary <= 120000
Total hiring budget is fixed at 70000
The selected senior set must maximize senior headcount first
Junior selection uses only remaining budget after seniors
Return columns exactly as: experience, accepted_candidates
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with columns [experience, accepted_candidates]

Candidates: | employee_id | experience | salary | |-------------|------------|--------| | 1 | Junior | 10000 | | 9 | Junior | 10000 | | 2 | Senior | 20000 | | 11 | Senior | 20000 | | 13 | Senior | 50000 | | 4 | Junior | 40000 |

Candidates: | employee_id | experience | salary | |-------------|------------|--------| | 1 | Junior | 10000 | | 9 | Junior | 10000 | | 2 | Senior | 80000 | | 11 | Senior | 80000 | | 13 | Senior | 80000 | | 4 | Junior | 40000 |

Candidates: | employee_id | experience | salary | |-------------|------------|--------| | 101 | Senior | 35000 | | 102 | Senior | 35000 | | 103 | Senior | 35000 | | 201 | Junior | 12000 | | 202 | Junior | 15000 |