Rippling Backend Engineer Coding Questions

Rippling aims to keep track of how many employees are active within certain hours to optimize productivity resources. A sliding window approach can facilitate this by keeping real-time data.
Problem Statement: Given an array activeHours, where each integer represents the hours when employees are active, calculate the maximum number of employees that can be active during any specific period of time using the sliding window technique.Example 1:
Input: activeHours = [1, 2, 2, 3, 3, 4, 5]
Output: 5
Explanation: The peak active hours would merge to add up to 5 employees active between hour 1 and hour 5.Example 2:
Input: activeHours = [1, 3, 4, 6, 7]
Output: 2
Explanation: The sliding window would find two peak hours.Constraints:
- 1 <= activeHours.length <= 10^4
- 0 <= activeHours[i] <= 24
- Hours are represented in a 24-hour format.

In Rippling's payroll system, calculating employee taxes accurately and efficiently is vital. As the number of employees grows, optimizing this process using effective algorithms becomes essential.
Problem Statement: Given an array of unique employees where each employee has a specific tax bracket as an integer, find the most optimal way to calculate the total taxes owed using Dynamic Programming. Return the total amount owed after optimizing the tax determination based on the employee array.Example 1:
Input: employees = [1000, 2500, 4500, 6000]
Output: 1450
Explanation: The tax calculations for employees will yield a total of 1450 after applying the optimal tax structure.Example 2:
Input: employees = [3000, 4000, 5500]
Output: 850
Explanation: The system optimizes the calculation to achieve a total of 850 based on the tax rules applied.Constraints:
- 1 <= employees.length <= 10^4
- 0 <= employees[i] <= 10^6
- Every employees value is unique.