coding
Hard
Verified Question
#1
1. [AI Enabled Coding] Design Logger
Category: Array coding problem# Question You need to design a logger library for a new application. The design should be able to allow us to easily add future loggers, like a db...Input: Array
Output: Printed output
coding
Hard
Verified Question
#2
2. [AI Enabled Coding] Rule Evaluator
Category: String coding problem# Question You need to build a rule evaluation system for a corporate credit card platform. Managers should be able to create rules that enforce...Input: List
Output: Computed result
system design
Hard
Verified Question
#3
3. Top 5 Rippling System Design Questions
Category: Sliding window system design problemThese are commonly asked system design questions from Rippling interviewsInput: Given input
Output: Computed result
coding
Hard
graph
#1
1. [OA] Dijkstra's Algorithm — Optimize Employee Benefits Allocation in Rippling’s Payroll System
Rippling aims to allocate benefits to employees based on their roles and locations efficiently.
Problem statement: You have a graph representing employees as nodes and available benefits as edges with weights belonging to those employees. Determine the minimum weight (cost) required to connect all benefits (nodes) starting from a specific employee node.
- Method:
Input:
Output:
Explanation: The optimal connections give a cost of 8 (via employee 1).Example 2:
Input:
Output:
Explanation: Only one connection is the minimum cost (from employee 1 to 0).Constraints:
-
-
Problem statement: You have a graph representing employees as nodes and available benefits as edges with weights belonging to those employees. Determine the minimum weight (cost) required to connect all benefits (nodes) starting from a specific employee node.
- Method:
minBenefitsCost(int start, List<List<int>> benefitsGraph) -> int - returns the minimum cost to connect all benefits.Example 1:Input:
start = 0, benefitsGraph = [[0, 5, 10], [5, 0, 3], [10, 3, 0]]Output:
8Explanation: The optimal connections give a cost of 8 (via employee 1).Example 2:
Input:
start = 1, benefitsGraph = [[0, 2], [2, 0]]Output:
2Explanation: Only one connection is the minimum cost (from employee 1 to 0).Constraints:
-
1 ≤ benefitsGraph.length ≤ 1000-
1 ≤ benefitsGraph[i][j] ≤ 10^4
coding
Hard
sliding window
#2
2. [OA] Sliding Window — Optimize the Time Tracking of Rippling's Employee Productivity
Rippling needs a solution to determine the longest sequence of time logs that maintain a consistent productivity rate when tracking employee hours.
Problem statement: Given an array of integers representing employee productivity per hour, find the longest contiguous subarray where the average productivity does not exceed a given threshold. The method should return the length of this subarray.
- Method:
Input:
Output:
Explanation: The longest subarray with an average ≤ 3 is
Input:
Output:
Explanation: The longest subarray with an average ≤ 3 is
-
-
-
Problem statement: Given an array of integers representing employee productivity per hour, find the longest contiguous subarray where the average productivity does not exceed a given threshold. The method should return the length of this subarray.
- Method:
longestSubarray(int[] productivity, int threshold) -> int - returns the length of the longest subarray where average productivity <= threshold.Example 1:Input:
[1, 2, 3, 4, 2, 3], threshold = 3Output:
4Explanation: The longest subarray with an average ≤ 3 is
[1, 2, 3, 4].Example 2:Input:
[5, 1, 3, 2, 5, 4], threshold = 3Output:
3Explanation: The longest subarray with an average ≤ 3 is
[1, 3, 2].Constraints:-
1 ≤ productivity.length ≤ 10^6-
1 ≤ productivity[i] ≤ 100-
1 ≤ threshold ≤ 100
coding
Hard
graph
#3
3. [OA] Graph Traversal — Find the shortest path to tax filing deadlines
Rippling offers tax filing services where users need to determine the shortest path to reach various filing deadlines based on their transaction history. Given a directed graph of filing options and their associated costs, compute the minimum cost to reach a specified deadline.
- Function signature:
Input:
Output:
Explanation: The shortest path is via node 0 to 1 to 3 with a total cost of
-
- Each node's neighbor list contains no more than
- Function signature:
def min_cost_path(graph: Dict[int, List[Tuple[int, int]]], start: int, end: int) -> intExample 1:Input:
{0: [(1, 5), (2, 10)], 1: [(3, 2)], 2: [(3, 1)], 3: []}, 0, 3Output:
7Explanation: The shortest path is via node 0 to 1 to 3 with a total cost of
5 + 2 = 7.Constraints:-
1 <= len(graph) <= 1000- Each node's neighbor list contains no more than
10 entries.
coding
Hard
dynamic programming
#4
4. [OA] Dynamic Programming — Calculate the tax compliance score for Rippling users
In the context of Rippling's payroll and tax compliance services, we need an efficient way to determine the overall compliance score for a given user based on their transactions and filing history.
Given a list of user transactions and their corresponding compliance values, compute the maximum compliance score that can be achieved using at most one transaction from each period.
- Function signature:
Input:
Output:
Explanation: The maximum compliance score is achieved by taking transactions with values
Input:
Output:
Explanation: The transactions
-
-
Given a list of user transactions and their corresponding compliance values, compute the maximum compliance score that can be achieved using at most one transaction from each period.
- Function signature:
def max_compliance_score(transactions: List[int]) -> intExample 1:Input:
[10, 20, 15, 25, 30]Output:
60Explanation: The maximum compliance score is achieved by taking transactions with values
10, 20, and 30, leading to a total of 60.Example 2:Input:
[5, 1, 2, 10]Output:
15Explanation: The transactions
5 and 10 yield the highest score of 15.Constraints:-
1 <= len(transactions) <= 1000-
1 <= transactions[i] <= 10000Start practicing Rippling questions
Sign up for free to access walkthroughs, AI-generated questions, and more.
Get Started Free