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Rippling Interview Questions

37 practice questions for Rippling technical interviews

Rippling software engineer interviews cover algorithms, data structures, system design, and coding problems drawn from real interview rounds.

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coding Medium Verified Question #1

1. [AI Enabled Coding] Card Game

Category: String coding problem

Question You are building a simplified card game where each player has a hand of cards and the higher-rated hand wins. Each hand contains exactly...

Input: String
Output: Computed result

coding Hard Verified Question #2

2. [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 Medium Verified Question #3

3. [AI Enabled Coding] Food Delivery Company

Category: String coding problem

Question You are building a driver payment system for a food delivery company. The accounting team needs to track how much money is owed to drivers...

Input: String
Output: Integer

coding Hard Verified Question #4

4. [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 #5

5. Top 5 Rippling System Design Questions

Category: Sliding window system design problem

These are commonly asked system design questions from Rippling interviews

Input: Given input
Output: Computed result

technical Medium Verified Question #6

6. How to pass AI Enabled Coding Rounds From FAANG Interviewer

Category: Algorithm coding problem

Tips For AI Coding Rounds AI coding rounds are not as different from regular coding rounds as you might think. The interviewer still needs to get...

Input: Given input
Output: Computed result

coding Medium graph #1

1. Graph — Find the Shortest Path with Weight Constraints

Background: Rippling manages various payroll and human resource data for companies. A crucial feature is optimizing transportation costs associated with employee relocations. To solve this, we need a way to find the most efficient route based on weight constraints of packages.
Problem statement: You are given a directed graph where each edge has a weight representing the cost of transportation. Your task is to write a function that finds the shortest path from a given startNode to an endNode such that the total weight does not exceed a given weightLimit. If no such path exists, return None.
Function/class signature:

def shortest_path_with_weight_limit(graph: Dict[str, List[Tuple[str, int]]], startNode: str, endNode: str, weightLimit: int) -> Optional[List[str]]:

Example 1:

Input: graph = {'A': [('B', 5), ('C', 10)], 'B': [('C', 2)], 'C': []}, startNode = 'A', endNode = 'C', weightLimit = 8

Output: ['A', 'B', 'C']

Explanation: The path A -> B -> C has a total weight of 7 which is within the weight limit.

Example 2:

Input: graph = {'A': [('B', 5), ('C', 10)], 'B': [('C', 2)], 'C': []}, startNode = 'A', endNode = 'C', weightLimit = 6

Output: None

Explanation: No path from A to C exists under the weight limit of 6.

Constraints:

The graph contains at most 10^5 nodes.

Each edge has a weight from 1 to 100.

Weight limit will be a positive integer up to 500.

All nodes are unique strings without spaces.

coding Medium dynamic programming #2

2. Dynamic Programming — Minimum Cost to Hire Employees

Background: At Rippling, companies streamline their employee management processes, and finding the optimal way to hire employees while minimizing costs is crucial. This problem relates to the payroll management system involving salaries and hiring strategies.
Problem statement: You are given an array cost of size n where cost[i] represents the cost of hiring the i-th employee. You can hire employees in blocks (consecutive hires). Since hiring costs may vary, you want to determine the minimum total cost to hire all the employees. Implement a function min_cost(cost: List[int]) -> int that returns this minimum cost.
Function/class signature:

def min_cost(cost: List[int]) -> int:

Example 1:

Input: cost = [10, 20, 30]

Output: 60

Explanation: The total cost to hire all employees directly is 10 + 20 + 30 = 60.

Example 2:

Input: cost = [10, 30, 20, 40]

Output: 100

Explanation: Hiring in blocks optimally leads to minimum cost, which is sequential hire in this case.

Constraints:

1 <= cost.length <= 1000

1 <= cost[i] <= 1000

The total cost should account for different strategies in hiring based on competition and salary expectations.

coding Medium hash map #3

3. Coding Challenge — Implement an employee attendance system

Background: At Rippling, efficient employee management is crucial, especially in handling attendance for various purposes like payroll and compliance. An efficient solution can automate this process, ensuring that all records are accurate and accessible.

Problem statement: You are required to implement a class AttendanceSystem that tracks employee attendance. The class should support methods to mark attendance, check if an employee was present on a given day, and get the total attendance count for an employee. Each employee is identified by a unique employee ID.

Function/class signature:

def __init__(self):

def mark_attendance(self, employee_id: int, date: str) -> None:

def was_present(self, employee_id: int, date: str) -> bool:

def total_attendance(self, employee_id: int) -> int:

Example 1:

Input: mark_attendance(1, "2023-10-01")

Output: None

Explanation: Employee with ID 1 marked present on October 1st, 2023.

Example 2:

Input: was_present(1, "2023-10-01")

Output: True

Explanation: Employee 1 was present on October 1st, 2023.

Constraints:

employee_id is a positive integer.

date is formatted as "YYYY-MM-DD" and represents valid dates only.

The number of attendance records will not exceed 10^5.

coding Medium hash map #4

4. Coding Challenge — Find the Longest Consecutive Sequence

Background: As Rippling enhances its payroll processing system, it needs to analyze employee engagement data more effectively. Tracking consecutive active days of employees is essential for assessing participation in company initiatives.
Problem statement: Given an unsorted array of integers representing employee IDs, write a function that returns the length of the longest consecutive sequence of IDs. The sequence is defined as consecutive integers that differ by 1.
Function/class signature:

def longest_consecutive(nums: List[int]) -> int:

Example 1:

Input: [100, 4, 200, 1, 3, 2]

Output: 4

Explanation: The longest consecutive sequence is [1, 2, 3, 4], which has a length of 4.

Example 2:

Input: [1, 2, 0, 1]

Output: 3

Explanation: The longest consecutive sequence is [0, 1, 2], with a length of 3.

Constraints:

0 <= len(nums) <= 10^5

-10^9 <= nums[i] <= 10^9

coding Hard graph #5

5. [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: 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: 8
Explanation: The optimal connections give a cost of 8 (via employee 1).

Example 2:
Input: start = 1, benefitsGraph = [[0, 2], [2, 0]]
Output: 2
Explanation: 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 #6

6. [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: 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 = 3
Output: 4
Explanation: The longest subarray with an average ≤ 3 is [1, 2, 3, 4].

Example 2:
Input: [5, 1, 3, 2, 5, 4], threshold = 3
Output: 3
Explanation: 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 #7

7. [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: def min_cost_path(graph: Dict[int, List[Tuple[int, int]]], start: int, end: int) -> int

Example 1:
Input: {0: [(1, 5), (2, 10)], 1: [(3, 2)], 2: [(3, 1)], 3: []}, 0, 3
Output: 7
Explanation: 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 #8

8. [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: def max_compliance_score(transactions: List[int]) -> int

Example 1:
Input: [10, 20, 15, 25, 30]
Output: 60
Explanation: 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: 15
Explanation: The transactions 5 and 10 yield the highest score of 15.

Constraints:

1 <= len(transactions) <= 1000

1 <= transactions[i] <= 10000

system design Hard api design #9

9. Design HotelBookingSystem — manage hotel bookings effectively

Background: Rippling aims to streamline the booking process for various hotels by managing inventory and facilitating reservations across different agents. Building a robust HotelBookingSystem will enhance user experience and operational efficiency.
Requirements:
1. Manage multiple hotels and their availability attributes.
2. Allow users to search for hotels based on location, dates, and room types.
3. Facilitate the booking of rooms, ensuring that inventory reflects real-time availability.
4. Handle bookings with customer details and payment confirmation.
Class API:

add_hotel(hotel: Hotel) -> None: Adds a new hotel to the system.

search_hotels(location: str, check_in: str, check_out: str, room_type: str) -> List[Hotel]: Returns a list of available hotels based on search criteria.

book_room(hotel_id: str, customer_details: Customer, payment_info: Payment) -> BookingConfirmation: Books a room for a customer and returns a booking confirmation.

cancel_booking(booking_id: str) -> bool: Cancels an existing booking using the booking ID.

Example 1:
Input: add_hotel(hotel)
Output: None
Explanation: Adds the specified hotel object to the hotel booking system.

Example 2:
Input: search_hotels("New York", "2023-12-01", "2023-12-10", "Deluxe")
Output: [Hotel1, Hotel2]
Explanation: Returns a list of hotels available in New York for the given stay dates and room type.
Constraints:

Maximum of 1000 hotels.

Each hotel can have a maximum of 100 rooms per type.

Search and booking operations should have a response time of under 1 second.

system design Medium api design #10

10. Graph — Implement a hotel booking system

Background: Rippling needs a robust hotel booking system to manage inventory and bookings efficiently across various properties. This system should provide the ability to handle varying room types, availability, and guest reservations effectively.

Problem statement: Create a class HotelBookingSystem that allows users to manage bookings in a hotel. The class should support the following operations: adding hotel rooms, checking availability for a specific date range, and making a reservation if rooms are available. Ensure that the book_room method prevents double bookings and updates the room availability accordingly.

Function/class signature:

class HotelBookingSystem:

def add_room(room_id: int, room_type: str, price: float) -> None:

def check_availability(start_date: str, end_date: str) -> List[str]:

def book_room(room_id: int, guest_name: str, start_date: str, end_date: str) -> bool:

Example 1:

Input: add_room(101, "Suite", 200.0)

Output: None

Explanation: A new room of type "Suite" priced at 200.0 is added.

Example 2:

Input: book_room(101, "John Doe", "2023-10-01", "2023-10-05")

Output: True

Explanation: Room 101 is successfully booked for John Doe from October 1 to October 5.

Constraints:

Room IDs are unique integers.

Dates are given in YYYY-MM-DD format.

There can be a maximum of 100 rooms in the system.

Each room can only be booked by one guest for a specific date range.

system design Senior caching #11

11. [OA] LRU Cache — Design a Cache for Rippling’s Previous Employee Benefits Access

Rippling needs a robust cache system to manage API requests for retrieving previously accessed employee benefits efficiently.
Problem statement: Implement an LRU (Least Recently Used) cache that supports the following operations: get(key: int) -> int (returns the value if the key exists, otherwise -1) and put(key: int, value: int) -> void (updates or adds the key/value pair). When the cache reaches its capacity, it should invalidate the least recently used item.

Class: LRUCache

- Method: get(key: int) -> int - returns the value or -1 if not found.
- Method: put(key: int, value: int) -> void - adds or updates the cache.

Example 1:
Input: put(1, 1)
Input: put(2, 2)
Input: get(1)
Output: 1
Explanation: Cache has key 1 with value 1.

Example 2:
Input: put(3, 3)
Input: get(2)
Output: -1
Explanation: Key 2 was evicted when key 3 was added, as the capacity limitation was reached.

Constraints:

1 ≤ capacity ≤ 3000

system design Senior caching #12

12. [OA] LRU Cache — Implement Rippling's caching layer for transaction history

Rippling processes a large number of transactions, and we need an efficient way to cache recently accessed transaction data to improve retrieval times. Implement a Least Recently Used (LRU) cache that allows storing a limited number of transactions and supports getting and setting transaction data.

Method signatures:

- def __init__(self, capacity: int) — Initialize the cache with a given capacity.
- def get(self, key: int) -> int — Retrieve the value for a given key if it exists, otherwise return -1.
- def put(self, key: int, value: int) -> None — Store the value for a key in the cache, evicting the least recently used item if necessary.

Example 1:
Input:

cache = LRUCache(2);
cache.put(1, 1);
cache.put(2, 2);
cache.get(1);

Output: 1
Explanation: The cache returns the value for key 1. The cache now contains [1, 2] as recently accessed items.

Example 2:
Input:

cache.put(3, 3);
cache.get(2);

Output: -1
Explanation: The key 2 was evicted when key 3 was added to the full cache.

Constraints:

1 <= capacity <= 3000

0 <= key, value <= 10^4

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Rippling Interview Questions

1. [AI Enabled Coding] Card Game

2. [AI Enabled Coding] Design Logger

3. [AI Enabled Coding] Food Delivery Company

4. [AI Enabled Coding] Rule Evaluator

5. Top 5 Rippling System Design Questions

6. How to pass AI Enabled Coding Rounds From FAANG Interviewer

1. Graph — Find the Shortest Path with Weight Constraints

2. Dynamic Programming — Minimum Cost to Hire Employees

3. Coding Challenge — Implement an employee attendance system

4. Coding Challenge — Find the Longest Consecutive Sequence

5. [OA] Dijkstra's Algorithm — Optimize Employee Benefits Allocation in Rippling’s Payroll System

6. [OA] Sliding Window — Optimize the Time Tracking of Rippling's Employee Productivity

7. [OA] Graph Traversal — Find the shortest path to tax filing deadlines

8. [OA] Dynamic Programming — Calculate the tax compliance score for Rippling users

9. Design HotelBookingSystem — manage hotel bookings effectively

10. Graph — Implement a hotel booking system

11. [OA] LRU Cache — Design a Cache for Rippling’s Previous Employee Benefits Access

12. [OA] LRU Cache — Implement Rippling's caching layer for transaction history

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