Airbnb software engineer interviews cover algorithms, data structures, system design, and coding problems drawn from real interview rounds.
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coding
Hard
Verified Question
#1
1. Best Service For Rental Properties
Category: Graph coding problemYou are helping users find the most cost-effective way to get all the services they want for their rental property. You are given: - A list of...Input: Graph (nodes and edges)
Output: Computed result
coding
Hard
Verified Question
#2
2. Best Ski Route
Category: Graph coding problem
Question You are skiing down from the top of a mountain and want to maximize your score when you reach the finish. There are multiple routes you...
Input: Graph (nodes and edges)Output: Computed result
coding
Hard
Verified Question
#3
3. Menu Order Equaling Target Sum
Category: Algorithm coding problemYou are given a menu containing prices of individual items. Given a target amount of money, find all possible combinations of menu items that...Input: Integer(s)
Output: Integer
coding
Hard
Verified Question
#4
4. Most Cost Effective Menu Order
Category: Dynamic programming coding problemYou are building an app that helps users determine the most cost-effective order they can place at a restaurant for the food items they want. You...Input: List
Output: Computed result
coding
Hard
Verified Question
#5
5. Collatz Sequence
Category: Algorithm coding problemThe Collatz conjecture is a famous unsolved problem in mathematics. For any positive integer
n, the sequence is defined as follows: - If n is...Input: Integer(s)Output: Computed result
coding
Hard
Verified Question
#6
6. Implement Refunds
Category: Algorithm coding problem
Question AirBnB has a need to support refunds for our customers in case of booking changes or cancellations.
Input: ListOutput: Array
coding
Hard
sliding window
#1
1. [OA] Sliding Window — Implement search function for Airbnb rentals based on dates
Users often look for homes to rent on specific datetime ranges, and it's essential to efficiently check if the rental is available for the requested dates.
Problem statement: Given a list of
Example 1:
Input: rentals = [(1, (1, 5)), (2, (2, 6)), (3, (5, 10))], query = (3, 4)
Output: [1, 3]
Explanation: Rentals 1 (1 to 5) and 3 (5 to 10) are available in the range of 3 to 4.Example 2:
Input: rentals = [(1, (1, 5)), (2, (6, 10)), (3, (5, 10))], query = (2, 4)
Output: [1]
Explanation: Only Rental 1 is available during the queried dates.Constraints:
Problem statement: Given a list of
rental_id with their corresponding availability ranges in the form of pairs (start_date, end_date) and a queried date range, return a list of rental IDs that are available during the entire period of the query.def search_available_rentals(rentals: List[Tuple[int, Tuple[int, int]]], query: Tuple[int, int]) -> List[int]:- Returns rental IDs that are available for the entire query date range.
Example 1:
Input: rentals = [(1, (1, 5)), (2, (2, 6)), (3, (5, 10))], query = (3, 4)
Output: [1, 3]
Explanation: Rentals 1 (1 to 5) and 3 (5 to 10) are available in the range of 3 to 4.Example 2:
Input: rentals = [(1, (1, 5)), (2, (6, 10)), (3, (5, 10))], query = (2, 4)
Output: [1]
Explanation: Only Rental 1 is available during the queried dates.Constraints:
1 <= rentals.length <= 10^41 <= rental_id <= 10^61 <= start_date < end_date <= 10^61 <= query[0] < query[1] <= 10^6
coding
Hard
graph
#2
2. [OA] Graph Traversal — Implement a neighbor-finding feature for Airbnb properties
In a platform like Airbnb, it's crucial to find neighboring properties efficiently to help users browse similar options. This will enhance the user experience by showing related listings when a user views a particular property.
Problem statement: You need to implement a function that takes a list of properties represented as nodes in a graph and returns a list of all neighboring properties within a certain distance. Each node has a
Example 1:
Input: properties = [[1, [2, 3]], [2, [1, 4]], [3, [1]], [4, [2]]], id = 1, distance = 1
Output: [2, 3]
Explanation: Property 1 is directly connected to properties 2 and 3.Example 2:
Input: properties = [[1, [2, 3]], [2, [1, 4]], [3, [1]], [4, [2]]], id = 1, distance = 2
Output: [2, 3, 4]
Explanation: Property 1 is connected to properties 2 and 3 directly, and property 2 connects to property 4.Constraints:
Problem statement: You need to implement a function that takes a list of properties represented as nodes in a graph and returns a list of all neighboring properties within a certain distance. Each node has a
property_id and a list of neighbors (i.e., directly connected properties).def find_neighbors(properties: List[Property], id: int, distance: int) -> List[int]:- Returns the list of property identifiers of neighboring properties within the specified distance.
Example 1:
Input: properties = [[1, [2, 3]], [2, [1, 4]], [3, [1]], [4, [2]]], id = 1, distance = 1
Output: [2, 3]
Explanation: Property 1 is directly connected to properties 2 and 3.Example 2:
Input: properties = [[1, [2, 3]], [2, [1, 4]], [3, [1]], [4, [2]]], id = 1, distance = 2
Output: [2, 3, 4]
Explanation: Property 1 is connected to properties 2 and 3 directly, and property 2 connects to property 4.Constraints:
1 <= properties.length <= 10001 <= property_id <= 10^60 <= distance <= 50
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