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Top 2 AppFolio Coding Interview Questions from 2025

Updated
3 min read

Introduction

In this blog post, we'll share the most commonly asked coding interview questions at AppFolio. If you don't have months to study for your interviews, you can use AI tools like Chatmagic to generate solutions quickly and efficiently - helping you pass the interviews and get the job offer!

Problem #1: Shortest Path in a Grid with Obstacles Elimination

You are given an m x n integer matrix grid where each cell is either 0 (empty) or 1 (obstacle). You can move up, down, left, or right from and to an empty cell in one step. Return the minimum number of steps to walk from the upper left corner (0, 0) to the lower right corner (m - 1, n - 1) given that you can eliminate at most k obstacles. If it is not possible to find such walk return -1. Example 1: Input: grid = [[0,0,0],[1,1,0],[0,0,0],[0,1,1],[0,0,0]], k = 1 Output: 6 Explanation: The shortest path without eliminating any obstacle is 10. The shortest path with one obstacle elimination at position (3,2) is 6. Such path is (0,0) -> (0,1) -> (0,2) -> (1,2) -> (2,2) -> (3,2) -> (4,2). Example 2: Input: grid = [[0,1,1],[1,1,1],[1,0,0]], k = 1 Output: -1 Explanation: We need to eliminate at least two obstacles to find such a walk. Constraints: m == grid.length n == grid[i].length 1 <= m, n <= 40 1 <= k <= m * n grid[i][j] is either 0 or 1. grid[0][0] == grid[m - 1][n - 1] == 0

Topics: Array, Breadth-First Search, Matrix

Problem #2: LRU Cache

Design a data structure that follows the constraints of a Least Recently Used (LRU) cache. Implement the LRUCache class: LRUCache(int capacity) Initialize the LRU cache with positive size capacity. int get(int key) Return the value of the key if the key exists, otherwise return -1. void put(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key. The functions get and put must each run in O(1) average time complexity. Example 1: Input ["LRUCache", "put", "put", "get", "put", "get", "put", "get", "get", "get"] [[2], [1, 1], [2, 2], [1], [3, 3], [2], [4, 4], [1], [3], [4]] Output [null, null, null, 1, null, -1, null, -1, 3, 4] Explanation LRUCache lRUCache = new LRUCache(2); lRUCache.put(1, 1); // cache is {1=1} lRUCache.put(2, 2); // cache is {1=1, 2=2} lRUCache.get(1); // return 1 lRUCache.put(3, 3); // LRU key was 2, evicts key 2, cache is {1=1, 3=3} lRUCache.get(2); // returns -1 (not found) lRUCache.put(4, 4); // LRU key was 1, evicts key 1, cache is {4=4, 3=3} lRUCache.get(1); // return -1 (not found) lRUCache.get(3); // return 3 lRUCache.get(4); // return 4 Constraints: 1 <= capacity <= 3000 0 <= key <= 104 0 <= value <= 105 At most 2 * 105 calls will be made to get and put.

Topics: Hash Table, Linked List, Design, Doubly-Linked List

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