Introduction

In this blog post, we'll share the most commonly asked coding interview questions at Netflix. 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: Reconstruct Itinerary

You are given a list of airline tickets where tickets[i] = [fromi, toi] represent the departure and the arrival airports of one flight. Reconstruct the itinerary in order and return it. All of the tickets belong to a man who departs from "JFK", thus, the itinerary must begin with "JFK". If there are multiple valid itineraries, you should return the itinerary that has the smallest lexical order when read as a single string. For example, the itinerary ["JFK", "LGA"] has a smaller lexical order than ["JFK", "LGB"]. You may assume all tickets form at least one valid itinerary. You must use all the tickets once and only once. Example 1: Input: tickets = [["MUC","LHR"],["JFK","MUC"],["SFO","SJC"],["LHR","SFO"]] Output: ["JFK","MUC","LHR","SFO","SJC"] Example 2: Input: tickets = [["JFK","SFO"],["JFK","ATL"],["SFO","ATL"],["ATL","JFK"],["ATL","SFO"]] Output: ["JFK","ATL","JFK","SFO","ATL","SFO"] Explanation: Another possible reconstruction is ["JFK","SFO","ATL","JFK","ATL","SFO"] but it is larger in lexical order. Constraints: 1 <= tickets.length <= 300 tickets[i].length == 2 fromi.length == 3 toi.length == 3 fromi and toi consist of uppercase English letters. fromi != toi

Topics: Depth-First Search, Graph, Eulerian Circuit

Problem #3: Merge Intervals

Given an array of intervals where intervals[i] = [starti, endi], merge all overlapping intervals, and return an array of the non-overlapping intervals that cover all the intervals in the input. Example 1: Input: intervals = [[1,3],[2,6],[8,10],[15,18]] Output: [[1,6],[8,10],[15,18]] Explanation: Since intervals [1,3] and [2,6] overlap, merge them into [1,6]. Example 2: Input: intervals = [[1,4],[4,5]] Output: [[1,5]] Explanation: Intervals [1,4] and [4,5] are considered overlapping. Constraints: 1 <= intervals.length <= 104 intervals[i].length == 2 0 <= starti <= endi <= 104

Topics: Array, Sorting

Problem #5: Summary Ranges

You are given a sorted unique integer array nums. A range [a,b] is the set of all integers from a to b (inclusive). Return the smallest sorted list of ranges that cover all the numbers in the array exactly. That is, each element of nums is covered by exactly one of the ranges, and there is no integer x such that x is in one of the ranges but not in nums. Each range [a,b] in the list should be output as: "a->b" if a != b "a" if a == b Example 1: Input: nums = [0,1,2,4,5,7] Output: ["0->2","4->5","7"] Explanation: The ranges are: [0,2] --> "0->2" [4,5] --> "4->5" [7,7] --> "7" Example 2: Input: nums = [0,2,3,4,6,8,9] Output: ["0","2->4","6","8->9"] Explanation: The ranges are: [0,0] --> "0" [2,4] --> "2->4" [6,6] --> "6" [8,9] --> "8->9" Constraints: 0 <= nums.length <= 20 -231 <= nums[i] <= 231 - 1 All the values of nums are unique. nums is sorted in ascending order.

Topics: Array

Problem #6: First Missing Positive

Given an unsorted integer array nums. Return the smallest positive integer that is not present in nums. You must implement an algorithm that runs in O(n) time and uses O(1) auxiliary space. Example 1: Input: nums = [1,2,0] Output: 3 Explanation: The numbers in the range [1,2] are all in the array. Example 2: Input: nums = [3,4,-1,1] Output: 2 Explanation: 1 is in the array but 2 is missing. Example 3: Input: nums = [7,8,9,11,12] Output: 1 Explanation: The smallest positive integer 1 is missing. Constraints: 1 <= nums.length <= 105 -231 <= nums[i] <= 231 - 1

Topics: Array, Hash Table

Problem #7: Word Search

Given an m x n grid of characters board and a string word, return true if word exists in the grid. The word can be constructed from letters of sequentially adjacent cells, where adjacent cells are horizontally or vertically neighboring. The same letter cell may not be used more than once. Example 1: Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "ABCCED" Output: true Example 2: Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "SEE" Output: true Example 3: Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "ABCB" Output: false Constraints: m == board.length n = board[i].length 1 <= m, n <= 6 1 <= word.length <= 15 board and word consists of only lowercase and uppercase English letters. Follow up: Could you use search pruning to make your solution faster with a larger board?

Topics: Array, String, Backtracking, Depth-First Search, Matrix

Problem #8: 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

Problem #9: Cache With Time Limit

Write a class that allows getting and setting key-value pairs, however a time until expiration is associated with each key. The class has three public methods: set(key, value, duration): accepts an integer key, an integer value, and a duration in milliseconds. Once the duration has elapsed, the key should be inaccessible. The method should return true if the same un-expired key already exists and false otherwise. Both the value and duration should be overwritten if the key already exists. get(key): if an un-expired key exists, it should return the associated value. Otherwise it should return -1. count(): returns the count of un-expired keys. Example 1: Input: actions = ["TimeLimitedCache", "set", "get", "count", "get"] values = [[], [1, 42, 100], [1], [], [1]] timeDelays = [0, 0, 50, 50, 150] Output: [null, false, 42, 1, -1] Explanation: At t=0, the cache is constructed. At t=0, a key-value pair (1: 42) is added with a time limit of 100ms. The value doesn't exist so false is returned. At t=50, key=1 is requested and the value of 42 is returned. At t=50, count() is called and there is one active key in the cache. At t=100, key=1 expires. At t=150, get(1) is called but -1 is returned because the cache is empty. Example 2: Input: actions = ["TimeLimitedCache", "set", "set", "get", "get", "get", "count"] values = [[], [1, 42, 50], [1, 50, 100], [1], [1], [1], []] timeDelays = [0, 0, 40, 50, 120, 200, 250] Output: [null, false, true, 50, 50, -1, 0] Explanation: At t=0, the cache is constructed. At t=0, a key-value pair (1: 42) is added with a time limit of 50ms. The value doesn't exist so false is returned. At t=40, a key-value pair (1: 50) is added with a time limit of 100ms. A non-expired value already existed so true is returned and the old value was overwritten. At t=50, get(1) is called which returned 50. At t=120, get(1) is called which returned 50. At t=140, key=1 expires. At t=200, get(1) is called but the cache is empty so -1 is returned. At t=250, count() returns 0 because the cache is empty. Constraints: 0 <= key, value <= 109 0 <= duration <= 1000 1 <= actions.length <= 100 actions.length === values.length actions.length === timeDelays.length 0 <= timeDelays[i] <= 1450 actions[i] is one of "TimeLimitedCache", "set", "get" and "count" First action is always "TimeLimitedCache" and must be executed immediately, with a 0-millisecond delay

Topics: nan

Problem #10: Word Break

Given a string s and a dictionary of strings wordDict, return true if s can be segmented into a space-separated sequence of one or more dictionary words. Note that the same word in the dictionary may be reused multiple times in the segmentation. Example 1: Input: s = "leetcode", wordDict = ["leet","code"] Output: true Explanation: Return true because "leetcode" can be segmented as "leet code". Example 2: Input: s = "applepenapple", wordDict = ["apple","pen"] Output: true Explanation: Return true because "applepenapple" can be segmented as "apple pen apple". Note that you are allowed to reuse a dictionary word. Example 3: Input: s = "catsandog", wordDict = ["cats","dog","sand","and","cat"] Output: false Constraints: 1 <= s.length <= 300 1 <= wordDict.length <= 1000 1 <= wordDict[i].length <= 20 s and wordDict[i] consist of only lowercase English letters. All the strings of wordDict are unique.

Topics: Array, Hash Table, String, Dynamic Programming, Trie, Memoization