Introduction

In this blog post, we'll share the most commonly asked coding interview questions at Datadog. 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: Most Common Word

Given a string paragraph and a string array of the banned words banned, return the most frequent word that is not banned. It is guaranteed there is at least one word that is not banned, and that the answer is unique. The words in paragraph are case-insensitive and the answer should be returned in lowercase. Example 1: Input: paragraph = "Bob hit a ball, the hit BALL flew far after it was hit.", banned = ["hit"] Output: "ball" Explanation: "hit" occurs 3 times, but it is a banned word. "ball" occurs twice (and no other word does), so it is the most frequent non-banned word in the paragraph. Note that words in the paragraph are not case sensitive, that punctuation is ignored (even if adjacent to words, such as "ball,"), and that "hit" isn't the answer even though it occurs more because it is banned. Example 2: Input: paragraph = "a.", banned = [] Output: "a" Constraints: 1 <= paragraph.length <= 1000 paragraph consists of English letters, space ' ', or one of the symbols: "!?',;.". 0 <= banned.length <= 100 1 <= banned[i].length <= 10 banned[i] consists of only lowercase English letters.

Topics: Array, Hash Table, String, Counting

Problem #3: Maximum Depth of N-ary Tree

Given a n-ary tree, find its maximum depth. The maximum depth is the number of nodes along the longest path from the root node down to the farthest leaf node. Nary-Tree input serialization is represented in their level order traversal, each group of children is separated by the null value (See examples). Example 1: Input: root = [1,null,3,2,4,null,5,6] Output: 3 Example 2: Input: root = [1,null,2,3,4,5,null,null,6,7,null,8,null,9,10,null,null,11,null,12,null,13,null,null,14] Output: 5 Constraints: The total number of nodes is in the range [0, 104]. The depth of the n-ary tree is less than or equal to 1000.

Topics: Tree, Depth-First Search, Breadth-First Search

Problem #5: Coin Change

You are given an integer array coins representing coins of different denominations and an integer amount representing a total amount of money. Return the fewest number of coins that you need to make up that amount. If that amount of money cannot be made up by any combination of the coins, return -1. You may assume that you have an infinite number of each kind of coin. Example 1: Input: coins = [1,2,5], amount = 11 Output: 3 Explanation: 11 = 5 + 5 + 1 Example 2: Input: coins = [2], amount = 3 Output: -1 Example 3: Input: coins = [1], amount = 0 Output: 0 Constraints: 1 <= coins.length <= 12 1 <= coins[i] <= 231 - 1 0 <= amount <= 104

Topics: Array, Dynamic Programming, Breadth-First Search

Problem #6: House Robber

You are a professional robber planning to rob houses along a street. Each house has a certain amount of money stashed, the only constraint stopping you from robbing each of them is that adjacent houses have security systems connected and it will automatically contact the police if two adjacent houses were broken into on the same night. Given an integer array nums representing the amount of money of each house, return the maximum amount of money you can rob tonight without alerting the police. Example 1: Input: nums = [1,2,3,1] Output: 4 Explanation: Rob house 1 (money = 1) and then rob house 3 (money = 3). Total amount you can rob = 1 + 3 = 4. Example 2: Input: nums = [2,7,9,3,1] Output: 12 Explanation: Rob house 1 (money = 2), rob house 3 (money = 9) and rob house 5 (money = 1). Total amount you can rob = 2 + 9 + 1 = 12. Constraints: 1 <= nums.length <= 100 0 <= nums[i] <= 400

Topics: Array, Dynamic Programming

Problem #7: Design Circular Queue

Design your implementation of the circular queue. The circular queue is a linear data structure in which the operations are performed based on FIFO (First In First Out) principle, and the last position is connected back to the first position to make a circle. It is also called "Ring Buffer". One of the benefits of the circular queue is that we can make use of the spaces in front of the queue. In a normal queue, once the queue becomes full, we cannot insert the next element even if there is a space in front of the queue. But using the circular queue, we can use the space to store new values. Implement the MyCircularQueue class: MyCircularQueue(k) Initializes the object with the size of the queue to be k. int Front() Gets the front item from the queue. If the queue is empty, return -1. int Rear() Gets the last item from the queue. If the queue is empty, return -1. boolean enQueue(int value) Inserts an element into the circular queue. Return true if the operation is successful. boolean deQueue() Deletes an element from the circular queue. Return true if the operation is successful. boolean isEmpty() Checks whether the circular queue is empty or not. boolean isFull() Checks whether the circular queue is full or not. You must solve the problem without using the built-in queue data structure in your programming language. Example 1: Input ["MyCircularQueue", "enQueue", "enQueue", "enQueue", "enQueue", "Rear", "isFull", "deQueue", "enQueue", "Rear"] [[3], [1], [2], [3], [4], [], [], [], [4], []] Output [null, true, true, true, false, 3, true, true, true, 4] Explanation MyCircularQueue myCircularQueue = new MyCircularQueue(3); myCircularQueue.enQueue(1); // return True myCircularQueue.enQueue(2); // return True myCircularQueue.enQueue(3); // return True myCircularQueue.enQueue(4); // return False myCircularQueue.Rear(); // return 3 myCircularQueue.isFull(); // return True myCircularQueue.deQueue(); // return True myCircularQueue.enQueue(4); // return True myCircularQueue.Rear(); // return 4 Constraints: 1 <= k <= 1000 0 <= value <= 1000 At most 3000 calls will be made to enQueue, deQueue, Front, Rear, isEmpty, and isFull.

Topics: Array, Linked List, Design, Queue

Problem #8: Design Add and Search Words Data Structure

Design a data structure that supports adding new words and finding if a string matches any previously added string. Implement the WordDictionary class: WordDictionary() Initializes the object. void addWord(word) Adds word to the data structure, it can be matched later. bool search(word) Returns true if there is any string in the data structure that matches word or false otherwise. word may contain dots '.' where dots can be matched with any letter. Example: Input ["WordDictionary","addWord","addWord","addWord","search","search","search","search"] [[],["bad"],["dad"],["mad"],["pad"],["bad"],[".ad"],["b.."]] Output [null,null,null,null,false,true,true,true] Explanation WordDictionary wordDictionary = new WordDictionary(); wordDictionary.addWord("bad"); wordDictionary.addWord("dad"); wordDictionary.addWord("mad"); wordDictionary.search("pad"); // return False wordDictionary.search("bad"); // return True wordDictionary.search(".ad"); // return True wordDictionary.search("b.."); // return True Constraints: 1 <= word.length <= 25 word in addWord consists of lowercase English letters. word in search consist of '.' or lowercase English letters. There will be at most 2 dots in word for search queries. At most 104 calls will be made to addWord and search.

Topics: String, Depth-First Search, Design, Trie

Problem #9: Sliding Window Median

The median is the middle value in an ordered integer list. If the size of the list is even, there is no middle value. So the median is the mean of the two middle values. For examples, if arr = [2,3,4], the median is 3. For examples, if arr = [1,2,3,4], the median is (2 + 3) / 2 = 2.5. You are given an integer array nums and an integer k. There is a sliding window of size k which is moving from the very left of the array to the very right. You can only see the k numbers in the window. Each time the sliding window moves right by one position. Return the median array for each window in the original array. Answers within 10-5 of the actual value will be accepted. Example 1: Input: nums = [1,3,-1,-3,5,3,6,7], k = 3 Output: [1.00000,-1.00000,-1.00000,3.00000,5.00000,6.00000] Explanation: Window position Median --------------- ----- [1 3 -1] -3 5 3 6 7 1 1 [3 -1 -3] 5 3 6 7 -1 1 3 [-1 -3 5] 3 6 7 -1 1 3 -1 [-3 5 3] 6 7 3 1 3 -1 -3 [5 3 6] 7 5 1 3 -1 -3 5 [3 6 7] 6 Example 2: Input: nums = [1,2,3,4,2,3,1,4,2], k = 3 Output: [2.00000,3.00000,3.00000,3.00000,2.00000,3.00000,2.00000] Constraints: 1 <= k <= nums.length <= 105 -231 <= nums[i] <= 231 - 1

Topics: Array, Hash Table, Sliding Window, Heap (Priority Queue)

Problem #10: People Whose List of Favorite Companies Is Not a Subset of Another List

Given the array favoriteCompanies where favoriteCompanies[i] is the list of favorites companies for the ith person (indexed from 0). Return the indices of people whose list of favorite companies is not a subset of any other list of favorites companies. You must return the indices in increasing order. Example 1: Input: favoriteCompanies = [["leetcode","google","facebook"],["google","microsoft"],["google","facebook"],["google"],["amazon"]] Output: [0,1,4] Explanation: Person with index=2 has favoriteCompanies[2]=["google","facebook"] which is a subset of favoriteCompanies[0]=["leetcode","google","facebook"] corresponding to the person with index 0. Person with index=3 has favoriteCompanies[3]=["google"] which is a subset of favoriteCompanies[0]=["leetcode","google","facebook"] and favoriteCompanies[1]=["google","microsoft"]. Other lists of favorite companies are not a subset of another list, therefore, the answer is [0,1,4]. Example 2: Input: favoriteCompanies = [["leetcode","google","facebook"],["leetcode","amazon"],["facebook","google"]] Output: [0,1] Explanation: In this case favoriteCompanies[2]=["facebook","google"] is a subset of favoriteCompanies[0]=["leetcode","google","facebook"], therefore, the answer is [0,1]. Example 3: Input: favoriteCompanies = [["leetcode"],["google"],["facebook"],["amazon"]] Output: [0,1,2,3] Constraints: 1 <= favoriteCompanies.length <= 100 1 <= favoriteCompanies[i].length <= 500 1 <= favoriteCompanies[i][j].length <= 20 All strings in favoriteCompanies[i] are distinct. All lists of favorite companies are distinct, that is, If we sort alphabetically each list then favoriteCompanies[i] != favoriteCompanies[j]. All strings consist of lowercase English letters only.

Topics: Array, Hash Table, String