Introduction

In this blog post, we'll share the most commonly asked coding interview questions at Nutanix. 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: Min Cost to Connect All Points

You are given an array points representing integer coordinates of some points on a 2D-plane, where points[i] = [xi, yi]. The cost of connecting two points [xi, yi] and [xj, yj] is the manhattan distance between them: |xi - xj| + |yi - yj|, where |val| denotes the absolute value of val. Return the minimum cost to make all points connected. All points are connected if there is exactly one simple path between any two points. Example 1: Input: points = [[0,0],[2,2],[3,10],[5,2],[7,0]] Output: 20 Explanation: We can connect the points as shown above to get the minimum cost of 20. Notice that there is a unique path between every pair of points. Example 2: Input: points = [[3,12],[-2,5],[-4,1]] Output: 18 Constraints: 1 <= points.length <= 1000 -106 <= xi, yi <= 106 All pairs (xi, yi) are distinct.

Topics: Array, Union Find, Graph, Minimum Spanning Tree

Problem #2: Binary Tree Zigzag Level Order Traversal

Given the root of a binary tree, return the zigzag level order traversal of its nodes' values. (i.e., from left to right, then right to left for the next level and alternate between). Example 1: Input: root = [3,9,20,null,null,15,7] Output: [[3],[20,9],[15,7]] Example 2: Input: root = [1] Output: [[1]] Example 3: Input: root = [] Output: [] Constraints: The number of nodes in the tree is in the range [0, 2000]. -100 <= Node.val <= 100

Topics: Tree, Breadth-First Search, Binary Tree

Problem #3: Group Anagrams

Given an array of strings strs, group the anagrams together. You can return the answer in any order. Example 1: Input: strs = ["eat","tea","tan","ate","nat","bat"] Output: [["bat"],["nat","tan"],["ate","eat","tea"]] Explanation: There is no string in strs that can be rearranged to form "bat". The strings "nat" and "tan" are anagrams as they can be rearranged to form each other. The strings "ate", "eat", and "tea" are anagrams as they can be rearranged to form each other. Example 2: Input: strs = [""] Output: [[""]] Example 3: Input: strs = ["a"] Output: [["a"]] Constraints: 1 <= strs.length <= 104 0 <= strs[i].length <= 100 strs[i] consists of lowercase English letters.

Topics: Array, Hash Table, String, Sorting

Problem #5: Top K Frequent Elements

Given an integer array nums and an integer k, return the k most frequent elements. You may return the answer in any order. Example 1: Input: nums = [1,1,1,2,2,3], k = 2 Output: [1,2] Example 2: Input: nums = [1], k = 1 Output: [1] Constraints: 1 <= nums.length <= 105 -104 <= nums[i] <= 104 k is in the range [1, the number of unique elements in the array]. It is guaranteed that the answer is unique. Follow up: Your algorithm's time complexity must be better than O(n log n), where n is the array's size.

Topics: Array, Hash Table, Divide and Conquer, Sorting, Heap (Priority Queue), Bucket Sort, Counting, Quickselect

Problem #6: 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 #7: 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 #8: Spiral Matrix

Given an m x n matrix, return all elements of the matrix in spiral order. Example 1: Input: matrix = [[1,2,3],[4,5,6],[7,8,9]] Output: [1,2,3,6,9,8,7,4,5] Example 2: Input: matrix = [[1,2,3,4],[5,6,7,8],[9,10,11,12]] Output: [1,2,3,4,8,12,11,10,9,5,6,7] Constraints: m == matrix.length n == matrix[i].length 1 <= m, n <= 10 -100 <= matrix[i][j] <= 100

Topics: Array, Matrix, Simulation

Problem #9: Binary Tree Maximum Path Sum

A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root. The path sum of a path is the sum of the node's values in the path. Given the root of a binary tree, return the maximum path sum of any non-empty path. Example 1: Input: root = [1,2,3] Output: 6 Explanation: The optimal path is 2 -> 1 -> 3 with a path sum of 2 + 1 + 3 = 6. Example 2: Input: root = [-10,9,20,null,null,15,7] Output: 42 Explanation: The optimal path is 15 -> 20 -> 7 with a path sum of 15 + 20 + 7 = 42. Constraints: The number of nodes in the tree is in the range [1, 3 * 104]. -1000 <= Node.val <= 1000

Topics: Dynamic Programming, Tree, Depth-First Search, Binary Tree

Problem #10: Maximum Frequency Stack

Design a stack-like data structure to push elements to the stack and pop the most frequent element from the stack. Implement the FreqStack class: FreqStack() constructs an empty frequency stack. void push(int val) pushes an integer val onto the top of the stack. int pop() removes and returns the most frequent element in the stack. If there is a tie for the most frequent element, the element closest to the stack's top is removed and returned. Example 1: Input ["FreqStack", "push", "push", "push", "push", "push", "push", "pop", "pop", "pop", "pop"] [[], [5], [7], [5], [7], [4], [5], [], [], [], []] Output [null, null, null, null, null, null, null, 5, 7, 5, 4] Explanation FreqStack freqStack = new FreqStack(); freqStack.push(5); // The stack is [5] freqStack.push(7); // The stack is [5,7] freqStack.push(5); // The stack is [5,7,5] freqStack.push(7); // The stack is [5,7,5,7] freqStack.push(4); // The stack is [5,7,5,7,4] freqStack.push(5); // The stack is [5,7,5,7,4,5] freqStack.pop(); // return 5, as 5 is the most frequent. The stack becomes [5,7,5,7,4]. freqStack.pop(); // return 7, as 5 and 7 is the most frequent, but 7 is closest to the top. The stack becomes [5,7,5,4]. freqStack.pop(); // return 5, as 5 is the most frequent. The stack becomes [5,7,4]. freqStack.pop(); // return 4, as 4, 5 and 7 is the most frequent, but 4 is closest to the top. The stack becomes [5,7]. Constraints: 0 <= val <= 109 At most 2 * 104 calls will be made to push and pop. It is guaranteed that there will be at least one element in the stack before calling pop.

Topics: Hash Table, Stack, Design, Ordered Set