Introduction

In this blog post, we'll share the most commonly asked coding interview questions at Target. 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: Longest Path With Different Adjacent Characters

You are given a tree (i.e. a connected, undirected graph that has no cycles) rooted at node 0 consisting of n nodes numbered from 0 to n - 1. The tree is represented by a 0-indexed array parent of size n, where parent[i] is the parent of node i. Since node 0 is the root, parent[0] == -1. You are also given a string s of length n, where s[i] is the character assigned to node i. Return the length of the longest path in the tree such that no pair of adjacent nodes on the path have the same character assigned to them. Example 1: Input: parent = [-1,0,0,1,1,2], s = "abacbe" Output: 3 Explanation: The longest path where each two adjacent nodes have different characters in the tree is the path: 0 -> 1 -> 3. The length of this path is 3, so 3 is returned. It can be proven that there is no longer path that satisfies the conditions. Example 2: Input: parent = [-1,0,0,0], s = "aabc" Output: 3 Explanation: The longest path where each two adjacent nodes have different characters is the path: 2 -> 0 -> 3. The length of this path is 3, so 3 is returned. Constraints: n == parent.length == s.length 1 <= n <= 105 0 <= parent[i] <= n - 1 for all i >= 1 parent[0] == -1 parent represents a valid tree. s consists of only lowercase English letters.

Topics: Array, String, Tree, Depth-First Search, Graph, Topological Sort

Problem #2: Product of the Last K Numbers

Design an algorithm that accepts a stream of integers and retrieves the product of the last k integers of the stream. Implement the ProductOfNumbers class: ProductOfNumbers() Initializes the object with an empty stream. void add(int num) Appends the integer num to the stream. int getProduct(int k) Returns the product of the last k numbers in the current list. You can assume that always the current list has at least k numbers. The test cases are generated so that, at any time, the product of any contiguous sequence of numbers will fit into a single 32-bit integer without overflowing. Example: Input ["ProductOfNumbers","add","add","add","add","add","getProduct","getProduct","getProduct","add","getProduct"] [[],[3],[0],[2],[5],[4],[2],[3],[4],[8],[2]] Output [null,null,null,null,null,null,20,40,0,null,32] Explanation ProductOfNumbers productOfNumbers = new ProductOfNumbers(); productOfNumbers.add(3); // [3] productOfNumbers.add(0); // [3,0] productOfNumbers.add(2); // [3,0,2] productOfNumbers.add(5); // [3,0,2,5] productOfNumbers.add(4); // [3,0,2,5,4] productOfNumbers.getProduct(2); // return 20. The product of the last 2 numbers is 5 4 = 20 productOfNumbers.getProduct(3); // return 40. The product of the last 3 numbers is 2 5 4 = 40 productOfNumbers.getProduct(4); // return 0. The product of the last 4 numbers is 0 2 5 4 = 0 productOfNumbers.add(8); // [3,0,2,5,4,8] productOfNumbers.getProduct(2); // return 32. The product of the last 2 numbers is 4 8 = 32 Constraints: 0 <= num <= 100 1 <= k <= 4 104 At most 4 * 104 calls will be made to add and getProduct. The product of the stream at any point in time will fit in a 32-bit integer. Follow-up: Can you implement both GetProduct and Add to work in O(1) time complexity instead of O(k) time complexity?

Topics: Array, Math, Design, Data Stream, Prefix Sum

Problem #3: Maximum Subarray

Given an integer array nums, find the subarray with the largest sum, and return its sum. Example 1: Input: nums = [-2,1,-3,4,-1,2,1,-5,4] Output: 6 Explanation: The subarray [4,-1,2,1] has the largest sum 6. Example 2: Input: nums = [1] Output: 1 Explanation: The subarray [1] has the largest sum 1. Example 3: Input: nums = [5,4,-1,7,8] Output: 23 Explanation: The subarray [5,4,-1,7,8] has the largest sum 23. Constraints: 1 <= nums.length <= 105 -104 <= nums[i] <= 104 Follow up: If you have figured out the O(n) solution, try coding another solution using the divide and conquer approach, which is more subtle.

Topics: Array, Divide and Conquer, Dynamic Programming