Introduction

In this blog post, we'll share the most commonly asked coding interview questions at WorldQuant. 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: Minimum Number of Visited Cells in a Grid

You are given a 0-indexed m x n integer matrix grid. Your initial position is at the top-left cell (0, 0). Starting from the cell (i, j), you can move to one of the following cells: Cells (i, k) with j < k <= grid[i][j] + j (rightward movement), or Cells (k, j) with i < k <= grid[i][j] + i (downward movement). Return the minimum number of cells you need to visit to reach the bottom-right cell (m - 1, n - 1). If there is no valid path, return -1. Example 1: Input: grid = [[3,4,2,1],[4,2,3,1],[2,1,0,0],[2,4,0,0]] Output: 4 Explanation: The image above shows one of the paths that visits exactly 4 cells. Example 2: Input: grid = [[3,4,2,1],[4,2,1,1],[2,1,1,0],[3,4,1,0]] Output: 3 Explanation: The image above shows one of the paths that visits exactly 3 cells. Example 3: Input: grid = [[2,1,0],[1,0,0]] Output: -1 Explanation: It can be proven that no path exists. Constraints: m == grid.length n == grid[i].length 1 <= m, n <= 105 1 <= m n <= 105 0 <= grid[i][j] < m n grid[m - 1][n - 1] == 0

Topics: Array, Dynamic Programming, Stack, Breadth-First Search, Union Find, Heap (Priority Queue), Matrix, Monotonic Stack

Problem #2: Find Median from Data Stream

The median is the middle value in an ordered integer list. If the size of the list is even, there is no middle value, and the median is the mean of the two middle values. For example, for arr = [2,3,4], the median is 3. For example, for arr = [2,3], the median is (2 + 3) / 2 = 2.5. Implement the MedianFinder class: MedianFinder() initializes the MedianFinder object. void addNum(int num) adds the integer num from the data stream to the data structure. double findMedian() returns the median of all elements so far. Answers within 10-5 of the actual answer will be accepted. Example 1: Input ["MedianFinder", "addNum", "addNum", "findMedian", "addNum", "findMedian"] [[], [1], [2], [], [3], []] Output [null, null, null, 1.5, null, 2.0] Explanation MedianFinder medianFinder = new MedianFinder(); medianFinder.addNum(1); // arr = [1] medianFinder.addNum(2); // arr = [1, 2] medianFinder.findMedian(); // return 1.5 (i.e., (1 + 2) / 2) medianFinder.addNum(3); // arr[1, 2, 3] medianFinder.findMedian(); // return 2.0 Constraints: -105 <= num <= 105 There will be at least one element in the data structure before calling findMedian. At most 5 * 104 calls will be made to addNum and findMedian. Follow up: If all integer numbers from the stream are in the range [0, 100], how would you optimize your solution? If 99% of all integer numbers from the stream are in the range [0, 100], how would you optimize your solution?

Topics: Two Pointers, Design, Sorting, Heap (Priority Queue), Data Stream