Data Structures & Algorithms Interview Questions

Alok S. - "I’d clarify the scope first. I’ll assume they want: Given a root folder and a search text, recursively find all files whose filename contains that text. Code: #include #include #include #include using namespace std; namespace fs = std::filesystem; vector searchFiles(const string& rootPath, const string& target) { vector ans; if(!fs::exists(rootPath)) { return ans; } // recursively go through all folder"See full answer

叶 路. - "from collections import deque def updateword(words, startword, end_word): if end_word not in words: return None # Early exit if end_word is not in the dictionary queue = deque([(start_word, 0)]) # (word, steps) visited = set([start_word]) # Keep track of visited words while queue: word, steps = queue.popleft() if word == end_word: return steps # Found the target word, return steps for i in range(len(word)): "See full answer

Dev S. - "#include #include bool palindrome(std::string &str, int left, int right, int error) { if (left >= right) { return true; } if (str[left] == str[right]) { return palindrome(str, left + 1, right - 1, error); } else if (error == 0) { return (palindrome(str, left + 1, right, 1) || palindrome(str,left, right -1,1)); } else { return false; } } int main() { std::string str = "abcbca"; int size = str.size() - 1; if"See full answer

Yeshwanth D. - "Was this for an entry level engineer role?"See full answer

Anonymous Roadrunner - "def is_palindrome(s: str) -> bool: new = '' for a in s: if a.isalpha() or a.isdigit(): new += a.lower() return (new == new[::-1]) debug your code below print(is_palindrome('abcba')) `"See full answer

Alfred O. - "We can use dictionary to store cache items so that our read / write operations will be O(1). Each time we read or update an existing record, we have to ensure the item is moved to the back of the cache. This will allow us to evict the first item in the cache whenever the cache is full and we need to add new records also making our eviction O(1) Instead of normal dictionary, we will use ordered dictionary to store cache items. This will allow us to efficiently move items to back of the cache a"See full answer

Rick E. - " O(n) - characters in the string O(n) - stack def identify_adjacent(s: str, k: int) -> str: stack = [] n = len(s) for ch in s: if stack: topch, topcnt = stack[-1] if top_ch == ch: top_cnt += 1 stack[-1] = (ch, top_cnt) else: top_cnt = 1 stack.append((ch,1)) if top_cnt == k: stack.pop() else: stack.append"See full answer

Richard W. - "int getMaxWater(vector& nums) { int n = nums.size(); int mx = INT_MIN; int i=0, j=n-1; while(i<j) { int water = (j - i) * min(nums[i], nums[j]); mx = max(mx, water); if(nums[i] < nums[j]){ i++; } else { j--; } } return mx; } `"See full answer

Jatin S. - "public int minInsertions(String s) { int n = s.length(); int dp = new intn; for (int len = 2; len <= n; n++) { for (int i = 0; i + len - 1 < n: i++) { int j = i + len - 1; if (s.charAt(i) == s.charAt(j)) { dpi = dpi + 1; } else { dpi = 1 + Math.min(dpi + 1, dpi); } } } return dp0; } `"See full answer

Ashish D. - "This is a geometric variation of the "Sliding Window" technique. The core challenge is converting a 2D coordinate problem into a 1D linear problem using angles. The standard approach is the Angular Sweep. By converting the Cartesian coordinates (x,y) of every tree into Polar coordinates (specifically the angle θ), we can process the trees based on the direction they lie in relative to the observer. The Strategy Coordinate Translation: Calculate the relative coordinates (Δx,Δy) for"See full answer

Kishor J. - "we can use two pointer + set like maintain i,j and also insert jth character to set like while set size is equal to our window j-i+1 then maximize our answer and increase jth pointer till last index"See full answer

Anni P. - "I firstly discuss the brute force approach in O(n^2) time complexity , than i moved to O(nlogn) tine complexity than i discussed the O(n) time complexity and O(n) space complexity . But interviewer want more optimised solution , in O(n) time complexity without using extra space , The solution wants O(1) space complexity i have to do changes in same array without using any space . This method is something like i have to place positive values to its original position by swapping and rest negativ"See full answer

Maykon henrique D. - "function longestCommonPrefix(arr1, arr2) { const prefixSet = new Set(); for (let num of arr1) { let str = num.toString(); for (let i = 1; i <= str.length; i++) { prefixSet.add(str.substring(0, i)); } } let longestPrefix = ""; for (let num of arr2) { let str = num.toString(); for (let i = 1; i <= str.length; i++) { let prefix = str.substring(0, i); if (prefixSet.has(prefix)) { "See full answer

Srikant V. - "Used Recursive approach to traverse the binary search tree and sum the values of the nodes that fall within the specified range [low, high]"See full answer

Abinash S. - " def hasgoodsubarray(nums, k): if not nums: return False if k == 0: for i in range(len(nums)): if nums[i] == 0 and nums[i + 1] == 0: return True return False map = {0:-1} sum = 0 for i,val in enumerate(nums): sum += val rem = sum % k if rem in map: if i - map[rem] >= 2: return True else: map[rem] = i return False print(hasgoods"See full answer

Laxman kishore K. - "Try not to take hints from the interviewer for solving the problem. They may provide hints but it would impact the final decision "See full answer

VContaineers - " Compare alternate houses i.e for each house starting from the third, calculate the maximum money that can be stolen up to that house by choosing between: Skipping the current house and taking the maximum money stolen up to the previous house. Robbing the current house and adding its value to the maximum money stolen up to the house two steps back. package main import ( "fmt" ) // rob function calculates the maximum money a robber can steal func maxRob(nums []int) int { ln"See full answer

Chinmay S. - "#include #include #include using namespace std; vector diff(const vector& A, const vector& B) { unordered_set elements; vector result; for (const auto& element : A) { elements.insert(element); } for (const auto& element : B) { if (elements.find(element) == elements.end()) { result.push_back(element); } else { elements.erase(element); } } for"See full answer

Karan K. - "2 Approaches: 1) The more intuitive approach is doing a multi-source BFS from all cats and storing the distance of closest cats. Then do a dfs/bfs from rat to bread. Time Complexity: O(mn + 4^L) where L is path length, worst case L could be mn Space Complexity: O(m*n) 2) The first approach should be fine for interviews. But if they ask to optimize it further, you can use Binary Search. Problems like "Finding max of min distance" or "Finding min of max" could be usually solved by BS. "See full answer