Navigating the landscape of technical interviews, especially for top-tier tech companies, often involves a rigorous assessment of your problem-solving abilities and foundational computer science knowledge. LeetCode questions have become a standard benchmark in this process, challenging candidates to demonstrate their proficiency in data structures, algorithms, and efficient coding. Mastering these types of problems is not just about memorizing solutions, but about developing a robust problem-solving mindset that allows you to break down complex challenges into manageable steps. This comprehensive guide will equip you with insights into why these questions are crucial, what they entail, and provide a curated list of 30 common LeetCode questions to help you prepare effectively for your next technical interview.
What Are LeetCode Questions?
LeetCode questions are programming challenges designed to test a candidate's understanding of data structures, algorithms, and general problem-solving skills. They typically involve a well-defined input, an expected output, and constraints on time and space complexity. These questions range in difficulty from "Easy" to "Hard" and cover a wide array of topics, including arrays, strings, linked lists, trees, graphs, dynamic programming, sorting, searching, and more. The platform provides an environment to write and test code against various test cases, offering immediate feedback on correctness and efficiency. Success in these problems often requires not just a correct solution, but an optimal one.
Why Do Interviewers Ask LeetCode Questions?
Interviewers ask LeetCode questions for several key reasons, primarily to assess a candidate's core technical competencies. Firstly, they evaluate problem-solving abilities: how you approach an unfamiliar problem, break it down, and develop an algorithmic solution. Secondly, these questions gauge your coding proficiency, including your ability to write clean, maintainable, and bug-free code under pressure. Thirdly, they test your fundamental understanding of data structures and algorithms, which are the building blocks of efficient software. Finally, LeetCode-style questions offer insights into how candidates handle constraints, optimize solutions for performance, and articulate their thought process, all crucial skills for a successful software engineering role.
Preview List
Two Sum
Valid Parentheses
Merge Two Sorted Lists
Best Time to Buy and Sell Stock
Reverse Linked List
Palindrome Number
Valid Anagram
Contains Duplicate
Fibonacci Number
Climbing Stairs
Longest Substring Without Repeating Characters
3Sum
Group Anagrams
Product of Array Except Self
Number of Islands
Validate Binary Search Tree
Kth Smallest Element in a BST
Word Break
Jump Game
Unique Paths
Coin Change
Spiral Matrix
Subsets
Pacific Atlantic Water Flow
Rotting Oranges
Course Schedule
Design HashMap
Merge k Sorted Lists
Binary Tree Maximum Path Sum
LRU Cache
1. Two Sum
Why you might get asked this:
To assess basic array manipulation, hash map usage, and ability to optimize for time complexity. Tests foundational problem-solving skills quickly.
How to answer:
Use a hash map to store numbers and their indices. Iterate once, for each number calculate the complement needed. Check if complement is in map; if so, return indices.
Example answer:
Iterate through the array. For each number, calculate its complement (target - current_num). Check if the complement exists in a hash map. If yes, return the current index and the index stored for the complement. Otherwise, add the current number and its index to the map.
2. Valid Parentheses
Why you might get asked this:
Checks understanding of stacks and their Last-In, First-Out (LIFO) property. Essential for parsing expressions and structured data.
How to answer:
Use a stack. Push opening brackets onto the stack. When a closing bracket appears, check if the stack top matches its corresponding opening bracket. Pop if matched, else invalid.
Example answer:
Initialize an empty stack. Iterate through the string. If an opening bracket is encountered, push it onto the stack. If a closing bracket is encountered, check if the stack is empty or its top element is not the corresponding opening bracket. If valid, pop from the stack. Finally, return true if the stack is empty.
3. Merge Two Sorted Lists
Why you might get asked this:
Tests linked list manipulation, iterative or recursive approaches, and handling edge cases with pointers.
How to answer:
Create a dummy head node. Iterate through both lists, appending the smaller current node to the merged list, advancing its pointer. Append remaining nodes from the non-empty list.
Example answer:
Create a new dummy node to serve as the head of the merged list. Use a current pointer to build the merged list. Compare nodes from both input lists; append the smaller node to the current pointer's next and advance that list's pointer. After one list is exhausted, append the remainder of the other. Return dummy's next.
4. Best Time to Buy and Sell Stock
Why you might get asked this:
A common dynamic programming or greedy algorithm problem that tests finding maximum profit under constraints.
How to answer:
Keep track of the minimum price seen so far and the maximum profit obtained. Iterate through prices, updating minimum price and calculating potential profit with current price.
Example answer:
Initialize minprice to infinity and maxprofit to zero. Iterate through the prices array. For each price, update minprice = min(minprice, price). Then, update maxprofit = max(maxprofit, price - minprice). Return maxprofit.
5. Reverse Linked List
Why you might get asked this:
A fundamental linked list manipulation problem that assesses pointer handling and iterative/recursive thinking.
How to answer:
Iteratively, keep track of prev, curr, and next pointers. For each node, change curr.next to prev, then advance prev and curr.
Example answer:
Initialize prev to None and curr to the head. In a loop, store curr.next in a temporary nextnode. Then, set curr.next = prev. Update prev = curr and curr = nextnode. Repeat until curr is None. prev will be the new head.
6. Palindrome Number
Why you might get asked this:
Tests logical reasoning and number manipulation without converting to a string, focusing on arithmetic operations.
How to answer:
Handle negative numbers and numbers ending in 0 (except 0 itself) as false. Reverse half of the number and compare it with the first half.
Example answer:
First, handle edge cases: negative numbers or numbers ending in zero (unless the number itself is zero) are not palindromes. Create a revertedhalf variable. In a loop, extract the last digit of the number and append it to revertedhalf, while removing it from the original number. Stop when originalnumber is less than or equal to revertedhalf. Compare originalnumber with revertedhalf (or reverted_half / 10 for odd-digit numbers).
7. Valid Anagram
Why you might get asked this:
Checks understanding of character frequency counting, hash maps, or sorting for string comparison.
How to answer:
Count character frequencies for both strings using hash maps or arrays. Compare the frequency counts. Alternatively, sort both strings and compare them directly.
Example answer:
Create two frequency maps (or arrays of size 26 for lowercase English letters). Iterate through the first string s to populate its frequency map. Iterate through the second string t to decrement counts in the map. If any count goes below zero or is not zero at the end, they are not anagrams.
8. Contains Duplicate
Why you might get asked this:
A basic problem testing array traversal and efficient lookup using hash sets or sorting.
How to answer:
Use a hash set to store seen numbers. Iterate through the array; if a number is already in the set, return true. Otherwise, add it.
Example answer:
Initialize an empty hash set. Iterate through each num in the input nums array. If num is already present in the hash set, return True immediately. Otherwise, add num to the hash set. If the loop completes without finding duplicates, return False.
9. Fibonacci Number
Why you might get asked this:
A classic dynamic programming or recursive problem, testing basic recursion, memoization, or iterative optimization.
How to answer:
The simplest iterative solution involves storing the previous two Fibonacci numbers and calculating the next one in a loop. Recursion with memoization is also an option.
Example answer:
For n <= 1, return n. For larger n, use dynamic programming. Initialize a = 0, b = 1. Loop from 2 to n. In each iteration, calculate c = a + b, then update a = b and b = c. Return b after the loop.
10. Climbing Stairs
Why you might get asked this:
Another dynamic programming classic, similar to Fibonacci, testing the ability to identify overlapping subproblems.
How to answer:
This problem maps directly to the Fibonacci sequence where dp[i] is the number of ways to reach stair i. dp[i] = dp[i-1] + dp[i-2].
Example answer:
If n is 1 or 2, return n. Otherwise, initialize dp[1] = 1 and dp[2] = 2. Iterate from i = 3 to n, calculating dp[i] = dp[i-1] + dp[i-2]. Return dp[n]. This can be optimized to use only two variables instead of a full DP array.
11. Longest Substring Without Repeating Characters
Why you might get asked this:
Tests string manipulation, sliding window technique, and efficient character lookup using hash sets or maps.
How to answer:
Use a sliding window (two pointers, left and right) and a hash set. Expand the right pointer. If a character is duplicated, shrink left until no duplicates. Update max length.
Example answer:
Initialize maxlen = 0, left = 0, and a charset (hash set). Iterate right from 0 to string length. While s[right] is in charset, remove s[left] from charset and increment left. Add s[right] to charset. Update maxlen = max(max_len, right - left + 1).
12. 3Sum
Why you might get asked this:
A medium-difficulty problem testing array manipulation, sorting, and the two-pointer technique for finding triplets that sum to zero.
How to answer:
Sort the array. Iterate with one pointer i. Use two pointers (left and right) for the rest of the array. Adjust left and right based on the sum, handling duplicates.
Example answer:
Sort the input array nums. Iterate i from 0 to n-2. Skip duplicate nums[i]. Set left = i+1 and right = n-1. While left < right: calculate currentsum = nums[i] + nums[left] + nums[right]. If currentsum == 0, add triplet to result and advance left/right skipping duplicates. If current_sum < 0, increment left. Else, decrement right.
13. Group Anagrams
Why you might get asked this:
Tests string manipulation, hash map usage for grouping, and understanding of canonical forms (like sorted strings).
How to answer:
For each string, create its canonical form (e.g., sort the string). Use this canonical form as a key in a hash map, and append the original string to the list of values.
Example answer:
Initialize an empty hash map where keys are sorted strings (tuples of characters or string representations) and values are lists of original strings. Iterate through each string in the input list. Sort each string to get its canonical form. Use this canonical form as the key to append the original string to the corresponding list in the hash map. Return all values from the hash map.
14. Product of Array Except Self
Why you might get asked this:
Tests array manipulation and the ability to compute products efficiently without division, often using prefix/suffix product arrays.
How to answer:
Create an array for prefix products and another for suffix products. The result for an index is prefix[i-1] * suffix[i+1]. Can be optimized to O(1) space.
Example answer:
Initialize an answer array of the same size as nums. First, calculate prefix products: answer[i] stores the product of all elements to the left of i. Then, calculate suffix products: iterate backward, multiplying answer[i] by the product of all elements to the right of i. Use a right_product variable for the suffix pass.
15. Number of Islands
Why you might get asked this:
A classic graph traversal (BFS/DFS) problem on a 2D grid, testing connectivity and exploration.
How to answer:
Iterate through the grid. If '1' is found, increment island count and start DFS/BFS from that cell to mark all connected '1's as '0' (visited).
Example answer:
Initialize numislands = 0. Iterate through each cell (r, c) of the grid. If grid[r][c] == '1', increment numislands and start a BFS or DFS from (r, c). During traversal, mark all visited '1's as '0' to prevent recounting. BFS uses a queue, DFS uses recursion or a stack.
16. Validate Binary Search Tree
Why you might get asked this:
Tests understanding of BST properties (left < root < right) and common tree traversal techniques (in-order, recursion).
How to answer:
Use an in-order traversal and check if nodes are visited in strictly increasing order. Or, use recursion with min and max bounds for node values.
Example answer:
Implement a recursive helper function isValidBST(node, minval, maxval). For each node, check if node.val is within (minval, maxval). Recursively call for node.left with (minval, node.val) and for node.right with (node.val, maxval). Initial call starts with (-infinity, +infinity).
17. Kth Smallest Element in a BST
Why you might get asked this:
Tests understanding of BST properties and in-order traversal, which naturally yields elements in sorted order.
How to answer:
Perform an in-order traversal of the BST. Keep a counter and return the node value when the counter reaches k. Can be done iteratively or recursively.
Example answer:
Perform an in-order traversal (left, root, right). Keep a counter, count, initialized to 0. When visiting a node, increment count. If count equals k, then the current node's value is the Kth smallest, so store it and stop. This can be done recursively or iteratively using a stack.
18. Word Break
Why you might get asked this:
A classic dynamic programming problem testing string segmentation and dictionary lookup.
How to answer:
Use dynamic programming. dp[i] is true if s[0...i-1] can be segmented. dp[i] = dp[j] && wordDict.contains(s[j...i-1]) for 0 <= j < i.
Example answer:
Create a boolean DP array dp of size n+1, where dp[i] is true if s[0...i-1] can be segmented. Initialize dp[0] = true. Iterate i from 1 to n. For each i, iterate j from 0 to i-1. If dp[j] is true and the substring s[j...i-1] is in wordDict, set dp[i] = true and break inner loop. Return dp[n].
19. Jump Game
Why you might get asked this:
Tests greedy algorithms or dynamic programming; involves finding if a path exists to the end of an array based on jump lengths.
How to answer:
Use a greedy approach. Maintain maxreach (farthest index reachable). Iterate through the array; if current index i is beyond maxreach, return false. Update maxreach = max(maxreach, i + nums[i]).
Example answer:
Initialize maxreach = 0. Iterate i from 0 to n-1. If i > maxreach, it's impossible to reach i, so return False. Update maxreach = max(maxreach, i + nums[i]). If maxreach >= n-1 at any point, return True. If the loop completes, return True (meaning maxreach covered the entire array).
20. Unique Paths
Why you might get asked this:
A common dynamic programming problem involving grid traversal and combinatorial counting.
How to answer:
The number of unique paths to (i, j) is paths(i-1, j) + paths(i, j-1). Use a 2D DP table to store results, or optimize to 1D.
Example answer:
Create a 2D DP array dp[m][n]. Initialize dp[0][j] = 1 for all j (first row) and dp[i][0] = 1 for all i (first column) because there's only one way to reach any cell in the first row/column. For dp[i][j] where i, j > 0, calculate dp[i][j] = dp[i-1][j] + dp[i][j-1]. Return dp[m-1][n-1].
21. Coin Change
Why you might get asked this:
A classic dynamic programming problem focused on finding the minimum number of coins to make a sum.
How to answer:
Use a DP array where dp[i] is the minimum coins for amount i. Initialize dp[0]=0 and others to infinity. Iterate through amounts and coins.
Example answer:
Create a DP array dp of size amount + 1, initialized to infinity (or amount + 1). Set dp[0] = 0. Iterate i from 1 to amount. For each coin in coins: if coin <= i, update dp[i] = min(dp[i], dp[i - coin] + 1). If dp[amount] is still infinity, return -1, else return dp[amount].
22. Spiral Matrix
Why you might get asked this:
Tests array manipulation, boundary tracking, and careful pointer management in a 2D grid.
How to answer:
Use four boundary variables (top, bottom, left, right). Iteratively traverse the matrix in four directions (right, down, left, up), adjusting boundaries after each traversal.
Example answer:
Initialize top = 0, bottom = m-1, left = 0, right = n-1 and an empty result list. While top <= bottom and left <= right: traverse right (add matrix[top][col] from left to right, then top++); traverse down (add matrix[row][right] from top to bottom, then right--); traverse left (add matrix[bottom][col] from right to left if top <= bottom, then bottom--); traverse up (add matrix[row][left] from bottom to top if left <= right, then left++).
23. Subsets
Why you might get asked this:
Tests backtracking or iterative bit manipulation for generating all possible subsets (power set) of a given set.
How to answer:
Use a recursive backtracking approach. At each element, decide to either include it in the current subset or exclude it, then recurse.
Example answer:
Use a recursive backtrack function. The function takes currentsubset and startindex. Add currentsubset to the results. Iterate i from startindex to nums.length. Add nums[i] to current_subset, recursively call backtrack with i+1, then remove nums[i] (backtrack). Initial call is backtrack([], 0).
24. Pacific Atlantic Water Flow
Why you might get asked this:
A graph traversal (DFS/BFS) problem on a 2D grid, involving multiple sources and intersection of reachable cells.
How to answer:
Perform two independent DFS/BFS traversals: one starting from all cells adjacent to the Pacific, and another from all cells adjacent to the Atlantic. Find common cells reachable by both.
Example answer:
Initialize two boolean matrices, pacificreachable and atlanticreachable. Start DFS/BFS from all cells in the first row/column for Pacific and last row/column for Atlantic. During traversal, only move to higher or equal elevation. Finally, iterate through all cells and add (r, c) to result if pacificreachable[r][c] and atlanticreachable[r][c] are both true.
25. Rotting Oranges
Why you might get asked this:
A classic BFS problem on a grid, typically involves layers of infection or propagation time.
How to answer:
Use BFS. Initialize the queue with all rotten oranges (initial layer). In each minute, process current rotten oranges and mark adjacent fresh oranges as rotten, adding them to the next layer.
Example answer:
Initialize a queue with all initial rotten oranges (r, c). Count fresh oranges. Perform BFS: in each step (minute), dequeue all oranges from the current layer. For each, check its 4 neighbors. If a neighbor is a fresh orange, mark it rotten, decrement fresh orange count, and enqueue it for the next minute. Increment minutes. If fresh_oranges > 0 after BFS, return -1; else return minutes.
26. Course Schedule
Why you might get asked this:
Tests graph traversal (DFS/BFS), topological sorting, and cycle detection in a directed graph.
How to answer:
Build an adjacency list and calculate in-degrees for each course. Use Kahn's algorithm (BFS) by starting with courses with in-degree 0, processing them, and decrementing neighbors' in-degrees.
Example answer:
Build a graph (adjacency list) and calculate indegree for each course. Add all courses with indegree = 0 to a queue. While the queue is not empty, dequeue a course, add it to the topologicalorder, and for its neighbors, decrement their indegree. If a neighbor's indegree becomes 0, enqueue it. If the length of topologicalorder equals numCourses, return True, else False (cycle detected).
27. Design HashMap
Why you might get asked this:
Assesses understanding of fundamental data structures, hash functions, collision resolution, and array/linked list implementation.
How to answer:
Implement a hash map using an array of linked lists (or arrays) to handle collisions (separate chaining). Provide put, get, remove methods.
Example answer:
Define a fixed-size array (buckets). For each bucket, use a linked list to store key-value pairs (nodes). Implement a hash function to map keys to bucket indices. put(key, value) computes hash, finds bucket, updates/adds node. get(key) computes hash, traverses linked list to find key. remove(key) similar to get, but removes the node.
28. Merge k Sorted Lists
Why you might get asked this:
A hard problem combining linked list manipulation with priority queues (min-heap) for efficient merging.
How to answer:
Use a min-heap. Add the first node of each list to the heap. Repeatedly extract the minimum node, add it to the merged list, and if it has a next node, add that to the heap.
Example answer:
Create a dummy head for the merged list. Initialize a min-heap. Add the head of each non-empty linked list to the min-heap (store (value, list_index) or (value, node)). While the heap is not empty, extract the minimum element. Append this element to the merged list. If the extracted node has a next node, add that next node to the min-heap.
29. Binary Tree Maximum Path Sum
Why you might get asked this:
A challenging recursive tree problem involving path sums, where a path doesn't necessarily pass through the root.
How to answer:
Use recursion. For each node, calculate the max path sum from that node going down (left or right, not both). Update a global max_sum with paths that potentially turn.
Example answer:
Implement a recursive dfs function that returns the maximum path sum starting from the current node and going downwards (i.e., node.val + max(leftpathsum, rightpathsum, 0)). Maintain a global variable maxsofar initialized to negative infinity. Inside dfs, update maxsofar = max(maxsofar, node.val + leftpathsum + rightpathsum). Return the dfs result.
30. LRU Cache
Why you might get asked this:
Tests understanding of data structures for caching, combining hash maps for O(1) lookup with doubly linked lists for O(1) eviction policy.
How to answer:
Implement using a hash map (key -> node) and a doubly linked list (stores nodes in LRU order). put updates map and moves node to front. get updates map and moves node to front. If capacity exceeded, remove from tail.
Example answer:
Implement using a hash map for key -> node mapping and a doubly linked list to maintain order (most recently used at head, least recently used at tail). get(key) retrieves node from map, moves it to head, returns value. put(key, value) creates/updates node, moves to head. If capacity exceeded, remove tail.prev node, update map.
Other Tips to Prepare for a LeetCode Interview
Consistent practice is paramount when preparing for LeetCode questions. "The only way to do great work is to love what you do," and for technical interviews, that means loving the challenge of problem-solving. Start with easier problems to build confidence and gradually move to medium and hard difficulties. Focus on understanding the underlying data structures and algorithms, not just memorizing solutions. Debugging your code effectively and articulating your thought process clearly are also critical skills.
Consider using tools like Verve AI Interview Copilot (https://vervecopilot.com) to simulate real interview environments. This innovative platform can provide instant feedback on your approach to LeetCode questions, helping you refine your communication and problem-solving strategies. As the legendary computer scientist Edsger Dijkstra once said, "The question of whether computers can think is no more interesting than the question of whether submarines can swim." The focus should be on how effectively you can utilize your tools and knowledge. Practice mock interviews with Verve AI Interview Copilot to get accustomed to the pressure and structure of technical interviews. Regular engagement with diverse LeetCode questions and leveraging intelligent practice tools like Verve AI Interview Copilot will significantly boost your readiness.
Frequently Asked Questions
Q1: How much time should I dedicate to LeetCode daily?
A1: Aim for at least 1-2 hours daily, focusing on understanding concepts and solving diverse LeetCode questions rather than just quantity.
Q2: Should I focus on specific LeetCode difficulties?
A2: Start with Easy, then move to Medium. Dedicate some time to Hard problems, but prioritize a strong grasp of Easy and Medium for most roles.
Q3: What if I get stuck on a LeetCode problem?
A3: Try to solve it for 30-45 minutes. If stuck, look at hints or explanations, understand the solution, and then re-implement it from scratch.
Q4: Is it better to use Python or Java for LeetCode?
A4: Use the language you are most comfortable with. Both Python and Java are widely accepted, but Python's conciseness can be advantageous for speed.
Q5: How important are system design questions compared to LeetCode?
A5: For senior roles, system design is equally, if not more, important. For junior to mid-level, LeetCode (data structures & algorithms) is usually the primary focus.
