Preparing for technical interviews, especially those at leading companies like Airbnb, often involves mastering LeetCode-style technical interview questions. These challenges are a cornerstone of the hiring process, designed to evaluate your fundamental computer science knowledge, problem-solving abilities, and coding proficiency under pressure. Understanding the types of questions commonly asked and developing a systematic approach to solving them can significantly boost your confidence and performance. This guide will walk you through what these questions entail, why they are crucial, and provide a comprehensive list of 30 common problems, along with tips on how to approach them effectively to showcase your analytical and coding skills.
What Are LeetCode-style Technical Interview Questions?
LeetCode-style technical interview questions are coding challenges that assess a candidate's understanding of data structures, algorithms, and computational complexity. They are typically presented on platforms like LeetCode or HackerRank and require writing efficient code to solve a given problem. These questions often involve manipulating arrays, strings, trees, graphs, or implementing dynamic programming solutions. The primary goal is not just to find a correct solution but to find the most optimal one in terms of time and space complexity. Interviewers use these questions to gauge a candidate's analytical thinking, logical reasoning, and ability to translate complex problems into clean, runnable code.
Why Do Interviewers Ask LeetCode-style Technical Interview Questions?
Interviewers ask LeetCode-style technical interview questions for several critical reasons. First, they provide a standardized way to evaluate a candidate's foundational computer science knowledge and problem-solving skills under a time constraint. These questions reveal how a candidate approaches an unfamiliar problem, breaks it down, and designs an efficient algorithm. Second, they assess coding proficiency, including syntax, debugging, and writing clean, maintainable code. Third, they help gauge a candidate's ability to analyze algorithmic efficiency (time and space complexity), a crucial skill for building scalable systems. Finally, these questions often simulate real-world coding challenges, demonstrating how a candidate might contribute to developing robust and performant software solutions.
Contains Duplicate
Valid Anagram
Two Sum
Merge Two Sorted Lists
Maximum Subarray
Climbing Stairs
Reverse Linked List
Valid Parentheses
Implement Queue using Stacks
Lowest Common Ancestor of a Binary Search Tree
Product of Array Except Self
Best Time to Buy and Sell Stock
Longest Common Prefix
Linked List Cycle
Binary Tree Inorder Traversal
Search in Rotated Sorted Array
House Robber
Number of Islands
Kth Largest Element in an Array
Group Anagrams
Delete Node in a Linked List
Symmetric Tree
Palindrome Number
Combination Sum
Jump Game
Word Break
All Paths From Source to Target
Design an LRU Cache
Minimum Path Sum
Find First and Last Position of Element in Sorted Array
Preview List
1. Contains Duplicate
Why you might get asked this:
This tests basic array manipulation and the efficient use of hash sets to check for element uniqueness, assessing your knowledge of data structures.
How to answer:
Use a hash set (or HashSet in Java, set in Python) to store elements as you iterate. If an element is already in the set, return true.
Example answer:
Iterate through the array. For each number, check if it's already present in a hash set. If yes, a duplicate exists, return true. If no, add the number to the set. If the loop finishes, no duplicates were found, return false. This approach ensures O(N) time complexity.
2. Valid Anagram
Why you might get asked this:
Evaluates string manipulation skills and the ability to use character counts (frequency arrays or hash maps) for comparison.
How to answer:
Sort both strings and compare them. Alternatively, use frequency arrays (26 for lowercase English) or hash maps to count characters for both strings and compare counts.
Example answer:
Convert both strings to character arrays, sort them, then compare if the sorted arrays are identical. An efficient approach is to use a frequency map: increment counts for characters in s and decrement for t. If all counts are zero, they are anagrams.
3. Two Sum
Why you might get asked this:
A foundational problem testing hash map usage for efficient lookups, crucial for optimizing search operations.
How to answer:
Use a hash map to store numbers and their indices. For each number, check if target - current_number exists in the map. If so, you found the pair.
Example answer:
Initialize a hash map. Iterate through the array nums. For each num, calculate complement = target - num. Check if complement exists as a key in the map. If yes, return [map[complement], current_index]. Otherwise, add num and its index to the map.
4. Merge Two Sorted Lists
Why you might get asked this:
Tests linked list manipulation, specifically merging two sorted structures while maintaining sorted order.
How to answer:
Use a dummy node to simplify list building. Iterate through both lists, appending the smaller value to the new list, advancing that list's pointer. Handle remaining elements.
Example answer:
Create a dummy node and a current pointer initialized to dummy. While both lists l1 and l2 have nodes, compare l1.val and l2.val. Append the smaller node to current.next and advance that list's pointer. Move current forward. Append any remaining nodes. Return dummy.next.
5. Maximum Subarray
Why you might get asked this:
A classic dynamic programming or greedy algorithm problem. It assesses your ability to find optimal substructures.
How to answer:
Kadane's algorithm is optimal: iterate through the array, maintaining currentmax ending at the current position and globalmax. currentmax = max(num, currentmax + num).
Example answer:
Initialize maxsofar to the first element and currentmax to the first element. Iterate from the second element: currentmax = max(nums[i], currentmax + nums[i]). Update maxsofar = max(maxsofar, currentmax). Return maxsofar.
6. Climbing Stairs
Why you might get asked this:
A classic dynamic programming problem showcasing Fibonacci sequence patterns and memoization/tabulation.
How to answer:
This is a Fibonacci sequence: dp[i] = dp[i-1] + dp[i-2]. Base cases dp[1]=1, dp[2]=2. You can use an array for memoization or optimize space.
Example answer:
This problem can be solved using dynamic programming. To reach stair n, you can either come from n-1 (one step) or n-2 (two steps). So, ways[n] = ways[n-1] + ways[n-2]. Base cases are ways[1]=1 and ways[2]=2. Iteratively compute up to n.
7. Reverse Linked List
Why you might get asked this:
Fundamental linked list manipulation. Tests pointer handling and iterative/recursive thinking.
How to answer:
Iteratively, keep track of previous, current, and nextnode. In each step, set current.next = previous, then update previous = current, current = nextnode.
Example answer:
Initialize prev to None and curr to head. While curr is not None: store nexttemp = curr.next, set curr.next = prev, update prev = curr, then curr = nexttemp. Finally, return prev as the new head.
8. Valid Parentheses
Why you might get asked this:
Tests stack usage for matching pairs of elements, a common pattern in parsing and expression evaluation.
How to answer:
Use a stack. Push opening brackets onto the stack. When a closing bracket appears, check if the stack top is its matching opening bracket. If so, pop.
Example answer:
Use a stack and a map for bracket pairs. Iterate through the string. If an opening bracket, push to stack. If closing, check if stack is empty or top doesn't match; if so, invalid. Pop matching opening bracket. Finally, stack must be empty for valid.
9. Implement Queue using Stacks
Why you might get asked this:
Evaluates understanding of ADTs and how to emulate one using another, focusing on stack properties (LIFO vs. FIFO).
How to answer:
Use two stacks: instack for push operations and outstack for pop/peek. When outstack is empty, transfer all elements from instack to out_stack.
Example answer:
Maintain inStack for new elements and outStack for dequeueing. push() just adds to inStack. pop() and peek() first check if outStack is empty; if so, move all elements from inStack to outStack (reversing order). Then, perform the operation on outStack.
10. Lowest Common Ancestor of a Binary Search Tree
Why you might get asked this:
Tests tree traversal logic and leveraging BST properties for efficient search.
How to answer:
Utilize BST property: if both nodes are smaller than root, go left. If both are larger, go right. If one is smaller/larger or one is root, then root is LCA.
Example answer:
Iteratively, while the root is not null: if p.val and q.val are both less than root.val, move root = root.left. If both are greater, move root = root.right. Otherwise, root is the LCA, return root.
11. Product of Array Except Self
Why you might get asked this:
Tests array manipulation, specifically avoiding division and achieving O(N) time with O(1) extra space (excluding output array).
How to answer:
Perform two passes. First pass: calculate prefix products. Second pass: calculate suffix products and combine with prefix products.
Example answer:
Initialize result array. First, fill result[i] with the product of all elements to its left. Then, iterate from right to left, multiplying result[i] by the product of all elements to its right (maintained in a running right_product).
12. Best Time to Buy and Sell Stock
Why you might get asked this:
A greedy algorithm problem focusing on optimizing profit by tracking minimum price seen so far.
How to answer:
Iterate through prices. Keep track of the minimum price encountered so far (minprice) and update the maximum profit (maxprofit = max(maxprofit, currentprice - min_price)).
Example answer:
Initialize minprice to infinity and maxprofit to 0. Iterate through prices: update minprice = min(minprice, price). Then, update maxprofit = max(maxprofit, price - minprice). Return maxprofit.
13. Longest Common Prefix
Why you might get asked this:
Tests string manipulation and iterative comparison of strings in an array.
How to answer:
Pick the first string as the initial prefix. Iterate through remaining strings, shortening the prefix until it matches the current string's start.
Example answer:
Take the first string as the initial prefix. Iterate from the second string in the array. While the current string does not start with prefix, shorten prefix by removing its last character. If prefix becomes empty, return "". Return final prefix.
14. Linked List Cycle
Why you might get asked this:
A classic linked list problem using the fast/slow pointer approach (Floyd's Cycle-Finding Algorithm).
How to answer:
Use two pointers, one slow (moves 1 step) and one fast (moves 2 steps). If they meet, a cycle exists.
Example answer:
Initialize slow and fast pointers to head. Move slow one step at a time and fast two steps. If they ever meet (point to the same node), a cycle is detected and return true. If fast or fast.next becomes null, no cycle, return false.
15. Binary Tree Inorder Traversal
Why you might get asked this:
Fundamental tree traversal, essential for understanding tree structures and recursive/iterative approaches.
How to answer:
Inorder: Left -> Root -> Right. Can be implemented recursively (DFS) or iteratively using a stack.
Example answer:
Recursive: inorder(node): if node is not null, inorder(node.left), add node.val to list, inorder(node.right).
Iterative: Use a stack. Push left children until null, then pop, add value, and move to right child.
16. Search in Rotated Sorted Array
Why you might get asked this:
Tests binary search adaptation in a non-trivial scenario, requiring careful handling of rotated segments.
How to answer:
Apply modified binary search. Determine which half is sorted. Based on the target's value, decide whether to search in the sorted or unsorted half.
Example answer:
Perform binary search. At mid, one half ([low, mid] or [mid, high]) will be sorted. Determine which half is sorted. If target falls within the sorted range, search there. Otherwise, search the other half. Adjust low/high accordingly.
17. House Robber
Why you might get asked this:
A classic dynamic programming problem illustrating state definition and recurrence relations (rob current or not).
How to answer:
Use DP. dp[i] is max money up to house i. dp[i] = max(dp[i-1], dp[i-2] + nums[i]). Optimize space to O(1) by only tracking prev1 and prev2.
Example answer:
This is a DP problem. To rob house i, you must not rob house i-1. Max money is max(moneyfromprevhouse, moneyfromtwohousesago + currenthousevalue). Use two variables: robprev (max money robbing prev house) and notrobprev (max money not robbing prev house). Update these for current house.
18. Number of Islands
Why you might get asked this:
Tests graph traversal (BFS or DFS) on a 2D grid, including handling visited states.
How to answer:
Iterate through the grid. If '1' is found, increment island count, then start BFS/DFS from it to mark all connected '1's as '0' (visited).
Example answer:
Iterate grid cells. If grid[r][c] == '1': increment island_count, and perform DFS/BFS starting from (r,c). In DFS/BFS, change grid[x][y] to '0' for all visited land cells to prevent re-counting and mark them as visited.
19. Kth Largest Element in an Array
Why you might get asked this:
Evaluates understanding of sorting algorithms or heap (priority queue) usage for finding order statistics efficiently.
How to answer:
Use a min-heap of size k. Iterate through array: push element. If heap size > k, pop smallest. The heap's root is the kth largest. Quickselect is O(N) average.
Example answer:
Use a min-heap (priority queue). Iterate through the array nums, adding each element to the heap. If the heap's size exceeds k, remove the smallest element from the heap (using pop). After iterating through all numbers, the root of the heap is the Kth largest element.
20. Group Anagrams
Why you might get asked this:
Tests string manipulation and hash map usage to group items based on a derived key (e.g., sorted string).
How to answer:
Use a hash map where keys are sorted strings (e.g., "aet" for "eat", "tea") and values are lists of original anagrams.
Example answer:
Create a hash map. For each word in the input array, sort its characters to form a key (e.g., "eat" -> "aet"). Use this sorted string as the key in the hash map, and append the original word to the list associated with that key. Return all values from the hash map.
21. Delete Node in a Linked List
Why you might get asked this:
A trickier linked list problem where you only have access to the node to be deleted, not its predecessor.
How to answer:
Copy the value from node.next to node, then update node.next to node.next.next. This effectively removes node.next.
Example answer:
Since you don't have access to the head or previous node, copy the value of the next node into the current node (node.val = node.next.val). Then, skip the next node by updating the current node's next pointer to point to the node after the next one (node.next = node.next.next).
22. Symmetric Tree
Why you might get asked this:
Tests recursive/iterative tree traversal and mirror-image comparison logic.
How to answer:
Define a helper function that compares two nodes: isMirror(node1, node2). Check if node1.val == node2.val AND isMirror(node1.left, node2.right) AND isMirror(node1.right, node2.left).
Example answer:
Implement a helper function isSymmetricHelper(leftNode, rightNode). This function returns true if both nodes are null, false if only one is null, and checks if leftNode.val == rightNode.val and recursively calls itself with (leftNode.left, rightNode.right) and (leftNode.right, rightNode.left).
23. Palindrome Number
Why you might get asked this:
Tests number manipulation without converting to string, focusing on reversing digits or comparing halves.
How to answer:
Reverse the second half of the number and compare it to the first half. Handle edge cases like negative numbers or numbers ending in 0.
Example answer:
Handle negative numbers (false). For positive numbers, reverse the number digit by digit, storing it in reversednum. Compare originalnum with reversed_num. Alternatively, for even length, compare the first half with the reversed second half.
24. Combination Sum
Why you might get asked this:
A classic backtracking problem. Tests recursive thinking for finding all possible combinations.
How to answer:
Use backtracking (DFS). At each step, either include the current candidate (and potentially include it again) or move to the next candidate. Sum must equal target.
Example answer:
Use a recursive backtracking function. In each call, iterate through candidates starting from a given startindex. Add the current candidate to currentcombination. If target is 0, add currentcombination to results. If target is negative, backtrack. Recursively call with target - candidate and currentindex (allowing reuse).
25. Jump Game
Why you might get asked this:
A greedy algorithm or dynamic programming problem. Tests ability to track reachability or maximum reachable index.
How to answer:
Greedy approach: iterate from start, keeping track of the maxreach index. If maxreach is ever less than current index, return false. If max_reach >= last index, return true.
Example answer:
Maintain a maxreach variable, initialized to 0. Iterate from i = 0 to n-1. If i > maxreach, return false (cannot reach this position). Update maxreach = max(maxreach, i + nums[i]). If max_reach >= n-1, return true.
26. Word Break
Why you might get asked this:
A dynamic programming problem. Tests string partitioning and dictionary lookups.
How to answer:
Use DP. dp[i] is true if s[0...i-1] can be segmented. dp[i] = dp[j] && wordDict.contains(s[j...i-1]) for j from 0 to i-1.
Example answer:
Create a boolean dp array of size n+1. dp[0] is true. For i from 1 to n, 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] to true and break. Return dp[n].
27. All Paths From Source to Target
Why you might get asked this:
Tests graph traversal (DFS) for finding all possible paths in a DAG.
How to answer:
Use DFS. Maintain currentpath. When target is reached, add currentpath to results. Backtrack after exploring each neighbor.
Example answer:
Implement a recursive DFS function dfs(node, currentpath, result, graph). Add node to currentpath. If node is the target, add a copy of currentpath to result. Otherwise, for each neighbor of node, call dfs(neighbor, currentpath, result, graph). Backtrack by removing node from current_path.
28. Design an LRU Cache
Why you might get asked this:
Tests system design principles, particularly the combination of a hash map (for O(1) access) and a doubly linked list (for O(1) eviction/recency updates).
How to answer:
Combine a hash map to store key -> Node mappings and a doubly linked list to maintain item recency. Head is most recent, tail is least recent. Update/move nodes on access.
Example answer:
Implement using a HashMap and a DoublyLinkedList. Node stores key, value, prev, next. get(key): if exists, move to head, return value. put(key, value): if exists, update value, move to head. If new, create node, add to head. If capacity exceeded, remove tail node and from map.
29. Minimum Path Sum
Why you might get asked this:
A classic dynamic programming problem on a 2D grid. Tests finding the minimum cost path.
How to answer:
Use DP. dp[i][j] is the minimum sum to reach (i,j). dp[i][j] = grid[i][j] + min(dp[i-1][j], dp[i][j-1]). Handle base cases (first row/column).
Example answer:
Create a dp grid of same size. dp[0][0] = grid[0][0]. Fill first row: dp[0][j] = dp[0][j-1] + grid[0][j]. Fill first column: dp[i][0] = dp[i-1][0] + grid[i][0]. For other cells, dp[i][j] = grid[i][j] + min(dp[i-1][j], dp[i][j-1]). Return dp[m-1][n-1].
30. Find First and Last Position of Element in Sorted Array
Why you might get asked this:
Tests advanced binary search, requiring two separate binary searches: one for the first occurrence and one for the last.
How to answer:
Perform two modified binary searches. First, find the leftmost occurrence (adjust high = mid - 1 when nums[mid] == target). Second, find the rightmost occurrence (adjust low = mid + 1 when nums[mid] == target).
Example answer:
Implement a findBound(nums, target, isFirst) helper function. If isFirst is true, find the first occurrence: when nums[mid] == target, store mid and search left (high = mid - 1). If isFirst is false, find the last: store mid and search right (low = mid + 1). Call this helper twice.
Other Tips to Prepare for a LeetCode-style Technical Interview
Mastering LeetCode-style technical interview questions requires consistent practice and a strategic approach. As Albert Einstein famously said, "The only source of knowledge is experience." This holds true for coding interviews; hands-on problem-solving builds intuition. Begin by understanding fundamental data structures and algorithms, then apply them by solving problems across various categories. Don't just solve problems; analyze their time and space complexity, and think about alternative solutions. Practice explaining your thought process aloud, as communication is key in interviews.
Utilize resources like Verve AI Interview Copilot (https://vervecopilot.com) to simulate real interview environments, get instant feedback on your code, and refine your communication skills. Verve AI Interview Copilot can provide personalized insights into your performance, helping you identify areas for improvement in both your coding and your articulation. Remember to practice under timed conditions to build resilience. Furthermore, engage in mock interviews with peers or mentors. This simulates the pressure of a real interview and allows you to practice articulating your solutions clearly. The more you practice with tools like Verve AI Interview Copilot, the more comfortable and confident you'll become, turning challenges into opportunities. As Confucius wisely noted, "The man who moves a mountain begins by carrying away small stones." Break down your preparation into manageable steps, focusing on one concept or problem type at a time, and gradually build your expertise.
Frequently Asked Questions
Q1: How much time should I dedicate to LeetCode practice?
A1: Aim for consistent practice, ideally 1-2 hours daily or several hours on weekends, for at least 2-3 months before your interview.
Q2: Should I memorize solutions to all common problems?
A2: No, focus on understanding the underlying algorithms and data structures, and the thought process to arrive at the solution.
Q3: What's more important: speed or correctness in coding?
A3: Correctness and optimal solution are primary. Speed comes with practice, but a correct, well-reasoned solution is always preferred.
Q4: How do I handle a question I don't know during an interview?
A4: Clarify the problem, discuss your thought process, break it down, brainstorm approaches, and communicate your steps even if stuck.
Q5: Should I write pseudocode before actual code?
A5: Yes, outlining your logic with pseudocode or high-level steps helps structure your solution and prevents errors.
Q6: Is it okay to ask clarifying questions about the problem?
A6: Absolutely. Asking clarifying questions demonstrates good communication skills and ensures you're solving the intended problem.
