Preparing for technical interviews at top tech companies, including Zoom, often revolves around mastering LeetCode-style interview questions. These challenges are designed to assess a candidate's problem-solving abilities, command over data structures, and algorithmic thinking. Far beyond mere coding proficiency, these questions probe your analytical skills, efficiency considerations, and ability to articulate complex solutions under pressure. This guide focuses on the most frequently encountered LeetCode interview questions, providing insights into why they are asked, how to approach them, and effective ways to answer, ensuring you are well-prepared to tackle these critical assessments.
What Are LeetCode Interview Questions?
LeetCode interview questions are algorithmic and data structure problems commonly used by tech companies to evaluate software engineering candidates. They cover a wide spectrum of topics, from fundamental array manipulations and string operations to complex graph algorithms, dynamic programming, and tree traversals. These questions typically involve implementing an efficient solution that adheres to specific time and space complexity constraints. The platform LeetCode serves as a popular online judge, providing a vast collection of such problems along with test cases to validate solutions. Mastering these types of problems is crucial for demonstrating your core computer science knowledge and practical coding skills.
Why Do Interviewers Ask LeetCode Interview Questions?
Interviewers ask LeetCode interview questions for several key reasons. Firstly, they effectively gauge a candidate's problem-solving methodology, including how they break down complex problems, identify patterns, and devise logical steps to reach a solution. Secondly, these questions reveal a candidate's understanding and application of fundamental data structures and algorithms, which are the building blocks of efficient software. Thirdly, the process of solving these coding challenges under timed conditions tests a candidate's ability to think critically and perform under pressure, mirroring real-world development scenarios. Finally, explaining your thought process and solution demonstrates communication skills, which are vital for collaborative team environments.
Two Sum
Reverse Integer
Palindrome Number
Valid Parentheses
Merge Two Sorted Lists
Search Insert Position
Climbing Stairs
Maximum Subarray
Single Number
Add Binary
Merge Sorted Array
Meeting Rooms
Maximum Depth of Binary Tree
Majority Element
Rotate Array
Word Search
Reverse Linked List
Course Schedule
Number of Islands
Longest Common Prefix
Generate Parentheses
Subdomain Visit Count
Text Justification
Find All Anagrams in a String
Letter Combinations of a Phone Number
Lowest Common Ancestor of a BST
Valid Sudoku
Longest Common Subsequence
Word Break
Container With Most Water
Preview List
1. Two Sum
Why you might get asked this:
This classic problem assesses your ability to use hash maps for efficient lookups, a fundamental skill for optimizing time complexity in array-based problems. It's often an early screener.
How to answer:
Iterate through the array, for each number, calculate its complement. Store numbers and their indices in a hash map. If the complement is found, return the indices.
Example answer:
To solve Two Sum, I'd use a hash map to store numbers and their indices as I iterate. For each num, I calculate complement = target - num. If complement is already in the map, I've found my pair. Otherwise, I add num and its index to the map. This achieves O(N) time complexity.
2. Reverse Integer
Why you might get asked this:
This question tests your handling of numerical manipulations, edge cases like negative numbers, and crucial overflow conditions, demonstrating attention to detail.
How to answer:
Extract digits using the modulus operator and reconstruct the reversed number. Before returning, check if the reversed number falls within the 32-bit integer range to prevent overflow.
Example answer:
I'd reverse the integer digit by digit using modulo and division. Key is to handle negative signs first, then apply the reversal. Crucially, before returning, I must check for integer overflow against 2^31 - 1 and -2^31 limits.
3. Palindrome Number
Why you might get asked this:
It checks your logic for number manipulation and ability to solve problems without converting to strings, focusing on mathematical properties.
How to answer:
Compare the number with its reversed version. For efficiency, consider only reversing half the number to avoid overflow and optimize comparisons. Handle negative numbers and single-digit numbers.
Example answer:
To check if an integer is a palindrome, I'd compare the number with its reversed self. I can reverse only half the number to be more efficient, stopping when the original number is less than the reversed half. Negative numbers are not palindromes.
4. Valid Parentheses
Why you might get asked this:
This problem is a common test of stack data structure usage, vital for parsing and expression evaluation tasks. It shows understanding of order and matching.
How to answer:
Use a stack. When an opening bracket is encountered, push it. When a closing bracket is encountered, pop from the stack and check for a match. If mismatch or empty stack, it's invalid.
Example answer:
I'd use a stack to track opening parentheses. When an opening bracket (, {, [ appears, I push it. When a closing bracket ), }, ] appears, I pop from the stack and check if it's the matching type. If not matched or stack is empty when popping, it's invalid.
5. Merge Two Sorted Lists
Why you might get asked this:
Tests your understanding of linked lists, recursion, and iterative merging, which are foundational for data processing. It assesses your ability to manipulate pointers.
How to answer:
Recursively or iteratively, compare the current nodes of both lists. The smaller node becomes the next node in the merged list, and you continue with the rest of that list.
Example answer:
To merge two sorted lists, I'd use a dummy head node to simplify initial pointer handling. I then iteratively compare nodes from both lists, appending the smaller one to the merged list and advancing its pointer. Finally, append any remaining nodes.
6. Search Insert Position
Why you might get asked this:
This question evaluates your proficiency with binary search, an essential algorithm for searching in sorted arrays efficiently. It's a quick check of fundamental algorithms.
How to answer:
Perform a standard binary search. If the target is found, return its index. If not found, the left pointer will end up at the correct insert position.
Example answer:
This is a classic binary search problem. I'd initialize low = 0 and high = len(nums) - 1. While low <= high, calculate mid. If nums[mid] equals the target, return mid. If nums[mid] is less, low = mid + 1; otherwise high = mid - 1. low will be the insert position.
7. Climbing Stairs
Why you might get asked this:
A simple introduction to dynamic programming or memoization, demonstrating your ability to identify overlapping subproblems and optimal substructure.
How to answer:
Recognize this as a Fibonacci sequence pattern. The number of ways to reach n steps is the sum of ways to reach n-1 and n-2 steps. Use DP array or just two variables.
Example answer:
This problem follows the Fibonacci sequence. The number of ways to climb n stairs is the sum of ways to climb n-1 stairs and n-2 stairs. I can use dynamic programming, storing results for dp[i] = dp[i-1] + dp[i-2], starting from dp[1]=1, dp[2]=2.
8. Maximum Subarray
Why you might get asked this:
Tests your understanding of array manipulation and Kadane's algorithm, an efficient greedy approach for finding maximum sums.
How to answer:
Use Kadane's algorithm: maintain a currentmax ending at the current position and a globalmax found so far. Update currentmax = max(num, currentmax + num).
Example answer:
I'd solve this using Kadane's algorithm. I'll maintain two variables: currentmax (the maximum sum ending at the current position) and globalmax (the overall maximum sum found). currentmax is updated as max(nums[i], currentmax + nums[i]), and globalmax is updated with currentmax at each step.
9. Single Number
Why you might get asked this:
An excellent test of bit manipulation techniques, showcasing your ability to solve problems efficiently using bitwise XOR operations.
How to answer:
XOR all elements in the array. The property of XOR (a ^ a = 0 and a ^ 0 = a) ensures that duplicate numbers cancel out, leaving only the unique number.
Example answer:
I'd use the XOR bitwise operation. The property of XOR is that a ^ a = 0 and a ^ 0 = a. If I XOR all numbers in the array together, all pairs will cancel each other out, leaving only the single unique number as the result.
10. Add Binary
Why you might get asked this:
Checks your ability to work with strings and simulate arithmetic operations, demonstrating attention to carry-overs and string manipulation.
How to answer:
Iterate from the end of both binary strings, adding digits along with any carry-over. Append results to a string builder, then reverse.
Example answer:
I would add the binary strings digit by digit from right to left, similar to how we perform decimal addition. I'd maintain a carry variable. Sum the current digits and carry, then determine the result digit and new carry. Append to a list and reverse at the end.
11. Merge Sorted Array
Why you might get asked this:
Tests in-place array manipulation and two-pointer techniques, specifically when merging from the end to avoid overwriting elements.
How to answer:
Use two pointers starting from the end of both arrays and a third pointer for the merged array's end. Compare elements and place the larger one at the merged end, moving pointers inward.
Example answer:
To merge in-place efficiently, I'd use three pointers: one at the end of nums1's valid elements (m-1), one at the end of nums2 (n-1), and one at the very end of nums1 (m+n-1). I'd compare elements and place the larger one at the end of nums1, working backwards.
12. Meeting Rooms
Why you might get asked this:
This problem assesses your understanding of interval scheduling, requiring sorting and then iterating to check for overlaps, useful in calendar or resource management.
How to answer:
Sort the meeting intervals by their start times. Then, iterate through the sorted intervals and check if any current meeting's start time is before the previous meeting's end time.
Example answer:
To determine if a person can attend all meetings, I would first sort the meetings by their start times. Then, I'd iterate through the sorted meetings, checking if the start time of any current meeting is earlier than the end time of the previous meeting. If so, there's an overlap, and it's not possible.
13. Maximum Depth of Binary Tree
Why you might get asked this:
A fundamental tree traversal question (DFS or BFS), evaluating your recursive thinking or iterative queue management for tree structures.
How to answer:
Use a Depth-First Search (DFS) or Breadth-First Search (BFS) approach. For DFS, recursively find the depth of left and right subtrees and return 1 + max(leftdepth, rightdepth).
Example answer:
I'd solve this recursively using Depth-First Search. The depth of a node is 1 plus the maximum depth of its left or right subtree. The base case is a null node, which has a depth of 0.
14. Majority Element
Why you might get asked this:
Tests your understanding of various algorithms, including the Boyer-Moore Voting Algorithm, for finding the majority element with optimal space and time.
How to answer:
The Boyer-Moore Voting Algorithm is ideal: maintain a candidate and a count. If count is 0, update candidate. If num matches candidate, increment count, else decrement.
Example answer:
I would use the Boyer-Moore Voting Algorithm. Initialize candidate and count (to 0). Iterate through the array: if count is 0, set candidate to current number. If current number matches candidate, increment count; else, decrement. The final candidate is the majority element.
15. Rotate Array
Why you might get asked this:
Examines in-place array manipulation, specifically using multiple reversals or cyclically shifting elements to achieve rotation efficiently.
How to answer:
A clever approach involves reversing the entire array, then reversing the first k elements, and finally reversing the remaining n-k elements. Remember k can be larger than n.
Example answer:
To rotate an array in-place, I'd first normalize k by k = k % len(nums). Then, I'd reverse the entire array. After that, I'd reverse the first k elements and finally reverse the remaining len(nums) - k elements. This technique is efficient.
16. Word Search
Why you might get asked this:
A classic backtracking problem that assesses your recursive thinking and ability to explore all possible paths in a grid, handling visited states.
How to answer:
Use Depth-First Search (DFS) with backtracking. For each cell, try to match the first letter. If it matches, recursively try adjacent cells for the next letter, marking visited cells. Backtrack if no path is found.
Example answer:
I'd use a DFS with backtracking. For each cell in the grid, I'd start a DFS traversal. The DFS function would check bounds, character match, and if the cell has been visited. If valid, mark visited, recurse on neighbors. If no path, backtrack by unmarking the cell.
17. Reverse Linked List
Why you might get asked this:
A fundamental linked list manipulation problem, testing your ability to iteratively or recursively change node pointers without losing data.
How to answer:
Iteratively, keep track of prev, current, and next_temp nodes. In each step, set current.next to prev, then update prev and current. Recursively, reverse the rest of the list and then link the current node.
Example answer:
Iteratively, I'd use three pointers: prev (initially null), current (initially head), and nexttemp. In a loop, nexttemp stores current.next, current.next becomes prev, prev becomes current, and current becomes next_temp. The final prev is the new head.
18. Course Schedule
Why you might get asked this:
This graph problem assesses your understanding of topological sort and cycle detection in directed graphs, crucial for dependency management systems.
How to answer:
Model courses as a directed graph. Use Kahn's algorithm (BFS with in-degrees) or DFS to detect a cycle. If a cycle exists, you cannot finish all courses.
Example answer:
I'd model this as a directed graph where courses are nodes and prerequisites are edges. Then I'd apply topological sort using Kahn's algorithm (BFS with in-degrees). If the number of processed nodes equals the total number of courses, it's possible; otherwise, a cycle exists, meaning it's impossible.
19. Number of Islands
Why you might get asked this:
A classic grid traversal problem, testing your mastery of DFS or BFS to explore connected components and mark visited cells to avoid re-counting.
How to answer:
Iterate through the grid. When a '1' is found, increment island count, then perform DFS or BFS from that cell to mark all connected '1's as '0' (visited) to prevent re-counting.
Example answer:
I'd iterate through the grid. When I find a '1', it signifies the start of an island. I increment my island count, then perform a DFS (or BFS) from that '1' to mark all connected '1's (horizontally and vertically) as '0' to avoid re-counting them.
20. Longest Common Prefix
Why you might get asked this:
Tests string manipulation, character-by-character comparison, and edge case handling with empty or single-string inputs.
How to answer:
Sort the array of strings. The longest common prefix must be a prefix of both the first and last strings in the sorted array. Compare character by character.
Example answer:
I would take the first string as an initial prefix. Then, I iterate through the remaining strings. For each string, I shorten the prefix from its end until it matches the beginning of the current string. If the prefix becomes empty, there's no common prefix.
21. Generate Parentheses
Why you might get asked this:
A common backtracking problem that checks your recursive function design, constraint management, and generating all valid combinations.
How to answer:
Use a backtracking approach. Maintain counts of open and close parentheses. Add an open parenthesis if open < n. Add a close parenthesis if close < open. Base case when open == close == n.
Example answer:
I'd use a recursive backtracking approach. My function takes opencount, closecount, and currentstring. If opencount < n, I can add an open parenthesis. If closecount < opencount, I can add a close parenthesis. Base case is when opencount == n and closecount == n.
22. Subdomain Visit Count
Why you might get asked this:
Tests string parsing, hash map usage for aggregation, and handling hierarchical data structure implied by domains.
How to answer:
For each cpdomain entry, parse the count. Then, iterate through the subdomains (e.g., mail.google.com -> google.com -> com), adding the count to a hash map for each level.
Example answer:
For each cpdomain, I'd first extract the count and the full domain. Then, I'd iterate through the domain, splitting it by . from right to left to get all subdomains (e.g., "mail.google.com", "google.com", "com"). For each subdomain, I'd add the count to a hash map.
23. Text Justification
Why you might get asked this:
A challenging string manipulation problem requiring careful handling of spacing, line breaks, and last-line rules, demonstrating attention to detail and edge cases.
How to answer:
Greedily fit as many words as possible into each line. Calculate spaces needed. Distribute spaces evenly among gaps, prioritizing left gaps. Special handling for the last line and single-word lines.
Example answer:
This requires careful logic for space distribution. I'd group words greedily per line. For each line (except the last), calculate total spaces to fill. Distribute these spaces as evenly as possible among word gaps. For the last line or single-word lines, left-justify with single spaces and fill remainder with one large space.
24. Find All Anagrams in a String
Why you might get asked this:
Tests your proficiency with the sliding window technique combined with character frequency maps for efficient string pattern matching.
How to answer:
Use a sliding window. Maintain frequency maps for the pattern and the current window. Expand the window, update frequencies, and shrink when the window size exceeds pattern length. Check for anagram match at each step.
Example answer:
I would use a sliding window approach. First, create frequency maps for the pattern string. Then, slide a window across the main string. Update the frequency map for the characters in the window as it expands and shrinks. Whenever the window size matches the pattern length, compare its frequency map with the pattern's.
25. Letter Combinations of a Phone Number
Why you might get asked this:
A classic backtracking problem, requiring recursive generation of all possible combinations based on digit-to-letter mappings.
How to answer:
Implement a backtracking recursive function. At each step, iterate through the letters corresponding to the current digit, append to current combination, and recurse for the next digit.
Example answer:
I'd use a recursive backtracking approach. I'll have a mapping of digits to letters. The recursive function will build combinations character by character. For each digit, it iterates through its corresponding letters, adds one to the current combination, and then calls itself for the next digit.
26. Lowest Common Ancestor of a BST
Why you might get asked this:
Checks your understanding of Binary Search Tree (BST) properties and how to leverage them for efficient traversal to find ancestors.
How to answer:
Utilize BST properties: if both nodes p and q are smaller than the current node, go left. If both are larger, go right. If one is smaller and one is larger (or one is the current node), the current node is the LCA.
Example answer:
Because it's a BST, I can find the LCA by traversing. If both p and q are smaller than the current node, the LCA must be in the left subtree. If both are larger, it's in the right. Otherwise, the current node is the LCA (or one of p, q is the current node).
27. Valid Sudoku
Why you might get asked this:
Tests your ability to validate constraints across rows, columns, and 3x3 sub-boxes, often using hash sets for efficient duplicate checks.
How to answer:
Use three sets (or arrays/hash maps) to keep track of numbers seen in each row, each column, and each 3x3 sub-box. Iterate through the board; if a duplicate is found, return false.
Example answer:
To validate a Sudoku, I would iterate through the board. For each cell, I'd check its value against sets tracking numbers seen in its row, its column, and its 3x3 sub-box. If a number is repeated within any of these constraints, the Sudoku is invalid.
28. Longest Common Subsequence
Why you might get asked this:
A standard dynamic programming problem, demonstrating your ability to build a DP table to solve overlapping subproblems.
How to answer:
Use a 2D dynamic programming table dp[i][j] representing the LCS length for text1[0...i-1] and text2[0...j-1]. If characters match, dp[i][j] = dp[i-1][j-1] + 1. Else, dp[i][j] = max(dp[i-1][j], dp[i][j-1]).
Example answer:
I'd use dynamic programming to solve this. Create a 2D dp table where dp[i][j] represents the length of the LCS of text1[0...i-1] and text2[0...j-1]. If text1[i-1] == text2[j-1], then dp[i][j] = dp[i-1][j-1] + 1. Otherwise, dp[i][j] = max(dp[i-1][j], dp[i][j-1]).
29. Word Break
Why you might get asked this:
Another dynamic programming problem, testing your ability to determine if a string can be segmented into dictionary words, often using memoization or a boolean array.
How to answer:
Use dynamic programming. Create a boolean array dp where dp[i] is true if s[0...i-1] can be segmented. dp[i] is true if dp[j] is true and s[j...i-1] is in the word dictionary.
Example answer:
I'd use dynamic programming with a boolean array dp of size len(s) + 1. dp[i] will be true if s[0...i-1] can be segmented. Iterate from j=0 to i-1; if dp[j] is true and the substring s[j:i] is in the dictionary, then dp[i] is true.
30. Container With Most Water
Why you might get asked this:
A clever two-pointer problem that requires understanding how to optimize area calculation by moving pointers strategically to maximize width and height.
How to answer:
Use two pointers, one at each end of the array. Calculate the area. Move the pointer pointing to the shorter line inward, as moving the taller line's pointer won't increase height but will decrease width.
Example answer:
I'd use a two-pointer approach, with left at the start and right at the end of the height array. In each step, calculate the area min(height[left], height[right]) * (right - left). Move the pointer pointing to the shorter line inward, aiming to potentially find a taller line to maximize the area.
Other Tips to Prepare for a LeetCode Interview
Mastering LeetCode Interview Questions requires consistent effort and strategic preparation. As renowned computer scientist Donald Knuth famously said, "An algorithm must be seen to be believed." This emphasizes the importance of truly understanding, not just memorizing, each problem's underlying logic. To excel, focus on a balanced approach: start by understanding core data structures and algorithms, then practice a wide range of problems, paying attention to different patterns like two-pointers, sliding window, DFS/BFS, and dynamic programming. A common pitfall is to only solve problems; equally important is practicing explaining your thought process clearly and concisely, simulating the actual interview environment.
Consider leveraging tools like Verve AI Interview Copilot (https://vervecopilot.com) for mock interviews and instant feedback, which can significantly refine your communication and problem-solving articulation. This AI-powered tool can help you identify areas for improvement in your explanations and coding approach. Remember, "The only way to do great work is to love what you do," as Steve Jobs said. Find enjoyment in the challenge of LeetCode Interview Questions, and your dedication will pay off. Beyond just coding, Verve AI Interview Copilot offers tailored advice on how to structure your answers and communicate your rationale effectively.
Frequently Asked Questions
Q1: What is the most important thing to focus on for LeetCode Interview Questions?
A1: Focus on understanding fundamental data structures and algorithms, and consistently practice common problem patterns like two-pointers and dynamic programming.
Q2: How many LeetCode Interview Questions should I solve?
A2: Aim for 150-200 common and medium-difficulty LeetCode problems to cover most interview scenarios. Quality over quantity is key.
Q3: Should I memorize solutions to LeetCode Interview Questions?
A3: No, focus on understanding the underlying logic and patterns. Memorizing solutions won't help with variations or new problems.
Q4: What if I get stuck on a LeetCode Interview Question?
A4: Try to break the problem into smaller parts, consider different data structures, or look for hints. Don't be afraid to analyze the solution afterward.
Q5: Are LeetCode Interview Questions similar across all companies?
A5: While core concepts are universal, some companies might lean more towards specific topics like graphs, DP, or system design related algorithmic problems.
