Prepare for coding interviews! Discover the top 30 most common LeetCode questions essential for your success. Master these for any tech interview.
Preparing for a software engineering interview often feels like navigating a dense forest of algorithms and data structures. LeetCode, an online platform offering coding challenges, has become the de facto standard for technical interview preparation. Excelling on LeetCode questions demonstrates not just your coding ability but also your foundational understanding of computer science principles and your problem-solving approach under pressure. This guide outlines the 30 most frequently asked LeetCode problems, providing insight into why they are common, how to approach them, and concise example answers to help you structure your preparation. Mastering these questions will significantly boost your confidence and performance in technical interviews, paving the way for success in your career.
What Are LeetCode Questions?
LeetCode questions are programming challenges designed to test a candidate's algorithmic thinking, data structure knowledge, and coding proficiency. These problems 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. Companies use these questions to assess how a candidate approaches complex problems, breaks them down, designs efficient solutions, and implements them correctly. The platform provides an online judge to test solutions against various test cases, offering immediate feedback on correctness and efficiency. Regularly practicing LeetCode questions helps to build a strong problem-solving muscle.
Why Do Interviewers Ask LeetCode Questions?
Interviewers ask LeetCode-style questions for several key reasons. Firstly, they assess a candidate's problem-solving skills; it's not just about getting the right answer, but demonstrating a logical, step-by-step approach to a challenge. Secondly, these questions evaluate your proficiency with fundamental data structures and algorithms, which are the building blocks of efficient software. Thirdly, they test your ability to write clean, correct, and optimized code, including handling edge cases and considering time/space complexity. Finally, technical interviews often simulate real-world debugging and collaborative problem-solving scenarios, allowing interviewers to observe your communication and thought process as you work through a problem. It reveals how you think under pressure.
Preview List
1. Two Sum
2. Add Two Numbers (Linked List)
3. Longest Substring Without Repeating Characters
4. Median of Two Sorted Arrays
5. Longest Palindromic Substring
6. ZigZag Conversion
7. Reverse Integer
8. String to Integer (atoi)
9. Palindrome Number
10. Container With Most Water
11. Integer to Roman
12. Roman to Integer
13. Longest Common Prefix
14. 3Sum
15. Remove Nth Node From End of List
16. Valid Parentheses
17. Merge Two Sorted Lists
18. Generate Parentheses
19. Merge Intervals
20. Unique Paths
21. Minimum Path Sum
22. Climbing Stairs
23. Set Matrix Zeroes
24. Search in Rotated Sorted Array
25. Find First and Last Position of Element in Sorted Array
26. Valid Sudoku
27. Count and Say
28. Combination Sum
29. Word Search
30. Maximum Subarray
1. Two Sum
Why you might get asked this:
This problem is a fundamental test of array manipulation and hash map usage, demonstrating your ability to optimize search operations from O(N^2) to O(N).
How to answer:
Use a hash map to store numbers and their indices. For each number, check if the complement (target - current_num) exists in the map.
Example answer:
"I would iterate through the array, for each number, calculate its complement needed to reach the target. I'd then check if this complement already exists in a hash map. If so, I return the current index and the index of the complement. If not, I add the current number and its index to the map. This uses O(N) time complexity."
2. Add Two Numbers (Linked List)
Why you might get asked this:
Tests your understanding of linked lists, pointer manipulation, and handling carry-overs in arithmetic operations, crucial for data structure fundamentals.
How to answer:
Iterate through both lists simultaneously, adding corresponding digits and a carry. Create new nodes for the sum's digits, linking them to form a new list.
Example answer:
"I'll use a dummy head node for the result list to simplify initial node creation. I'll iterate using pointers for both input lists and the result list. At each step, I add digits and any carry from the previous step, calculate the current digit (sum % 10), and the new carry (sum / 10). I create a new node with the current digit and move all pointers forward. Remember to handle a final carry."
3. Longest Substring Without Repeating Characters
Why you might get asked this:
Assesses string manipulation, sliding window technique, and efficient character tracking, often using a hash set or array for O(1) lookups.
How to answer:
Employ a sliding window. Use a hash set to track characters within the current window. Expand the window while characters are unique, shrink when a duplicate is found.
Example answer:
"I'll use a sliding window approach with two pointers, `left` and `right`. A hash set will store characters within the current window `s[left...right]`. As `right` moves, if `s[right]` is not in the set, add it and update max length. If it is, remove `s[left]` from the set and increment `left` until the duplicate is resolved. This ensures O(N) time."
4. Median of Two Sorted Arrays
Why you might get asked this:
A challenging problem that tests binary search, partitioning arrays, and understanding edge cases, demonstrating advanced algorithmic skills.
How to answer:
The optimal solution involves a modified binary search to find the correct partition point in the shorter array that divides both arrays into two halves with equal element counts.
Example answer:
"This requires finding the k-th element in two sorted arrays, specifically the (totallen+1)/2-th and (totallen+2)/2-th elements. I'll apply binary search on the smaller array to find an optimal split point such that elements to the left of the split in both arrays are less than or equal to elements to the right. The median is then derived from the max of left parts and min of right parts."
5. Longest Palindromic Substring
Why you might get asked this:
Tests dynamic programming or expand-around-center strategy, focusing on string properties and optimization for palindrome detection.
How to answer:
Iterate through each character, treating it as the center of a potential palindrome (both odd and even length). Expand outwards to find the longest palindrome.
Example answer:
"I'll use the 'expand around center' approach. For each character `i` in the string, I'll consider two cases: `i` as the center of an odd-length palindrome (e.g., 'aba') and `i` and `i+1` as the center of an even-length palindrome (e.g., 'abba'). I'll expand outwards from these centers, checking for character equality. I'll keep track of the start and end of the longest palindrome found so far."
6. ZigZag Conversion
Why you might get asked this:
Assesses string manipulation and pattern recognition, requiring careful indexing or simulation of the zigzag pattern.
How to answer:
Create a list of strings for each row. Iterate through the input string, appending characters to the correct row based on a changing direction.
Example answer:
"I'll simulate the zigzag pattern by using a list of strings, one for each row. I'll maintain a `currentrow` index and a `direction` (up or down). I iterate through the input string, appending each character to `rows[currentrow]`. If `current_row` reaches the first or last row, I reverse the `direction`. Finally, I join all strings in the `rows` list."
7. Reverse Integer
Why you might get asked this:
A common problem that checks integer manipulation, handling edge cases like overflow, and basic arithmetic operations.
How to answer:
Extract digits one by one using modulo and integer division. Reconstruct the reversed number, checking for overflow at each step before appending the digit.
Example answer:
"I'll initialize `reversedint` to 0. In a loop, I extract the last digit using `x % 10`, then update `x` using `x / 10`. Before adding the digit to `reversedint * 10`, I'll check if `reversedint` would exceed `INTMAX/10` or `INTMIN/10` to prevent overflow. If overflow is detected, return 0. Finally, return `reversedint`."
8. String to Integer (atoi)
Why you might get asked this:
Tests robust string parsing, edge case handling (whitespace, sign, overflow, invalid characters), and careful state management.
How to answer:
Trim whitespace, determine sign, iterate through digits. Convert characters to integers, and check for overflow at each step. Stop at the first non-digit character.
Example answer:
"First, I'd skip leading whitespace. Then, identify the sign (+/-) or assume positive. Iterate through subsequent characters, converting digits to an integer. Crucially, before adding each digit, I'll check for potential integer overflow against `INTMAX` and `INTMIN`. If a non-digit character is encountered or overflow occurs, stop processing and return the current result."
9. Palindrome Number
Why you might get asked this:
A simpler number manipulation problem to check basic understanding of arithmetic and reversing numbers without converting to strings.
How to answer:
Reverse half of the number and compare it to the first half. Handle negative numbers and numbers ending in zero (except 0 itself).
Example answer:
"I'll handle negative numbers and numbers ending in zero (except 0) by returning `false` immediately. For positive numbers, I'll build a `revertednumber` by repeatedly taking the last digit of the original number and appending it. I'll stop when `x` (original number) is less than or equal to `revertednumber`. Then, I check if `x` equals `revertednumber` or `revertednumber / 10` for odd-length numbers."
10. Container With Most Water
Why you might get asked this:
An excellent problem for demonstrating the two-pointer technique and optimizing a quadratic solution to linear time.
How to answer:
Use two pointers, one at each end of the array. Move the pointer associated with the shorter line inwards, maximizing the area at each step.
Example answer:
"I'll use two pointers, `left` at the beginning and `right` at the end of the `height` array. The current area is `min(height[left], height[right]) * (right - left)`. I update the maximum area found. To maximize, I move the pointer pointing to the shorter line inward, as moving the taller line's pointer won't increase the minimum height and thus won't necessarily increase the area. Continue until pointers cross."
11. Integer to Roman
Why you might get asked this:
Tests understanding of number systems, greedy algorithms, and careful mapping, often using pre-defined value-symbol pairs.
How to answer:
Use a greedy approach. Define a list of (value, symbol) pairs in descending order. Iteratively subtract the largest possible value and append its symbol.
Example answer:
"I'll use a greedy approach with two arrays: `values` (e.g., 1000, 900, 500...) and `symbols` (e.g., 'M', 'CM', 'D'...). I iterate through these arrays from largest value to smallest. While the input `num` is greater than or equal to the current `value`, I append the corresponding `symbol` to my result string and subtract `value` from `num`. This ensures the longest possible symbol is used first."
12. Roman to Integer
Why you might get asked this:
Similar to Integer to Roman, but tests parsing strings and handling special Roman numeral subtraction rules (e.g., IV, IX).
How to answer:
Iterate through the Roman numeral string. Use a hash map for symbol values. If a symbol's value is less than the next, subtract it; otherwise, add it.
Example answer:
"I'll use a hash map to store the integer value for each Roman numeral character (e.g., 'I': 1, 'V': 5). I iterate through the input string from left to right. If the current character's value is less than the next character's value (e.g., 'IV'), it signifies a subtraction, so I subtract the current value. Otherwise, I add it. The total sum is the result."
13. Longest Common Prefix
Why you might get asked this:
A fundamental string problem testing basic string comparison and iterative refinement.
How to answer:
Start with the first string as the prefix. Iterate through remaining strings, shortening the prefix until it matches the beginning of each string.
Example answer:
"I'll initialize the `longestcommonprefix` with the first string in the array. Then, I iterate through the rest of the strings. For each string, I continuously shorten the `longestcommonprefix` from its end until it is a prefix of the current string. If `longestcommonprefix` becomes empty at any point, I return an empty string. Finally, return the `longestcommonprefix`."
14. 3Sum
Why you might get asked this:
A classic problem combining sorting and the two-pointer technique to solve a three-sum variation efficiently, managing duplicates.
How to answer:
Sort the array. Iterate through each number, then use two pointers on the remaining array to find pairs that sum to the negative of the current number. Handle duplicates.
Example answer:
"First, I'll sort the input array. Then, I'll iterate through the sorted array with a pointer `i`. To avoid duplicate triplets, I'll skip if `nums[i]` is the same as `nums[i-1]`. For each `nums[i]`, I'll use a two-pointer approach (`left` and `right`) on the subarray `nums[i+1:]` to find two numbers that sum to `-nums[i]`. I'll manage `left` and `right` pointers and skip duplicates while moving them."
15. Remove Nth Node From End of List
Why you might get asked this:
Tests linked list traversal, manipulation, and the use of two pointers (fast/slow or two-pass) to locate a specific node.
How to answer:
Use two pointers: `first` and `second`. Advance `first` `n` steps ahead. Then, advance both pointers until `first` reaches the end. `second` will then be at the node before the one to remove.
Example answer:
"I'll use a dummy node attached to the head to simplify edge cases. Then, two pointers, `first` and `second`, both starting at the dummy node. I advance `first` `n+1` steps. Now, `first` is `n+1` nodes ahead of `second`. Then, I move both `first` and `second` one step at a time until `first` reaches the end of the list. At this point, `second` will be at the node just before the `n`th node from the end. I then update `second.next` to `second.next.next`."
16. Valid Parentheses
Why you might get asked this:
A classic stack problem. Tests understanding of LIFO data structures and matching pairs, fundamental for parsing and compiler design.
How to answer:
Use a stack. Push opening brackets onto the stack. When a closing bracket appears, pop from the stack and check for a match.
Example answer:
"I'll use a stack to keep track of opening brackets. I'll iterate through the input string. If I encounter an opening bracket ('(', '{', '['), I push it onto the stack. If a closing bracket is found, I check if the stack is empty or if the top of the stack does not match the corresponding opening bracket. If either is true, it's invalid. Otherwise, I pop from the stack. After iterating, if the stack is empty, it's valid."
17. Merge Two Sorted Lists
Why you might get asked this:
A basic linked list problem. Tests pointer manipulation, iteration, and combining sorted data structures, often used as a sub-problem.
How to answer:
Create a dummy head node for the merged list. Iterate through both lists, always appending the smaller of the current nodes to the merged list.
Example answer:
"I'll create a dummy node to serve as the head of the merged list, and a `current` pointer to build the list. While both input lists (`l1`, `l2`) have nodes, I compare their values. I append the node with the smaller value to `current.next` and advance that list's pointer. After one list is exhausted, I append the remaining nodes of the other list. Finally, return `dummy.next`."
18. Generate Parentheses
Why you might get asked this:
A common recursion/backtracking problem. Tests combinatorial generation and constraints (validity rules).
How to answer:
Use backtracking. Maintain counts for open and closed parentheses. Add open if `open < n`, add close if `close < open`. Base case when both are `n`.
Example answer:
"I'll use a recursive backtracking approach. My recursive function will take `opencount`, `closecount`, and the current string `currentcombination`. If `opencount < n`, I can add an open parenthesis and recurse. If `closecount < opencount`, I can add a close parenthesis and recurse. The base case is when `opencount == n` and `closecount == n`, at which point I add `current_combination` to my results list."
19. Merge Intervals
Why you might get asked this:
Tests array manipulation, sorting, and interval management. Useful for scheduling, time management, and range-based problems.
How to answer:
Sort intervals by start time. Iterate through sorted intervals, merging current with next if they overlap.
Example answer:
"First, I'll sort the input `intervals` array based on the start time of each interval. Then, I'll initialize a `mergedintervals` list with the first interval. I iterate from the second interval onwards. If the current interval overlaps with the last interval in `mergedintervals` (i.e., its start is less than or equal to the end of the last merged interval), I update the end of the last merged interval to be the maximum of its current end and the current interval's end. Otherwise, I add the current interval to `merged_intervals`."
20. Unique Paths
Why you might get asked this:
A classic dynamic programming problem. Tests grid traversal, path counting, and memoization/tabulation.
How to answer:
Use dynamic programming. The number of unique paths to a cell (i, j) is the sum of paths from (i-1, j) and (i, j-1).
Example answer:
"This is a dynamic programming problem. I'll create a 2D `dp` array of size `m x n`. `dp[i][j]` will store the number of unique paths to reach cell `(i, j)`. The base cases are `dp[0][j] = 1` and `dp[i][0] = 1` (since there's only one way to reach any cell in the first row/column). For `(i, j)` where `i,j > 0`, `dp[i][j] = dp[i-1][j] + dp[i][j-1]`. The final answer is `dp[m-1][n-1]`."
21. Minimum Path Sum
Why you might get asked this:
Another dynamic programming problem on grids. Tests finding optimal paths and cost accumulation.
How to answer:
Dynamic programming. `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])`.
Example answer:
"I'll use dynamic programming. I can either modify the input `grid` in-place or create a new `dp` table of the same dimensions. `dp[i][j]` will represent the minimum path sum to reach cell `(i, j)`. I initialize the first row and column. For `dp[i][j]`, the value is `grid[i][j]` plus the minimum of `dp[i-1][j]` (from above) and `dp[i][j-1]` (from left). The result is `dp[m-1][n-1]`."
22. Climbing Stairs
Why you might get asked this:
A simple dynamic programming/Fibonacci-sequence problem, often used as an introduction to DP concepts.
How to answer:
This problem is a classic Fibonacci sequence. The number of ways to climb `n` stairs is the sum of ways to climb `n-1` and `n-2` stairs.
Example answer:
"This problem can be solved using dynamic programming or by recognizing the Fibonacci sequence. To reach step `n`, you can either come from step `n-1` (by taking 1 step) or step `n-2` (by taking 2 steps). So, `dp[n] = dp[n-1] + dp[n-2]`. Base cases are `dp[1]=1` and `dp[2]=2`. I'll use an array to store these values iteratively up to `n`."
23. Set Matrix Zeroes
Why you might get asked this:
Tests in-place matrix modification and careful state tracking to avoid cascading changes, often using first row/column as markers.
How to answer:
Use the first row and first column as markers to record if a row or column needs to be zeroed. Handle the first cell `matrix[0][0]` specially.
Example answer:
"I'll use the first row and first column of the matrix itself to mark which rows and columns need to be zeroed. I'll need a boolean flag for `iscol0_zero` to handle `matrix[0][0]`'s dual role. First pass: iterate and mark. If `matrix[i][j]` is zero, set `matrix[i][0] = 0` and `matrix[0][j] = 0`. Second pass: use these marks to zero out the rest of the matrix. Third, zero `matrix[0][0]` row/col based on the flag."
24. Search in Rotated Sorted Array
Why you might get asked this:
A challenging variation of binary search. Tests ability to adapt standard algorithms to complex data structures and edge cases.
How to answer:
Apply modified binary search. Determine which half (left or right) is sorted and if the target lies within that sorted half.
Example answer:
"I'll use a modified binary search. In each step, I find the middle element. One half of the array will always be sorted. I determine which half is sorted (`nums[low] <= nums[mid]` means left half sorted). Then, I check if the target falls within that sorted half. If it does, I search in that half; otherwise, I search in the other half. This ensures the search space is halved in each step, maintaining O(logN) complexity."
25. Find First and Last Position of Element in Sorted Array
Why you might get asked this:
Tests binary search robustness, requiring two separate binary searches or modifications to find boundary elements efficiently.
How to answer:
Perform two binary searches: one to find the leftmost occurrence and another to find the rightmost occurrence of the target.
Example answer:
"I'll use a modified binary search. First, to find the leftmost occurrence, I'll search for the target, but if `nums[mid] == target`, I'll continue searching in the left half to see if an even earlier `target` exists. Second, to find the rightmost occurrence, I'll search for the target, but if `nums[mid] == target`, I'll continue searching in the right half. If the target isn't found in either search, return `[-1, -1]`."
26. Valid Sudoku
Why you might get asked this:
Tests grid traversal, hash set usage, and careful constraint checking for rows, columns, and 3x3 sub-boxes.
How to answer:
Use hash sets (or boolean arrays) to keep track of seen numbers for each row, column, and 3x3 sub-box. Iterate through the grid.
Example answer:
"I'll iterate through the 9x9 grid. For each cell `(r, c)`, if it contains a digit (not '.'), I'll check its validity. I'll use three sets of hash sets: one for rows (`rowsets[r]`), one for columns (`colsets[c]`), and one for 3x3 boxes (`boxsets[boxidx]`). The `box_idx` can be calculated as `(r / 3) * 3 + (c / 3)`. If the digit is already in any of these relevant sets, it's invalid. Otherwise, add the digit to all three sets."
27. Count and Say
Why you might get asked this:
Tests string manipulation, pattern generation, and careful iteration/string building, often solved recursively or iteratively.
How to answer:
Generate the sequence iteratively. For each term, iterate through the previous term, counting consecutive digits and building the next string.
Example answer:
"This problem involves generating a sequence based on the previous term. I'll start with `s = '1'` for `n = 1`. For `n > 1`, I'll iterate through the previous string `s`. I'll use a counter for consecutive identical characters. When the character changes or the string ends, I append the `count` and the `character` to a new string, which then becomes the `s` for the next iteration. This is done iteratively until `n` is reached."
28. Combination Sum
Why you might get asked this:
A classic backtracking problem with a twist: elements can be reused. Tests combinatorial search and recursion.
How to answer:
Use backtracking. At each step, either include the current candidate (and potentially reuse it) or move to the next candidate. Sum must equal target.
Example answer:
"I'll use a recursive backtracking function. The function will take the remaining `target`, the `currentcombination` list, and the `startindex` to avoid duplicates. For each candidate number from `startindex` onwards, if it's less than or equal to the `target`, I add it to `currentcombination`, subtract it from `target`, and recurse with the same `start_index` (allowing reuse). After recursion, I backtrack by removing the number. If `target` becomes 0, a valid combination is found."
29. Word Search
Why you might get asked this:
Tests depth-first search (DFS) or backtracking on a grid. Crucial for understanding graph traversal and pathfinding.
How to answer:
Iterate through the grid. If a cell matches the first letter of the word, start a DFS from that cell. Mark visited cells to avoid cycles.
Example answer:
"I'll iterate through each cell `(r, c)` in the `board`. If `board[r][c]` matches the first character of the `word`, I initiate a DFS (Depth First Search) from that cell. The DFS function takes `(r, c, k)` where `k` is the index of the character we're currently trying to match in `word`. In DFS, I mark the current cell as visited, then try all four adjacent directions. If any recursive call returns true, it's found. Backtrack by unmarking the cell after returning."
30. Maximum Subarray
Why you might get asked this:
A classic dynamic programming problem (Kadane's algorithm). Tests efficient array processing and optimization.
How to answer:
Use Kadane's algorithm: keep track of the current maximum sum ending at the current position, and the overall maximum sum found so far.
Example answer:
"I'll use Kadane's algorithm. I maintain two variables: `currentmax` (the maximum sum of a subarray ending at the current position) and `globalmax` (the maximum sum found so far across all subarrays). I iterate through the array. For each number, `currentmax = max(num, currentmax + num)`. Then, `globalmax = max(globalmax, currentmax)`. Initialize `currentmax` and `globalmax` to `nums[0]`. The final `globalmax` is the answer."
Other Tips to Prepare for a LeetCode Interview
Effective LeetCode preparation extends beyond just solving problems. "The best way to learn is by doing," as famously quoted by many educators. Start early and be consistent with your practice. Don't just memorize solutions; understand the underlying algorithms and data structures. When you encounter a new problem, first try to solve it on your own, starting with a brute-force approach, then optimizing it. Discuss solutions with peers, explain your thought process out loud, and consider various edge cases. This active learning approach builds true understanding.
Remember, communication is as important as coding. Be ready to articulate your thought process, explain your approach, and analyze the time and space complexity of your solution. Tools like Verve AI Interview Copilot can be incredibly beneficial here, offering mock interviews and real-time feedback on your verbal explanations and problem-solving strategies. It's like having a personal coach. "Success is not final, failure is not fatal: it is the courage to continue that counts." Leverage resources such as Verve AI Interview Copilot (https://vervecopilot.com) to refine your communication and confidence. Regular practice, combined with targeted feedback from platforms like Verve AI Interview Copilot, can significantly enhance your interview readiness, transforming daunting challenges into manageable steps towards your dream job.
Frequently Asked Questions
Q1: How much time should I spend on each LeetCode problem? A1: Aim for 30-45 minutes per problem for initial attempts. If stuck, review hints or solutions, then re-attempt without looking.
Q2: Should I focus on specific LeetCode difficulties? A2: Start with easy and medium problems. Once comfortable, move to hard problems, especially those common in your target companies.
Q3: Is it better to memorize solutions or understand concepts? A3: Always prioritize understanding concepts. Memorizing solutions for 30 questions is less effective than mastering underlying algorithmic patterns.
Q4: How important are time and space complexity? A4: Very important. Always analyze your solution's complexity and try to optimize. It shows a deep understanding of efficient coding.
Q5: What if I get stuck on a problem for a long time? A5: Don't spend more than an hour stuck. Look at a hint or solution, understand it, then try to implement it yourself without peeking.
Q6: Should I use a specific programming language for LeetCode? A6: Use a language you are most comfortable with, typically Python, Java, or C++. Consistency helps you focus on logic.
Kent McAllister
Career Advisor
