Navigating the competitive landscape of tech interviews, especially at companies like Square, requires a strategic approach. Square, renowned for its innovative financial technology and robust engineering culture, emphasizes strong problem-solving skills, algorithmic prowess, and system design acumen. Aspiring candidates often turn to LeetCode to hone these abilities, making "Square LeetCode" a common phrase among those preparing for the rigorous interview process. This guide compiles a list of frequently encountered problem types and provides actionable advice on how to approach them, ensuring you are well-equipped to demonstrate your technical capabilities. Success in a Square interview isn't just about knowing the answer; it's about showcasing your thought process, efficiency, and ability to handle complex challenges under pressure.
What Are Square LeetCode Questions?
Square LeetCode questions are a combination of algorithmic challenges commonly found on platforms like LeetCode, tailored or selected to assess skills relevant to Square's engineering needs. These typically involve data structures (arrays, linked lists, trees, graphs, hash maps) and algorithms (sorting, searching, dynamic programming, recursion, greedy algorithms). Given Square's focus on scalable and reliable financial systems, questions might also implicitly or explicitly touch on concepts like concurrency, transaction processing, or distributed systems, even within an algorithmic problem. The goal is not just to solve the problem but to demonstrate an understanding of time and space complexity, edge cases, and the ability to write clean, efficient, and maintainable code. They gauge your fundamental computer science knowledge and practical coding skills.
Why Do Interviewers Ask Square LeetCode Questions?
Interviewers at Square ask these types of questions for several key reasons. Firstly, they serve as a robust filter to assess a candidate's core problem-solving abilities and logical thinking. The ability to break down a complex problem into manageable parts, identify efficient algorithms, and handle various constraints is crucial for software engineers. Secondly, these questions evaluate your proficiency in fundamental data structures and algorithms, which are the building blocks for any sophisticated software system, especially in a performance-critical environment like financial technology. Lastly, they help interviewers understand how you approach and debug problems under pressure, communicate your thought process, and consider trade-offs (e.g., between time and space complexity). This comprehensive evaluation ensures that only candidates with strong foundational skills and practical application knowledge join Square's engineering teams.
Two Sum
Add Two Numbers
Longest Substring Without Repeating Characters
Median of Two Sorted Arrays
Longest Palindromic Substring
Reverse Integer
String to Integer (atoi)
Container With Most Water
3Sum
Remove Nth Node From End of List
Valid Parentheses
Merge Two Sorted Lists
Generate Parentheses
Merge k Sorted Lists
Swap Nodes in Pairs
Reverse Nodes in k-Group
Remove Duplicates from Sorted Array
Remove Element
Implement strStr()
Divide Two Integers
Substring with Concatenation of All Words
Next Permutation
Search in Rotated Sorted Array
Find First and Last Position of Element in Sorted Array
Combination Sum
Trapping Rain Water
Permutations
Rotate Image
Group Anagrams
Maximum Subarray
Preview List
1. Two Sum
Why you might get asked this:
Tests basic array manipulation, hash map usage, and efficient search. It's a common warm-up to check fundamental data structure knowledge and time complexity awareness.
How to answer:
Use a hash map to store numbers and their indices. For each number, check if its complement (target - current_num) exists in the map.
Example answer:
Iterate through the array, storing each number and its index in a hash map. For each num, calculate complement = target - num. If complement is in the map and not the current num itself, return the indices. Otherwise, add num to the map.
2. Add Two Numbers
Why you might get asked this:
Assesses linked list manipulation, handling carry-overs, and constructing new lists. It's a fundamental problem for understanding linked list operations.
How to answer:
Simulate manual addition with a dummy head node. Iterate through both lists, summing digits and handling carries. Create new nodes for the result.
Example answer:
Initialize a dummy head and current node for the result list. Iterate while either input list has nodes or there's a carry. Sum current digits plus carry, update carry, and create a new node with the sum's unit digit.
3. Longest Substring Without Repeating Characters
Why you might get asked this:
Evaluates string manipulation, sliding window technique, and efficient character tracking, often using hash sets or maps.
How to answer:
Use a sliding window approach with a hash set to track characters in the current window. Expand the window, shrinking it from the left if a repeat is found.
Example answer:
Use two pointers, left and right, defining a window. Maintain a hash set of characters within the window. As right moves, add s[right] to the set. If s[right] is already present, remove s[left] and increment left until unique. Update max length.
4. Median of Two Sorted Arrays
Why you might get asked this:
A classic hard problem testing binary search on partitioned arrays. It assesses understanding of finding k-th elements in merged sorted data.
How to answer:
Find the k-th element in two sorted arrays using a modified binary search. This avoids merging and achieves O(log(m+n)) complexity.
Example answer:
The problem can be reduced to finding the k-th smallest element. Use binary search on the smaller array to find a partition point that divides both arrays such that elements to the left are smaller and elements to the right are larger.
5. Longest Palindromic Substring
Why you might get asked this:
Tests dynamic programming or expand-around-center strategy. It explores string properties and optimization for pattern recognition.
How to answer:
Iterate through each character as a potential center (for odd length palindromes) or between two characters (for even length). Expand outwards to find the longest palindrome.
Example answer:
For each character i in the string, consider it as the center of a palindrome (odd length). Also consider i and i+1 as centers (even length). Expand outwards from these centers as long as characters match, keeping track of the longest found.
6. Reverse Integer
Why you might get asked this:
Assesses integer manipulation, handling edge cases like overflow, and understanding numeric representation without strings.
How to answer:
Extract digits one by one using modulo and division. Build the reversed number, checking for overflow before each addition.
Example answer:
Initialize rev = 0. In a loop, extract the last digit using x % 10. Before multiplying rev by 10 and adding the digit, check if rev exceeds INTMAX/10 or INTMIN/10 to prevent overflow. Then, update rev = rev * 10 + digit and x = x // 10.
7. String to Integer (atoi)
Why you might get asked this:
Checks parsing logic, handling various input formats (whitespace, signs), and robust error/overflow checking for numeric conversions.
How to answer:
Trim whitespace, handle optional sign, then iterate through digits. Convert characters to integers and accumulate, carefully checking for overflow at each step.
Example answer:
First, skip leading whitespace. Determine the sign. Iterate through digits, converting them and accumulating the result. Before each addition, verify if adding the next digit would cause an integer overflow, returning INTMAX or INTMIN if so.
8. Container With Most Water
Why you might get asked this:
A two-pointer problem testing greedy strategy and optimizing area calculation by moving the shorter line.
How to answer:
Use two pointers, one at each end. Calculate area, then move the pointer pointing to the shorter line inward to potentially find a larger height.
Example answer:
Initialize left = 0, right = n-1, and maxarea = 0. While left < right: calculate current area min(height[left], height[right]) * (right - left). Update maxarea. If height[left] < height[right], increment left, else decrement right.
9. 3Sum
Why you might get asked this:
Tests array manipulation, sorting, and multi-pointer techniques for finding combinations that sum to a target, often involving duplicate handling.
How to answer:
Sort the array. Iterate through each element, using two pointers on the remaining part to find the other two elements that sum to the negative of the current element. Handle duplicates.
Example answer:
Sort the input array nums. Iterate i from 0 to n-3. Skip duplicates for nums[i]. Set left = i+1, right = n-1. While left < right, calculate sum = nums[i] + nums[left] + nums[right]. Adjust left or right based on sum relative to 0, skipping duplicates.
10. Remove Nth Node From End of List
Why you might get asked this:
Assesses linked list traversal and manipulating pointers effectively, often using two-pointer techniques for offset.
How to answer:
Use two pointers: fast moves n steps ahead, then slow and fast move together. When fast reaches the end, slow is at the node before the one to be removed.
Example answer:
Create a dummy node pointing to the head. Initialize fast and slow pointers to the dummy node. Move fast n steps forward. Then, move slow and fast one step at a time until fast reaches the end. slow will now be positioned just before the node to remove.
11. Valid Parentheses
Why you might get asked this:
Tests stack usage for matching pairs and understanding parsing rules. Fundamental for expressions or code parsing.
How to answer:
Use a stack. Push opening brackets. When a closing bracket appears, pop from the stack and check for a match. Stack must be empty at the end.
Example answer:
Initialize an empty 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. Else, pop. After loop, if stack is empty, it's valid.
12. Merge Two Sorted Lists
Why you might get asked this:
A basic linked list problem assessing recursive or iterative merging. Essential for understanding list manipulation.
How to answer:
Create a dummy head. Iterate, comparing nodes from both lists and appending the smaller one to the merged list. Handle remaining nodes.
Example answer:
Initialize a dummy head node and a current pointer. While both lists have nodes, compare list1.val and list2.val. Append the smaller node to current.next and advance that list's pointer. After the loop, append any remaining nodes.
13. Generate Parentheses
Why you might get asked this:
Explores recursion/backtracking and understanding constraints (validity of parentheses) to generate combinations.
How to answer:
Use backtracking. Maintain counts of open and close parentheses. Add '(' if open < n. Add ')' if close < open. Recurse until both counts are n.
Example answer:
Implement a recursive backtrack(currentstring, opencount, closecount) function. Base case: if currentstring length is 2*n, add to result. Recurse by adding '(' if opencount < n, and by adding ')' if closecount < open_count.
14. Merge k Sorted Lists
Why you might get asked this:
Tests understanding of data structures like priority queues (min-heap) or divide-and-conquer strategy for merging multiple lists efficiently.
How to answer:
Use a min-heap to keep track of the smallest current node from each list. Repeatedly extract min, add to result, and insert next node from extracted list.
Example answer:
Insert the first node of each non-empty list into a min-heap. Initialize a dummy head. While the heap is not empty, extract the minimum node, append it to the merged list, and if that node has a next, insert it into the heap.
15. Swap Nodes in Pairs
Why you might get asked this:
Focuses on in-place linked list manipulation, requiring careful pointer updates.
How to answer:
Iterate through the list, swapping nodes in pairs. Keep track of the previous node to connect the swapped pair.
Example answer:
Use a dummy node pointing to the head. Iterate while current.next and current.next.next exist. Store the two nodes to be swapped. Perform the swap by updating prev.next, first.next, and second.next. Advance prev.
16. Reverse Nodes in k-Group
Why you might get asked this:
A more advanced linked list problem combining reversal with group-based processing, requiring precise pointer handling.
How to answer:
Recursively reverse each k-group. Connect the reversed group to the next reversed group. Handle remaining nodes if less than k.
Example answer:
Determine if there are at least k nodes remaining. If so, reverse the first k nodes. Recursively call the function for the rest of the list. Connect the tail of the reversed k-group to the head of the recursively reversed list.
17. Remove Duplicates from Sorted Array
Why you might get asked this:
Tests in-place array modification using two pointers. Focuses on efficient space usage.
How to answer:
Use two pointers: i for iterating, j for placing unique elements. If nums[i] is unique, copy it to nums[j] and increment j.
Example answer:
Initialize insertindex = 1. Iterate i from 1 to n-1. If nums[i] is different from nums[i-1], then nums[insertindex] = nums[i] and increment insertindex. The insertindex will be the new length.
18. Remove Element
Why you might get asked this:
Similar to removing duplicates, tests in-place array modification and handling specific value removal.
How to answer:
Use two pointers. One pointer iterates, the other places elements not equal to val. Alternatively, swap elements equal to val with the last element.
Example answer:
Initialize k = 0. Iterate through the array with pointer i. If nums[i] is not equal to val, then nums[k] = nums[i] and increment k. The final value of k is the new length.
19. Implement strStr()
Why you might get asked this:
Focuses on string searching algorithms (naive or KMP/Rabin-Karp for optimization). Checks understanding of substrings.
How to answer:
Use a brute-force approach, iterating through haystack and checking if a substring matches needle. For efficiency, consider KMP algorithm.
Example answer:
Iterate i from 0 to len(haystack) - len(needle). For each i, check if haystack[i:i+len(needle)] equals needle. If a match is found, return i. If no match after checks, return -1.
20. Divide Two Integers
Why you might get asked this:
Tests bit manipulation and handling division without multiplication/division/modulo operators, focusing on edge cases and integer limits.
How to answer:
Use bit shifts to subtract multiples of the divisor from the dividend. Handle signs and overflow explicitly.
Example answer:
Handle signs first. Convert both to positive for calculation. Use bitwise left shifts (<<) to find the largest multiple of divisor that can be subtracted from dividend. Repeatedly subtract this shifted divisor from dividend and add 1 << shift to the quotient.
21. Substring with Concatenation of All Words
Why you might get asked this:
Involves string parsing, hash maps for frequency counting, and managing a sliding window over multiple fixed-length words.
How to answer:
Use a sliding window of size totalwordslength. Inside the window, check if the words match the frequency in the given words list using hash maps.
Example answer:
Count word frequencies in words into a map wordcounts. Iterate through s starting points. For each, extract wordlength substrings. Count frequencies of extracted words in a temporary map. If temporary map matches word_counts, add start index to result.
22. Next Permutation
Why you might get asked this:
Tests array manipulation, understanding permutations, and finding the lexicographically next arrangement in-place.
How to answer:
Find the first decreasing element from the right. Swap it with the smallest element to its right that's greater. Reverse the suffix after the swap.
Example answer:
Find the largest index i such that nums[i] < nums[i+1]. If no such index exists, reverse the array. Otherwise, find the largest index j such that nums[j] > nums[i]. Swap nums[i] and nums[j]. Reverse the subarray nums[i+1:].
23. Search in Rotated Sorted Array
Why you might get asked this:
Classic binary search variant requiring careful handling of rotation point and determining which half is sorted.
How to answer:
Perform a modified binary search. In each step, determine which half of the array is sorted and check if the target falls into that sorted range.
Example answer:
Apply binary search. If nums[mid] is greater than or equal to nums[left], the left half is sorted. Check if target is in [nums[left], nums[mid]]. Otherwise, the right half [nums[mid], nums[right]] is sorted; check target there.
24. Find First and Last Position of Element in Sorted Array
Why you might get asked this:
Tests binary search application, specifically for finding boundaries (first and last occurrences) of an element in a sorted array.
How to answer:
Use two separate binary searches: one to find the first occurrence and another to find the last occurrence of the target.
Example answer:
Implement a helper binarySearch(nums, target, findfirst) function. If findfirst is true, adjust search to find leftmost valid index. If false, adjust to find rightmost valid index. Call helper twice and combine results.
25. Combination Sum
Why you might get asked this:
Explores backtracking for generating combinations where elements can be reused. Important for understanding combinatorial problems.
How to answer:
Use a recursive backtracking function. At each step, either include the current candidate (and recurse with the same candidate) or exclude it and move to the next candidate.
Example answer:
Define a recursive backtrack(remainingtarget, currentcombination, startindex) function. If remainingtarget is 0, add currentcombination to results. If remainingtarget < 0, return. Iterate i from startindex to n-1, add candidates[i] to currentcombination, recurse, then backtrack (remove candidates[i]).
26. Trapping Rain Water
Why you might get asked this:
A challenging array problem often solved with dynamic programming or two pointers, testing spatial reasoning and optimized calculation.
How to answer:
Dynamic programming: compute maxleft and maxright arrays for each position. Then sum min(maxleft[i], maxright[i]) - height[i]. Two pointers: move pointers from ends, summing water based on the shorter side.
Example answer:
Initialize left=0, right=n-1, maxleft=0, maxright=0, water=0. While left <= right: if height[left] <= height[right], maxleft = max(maxleft, height[left]), water += maxleft - height[left], left++. Else, maxright = max(maxright, height[right]), water += maxright - height[right], right--.
27. Permutations
Why you might get asked this:
Another core backtracking problem that requires generating all possible orderings of elements without repetition.
How to answer:
Use a recursive backtracking function. For each position, try placing every available number. Mark numbers as used and unmark after recursion.
Example answer:
Define backtrack(currentpermutation, availablenumbers). Base case: if currentpermutation is full, add to results. Iterate through availablenumbers. For each num, add it to currentpermutation, remove from availablenumbers, recurse, then backtrack (add num back).
28. Rotate Image
Why you might get asked this:
Tests in-place 2D array manipulation and spatial transformation. Can be solved by layers or by transpose then reverse.
How to answer:
Rotate in layers from outer to inner. For each layer, swap four elements in a cycle. Alternatively, transpose the matrix then reverse each row.
Example answer:
Option 1: Transpose the matrix (swap matrix[i][j] with matrix[j][i]). Then, reverse each row of the transposed matrix. This rotates the image 90 degrees clockwise in-place.
29. Group Anagrams
Why you might get asked this:
Tests string manipulation and hash map usage for grouping based on a canonical form (sorted string or character counts).
How to answer:
For each word, generate a unique key (e.g., sorted string or character count array/tuple). Store words in a hash map where keys are these canonical forms and values are lists of anagrams.
Example answer:
Create a hash map. Iterate through each string s in the input. Sort s to get its canonical form (e.g., "aet"). Use this sorted string as the key in the hash map. Append s to the list associated with that key. Return all values from the hash map.
30. Maximum Subarray
Why you might get asked this:
A classic dynamic programming or greedy problem (Kadane's algorithm). Checks understanding of optimizing sums over contiguous segments.
How to answer:
Kadane's algorithm: Iterate through the array, maintaining the currentmax ending at the current position and the globalmax found so far.
Example answer:
Initialize maxsofar = nums[0] and currentmax = nums[0]. Iterate from the second element. currentmax = max(nums[i], currentmax + nums[i]). maxsofar = max(maxsofar, currentmax). Return maxsofar.
Other Tips to Prepare for a Square LeetCode Interview
Preparing for a Square LeetCode interview extends beyond merely solving problems. It's about developing a robust problem-solving methodology. Start by mastering fundamental data structures and algorithms, then practice regularly on platforms like LeetCode, focusing on variety and understanding underlying patterns. "The only way to do great work is to love what you do," a quote often attributed to Steve Jobs, reminds us that genuine curiosity and enjoyment of problem-solving can significantly boost your performance. Always articulate your thought process aloud, even during self-practice; this simulates the interview environment and helps clarify your logic.
Consider using tools like Verve AI Interview Copilot (https://vervecopilot.com) to refine your communication and problem-solving approach. Verve AI Interview Copilot offers real-time feedback on your coding and explanation, helping you identify areas for improvement. Leverage Verve AI Interview Copilot to simulate mock interviews, gaining exposure to diverse questions and refining your ability to explain complex solutions concisely. Another great tip: "Practice makes perfect," as the old adage goes. Consistently working through Square LeetCode problems and getting feedback, perhaps through Verve AI Interview Copilot, will build confidence and competence. Remember to review common system design patterns relevant to Square's domain if you are interviewing for senior roles.
Frequently Asked Questions
Q1: How difficult are Square LeetCode questions typically?
A1: Square questions range from medium to hard, often testing fundamental algorithms, system design principles, and sometimes front-end specific challenges.
Q2: Should I focus on specific LeetCode categories for Square?
A2: Prioritize arrays, strings, linked lists, trees, graphs, dynamic programming, and common sorting/searching algorithms relevant for Square LeetCode.
Q3: Is it important to optimize my solution for Square interviews?
A3: Yes, strive for optimal time and space complexity. Always discuss trade-offs and explain why your chosen solution is efficient.
Q4: How important is communication during a Square interview?
A4: Highly important. Clearly explain your thought process, assumptions, edge cases, and reasoning behind your approach.
Q5: Are there any specific languages preferred for Square LeetCode problems?
A5: While Square accepts common languages like Python, Java, or C++, consistency and clear code are more important than the specific language choice.
