Preparing for a technical interview, especially with a company like Intuit, demands a robust understanding of data structures and algorithms. Intuit, renowned for its financial software products like QuickBooks and TurboTax, seeks engineers who can not only write efficient code but also articulate their problem-solving thought processes. The technical interviews at Intuit often feature coding challenges closely resembling those found on platforms like LeetCode. This comprehensive guide will walk you through the most frequently encountered Intuit LeetCode questions, offering insights into why they are asked, how to approach them, and effective example answers to help you ace your next interview. Mastering these fundamental problems will significantly boost your confidence and readiness, equipping you with the necessary skills to demonstrate your analytical prowess and coding expertise to Intuit recruiters.
What Are Intuit LeetCode Questions?
Intuit LeetCode questions refer to the type of algorithmic and data structure challenges commonly posed during technical interviews at Intuit, mirroring the format and complexity of problems found on LeetCode. These questions are designed to assess a candidate's fundamental programming skills, problem-solving abilities, and efficiency in handling various data structures like arrays, linked lists, trees, graphs, and hash maps. Interviewers use these problems to gauge your understanding of core algorithms (e.g., sorting, searching, dynamic programming, BFS/DFS) and your capacity to apply them under pressure. Success in these challenges demonstrates your aptitude for developing optimized solutions, which is crucial for building scalable and high-performance software at Intuit. They serve as a practical evaluation of your readiness for real-world engineering challenges.
Why Do Interviewers Ask Intuit LeetCode Questions?
Interviewers at Intuit, like many leading tech companies, ask LeetCode-style questions for several strategic reasons. Firstly, these questions provide a standardized way to evaluate a candidate's core computer science fundamentals, including data structures and algorithms, which are the building blocks of efficient software. Secondly, they reveal a candidate's problem-solving methodology: how they break down complex problems, identify patterns, and devise logical steps to arrive at a solution. This includes assessing their ability to think critically, analyze edge cases, and articulate their thought process clearly. Furthermore, Intuit LeetCode questions test a candidate's coding proficiency, assessing their ability to translate an algorithm into clean, correct, and efficient code. Lastly, it helps interviewers understand how candidates handle ambiguity and pressure, and whether they can optimize solutions for both time and space complexity, crucial skills for developing robust products.
Preview List
Two Sum
Valid Parentheses
Merge Intervals
Longest Substring Without Repeating Characters
Group Anagrams
Number of Islands
3Sum
Trapping Rain Water
Median of Two Sorted Arrays
Merge k Sorted Lists
Binary Tree Inorder Traversal
Maximum Subarray
Word Ladder
Valid Sudoku
Clone Graph
Course Schedule
Linked List Cycle
Reverse Linked List
Lowest Common Ancestor of a BST
Number of Connected Components in Graph
Design LRU Cache
Sliding Window Maximum
Product of Array Except Self
Rotate Image
Kth Largest Element in an Array
Serialize and Deserialize Binary Tree
Validate Binary Search Tree
Search in Rotated Sorted Array
Subsets
Coin Change
1. How do you find two numbers in an array that sum to a target?
Why you might get asked this:
This easy Intuit LeetCode problem tests your understanding of arrays, hash maps, and optimizing for time complexity. It's a foundational problem for basic search.
How to answer:
Use a hash map to store numbers encountered and their indices. For each number, calculate its complement (target - current_number) and check if it exists in the map.
Example answer:
Iterate through the array. For each nums[i], calculate complement = target - nums[i]. If complement is in the hash map, return [map.get(complement), i]. Otherwise, add nums[i] and its index i to the map. This uses O(N) time.
2. How do you check if a string of parentheses is valid?
Why you might get asked this:
Tests stack data structure usage and logical handling of matching pairs in sequences, common in parsing or compiler design.
How to answer:
Use a stack to keep track of opening brackets. When a closing bracket appears, pop from the stack and check for a match.
Example answer:
Initialize an empty stack. Iterate through the string. If an opening bracket (, {, [ is found, push it. If a closing bracket is found, check if the stack is empty or if the top element doesn't match. Pop if it matches. Finally, the stack must be empty.
3. How do you merge overlapping intervals in a list?
Why you might get asked this:
Tests sorting and greedy algorithm approaches, useful for scheduling or resource allocation problems. It's a common Intuit LeetCode pattern.
How to answer:
Sort the intervals by their start times. Iterate through the sorted intervals, merging current with the next if they overlap.
Example answer:
Sort intervals by start point. Initialize merged list with the first interval. For subsequent intervals, if it overlaps with the last in merged, update the end of the last interval. Otherwise, add the new interval to merged.
4. How do you find the longest substring without repeating characters?
Why you might get asked this:
Evaluates sliding window technique and hash set usage for efficient character tracking. Demonstrates dynamic window sizing.
How to answer:
Use a sliding window (two pointers) and a hash set to keep track of characters within the current window. Expand the window, shrinking when a duplicate is found.
Example answer:
Initialize left=0, maxLength=0, and a charSet. Iterate right pointer. If s[right] is in charSet, remove s[left] from charSet and increment left until no duplicate. Add s[right] to charSet. Update maxLength = max(maxLength, right - left + 1).
5. How do you group words that are anagrams of each other?
Why you might get asked this:
Tests string manipulation, hash map usage for grouping, and understanding of canonical forms. It's a common Intuit LeetCode string problem.
How to answer:
For each word, generate a unique "key" (e.g., sorted string or character count array) that represents its anagram group. Use a hash map to group words by these keys.
Example answer:
Create a hash map map>. Iterate through the input words. For each word, sort its characters to form a key. Add the original word to the list associated with this key in the map. Return all values from the map.
6. How do you count the number of islands in a 2D binary grid?
Why you might get asked this:
Assesses graph traversal algorithms (DFS/BFS) on a grid, common for connectivity problems and understanding matrix manipulation.
How to answer:
Iterate through the grid. When an '1' is found, increment island count and start a DFS/BFS from that cell to mark all connected '1's as visited ('0').
Example answer:
Initialize count = 0. Iterate grid cells. If grid[r][c] == '1', increment count and call DFS/BFS from (r, c). In DFS/BFS, mark current cell as '0' (visited) and recursively/iteratively explore valid neighbors.
7. How do you find all unique triplets in an array that sum to zero?
Why you might get asked this:
Tests multi-pointer technique, sorting, and careful handling of duplicates in a medium-difficulty array problem.
How to answer:
Sort the array. Iterate through each element as the first number. Use two pointers (left and right) on the remaining array to find the other two numbers. Handle duplicates carefully.
Example answer:
Sort nums. Iterate i from 0 to n-3. Skip duplicates for nums[i]. Set left = i+1, right = n-1. While left < right: currentsum = nums[i] + nums[left] + nums[right]. If currentsum == 0, add triplet, left++, right--, skip left and right duplicates. If current_sum < 0, left++. Else right--.
8. How do you calculate the maximum water that can be trapped between bars?
Why you might get asked this:
A challenging problem requiring careful application of two pointers or dynamic programming to find optimal heights.
How to answer:
Use two pointers, one from each end. Maintain maxLeft and maxRight heights. The water trapped at current position depends on the minimum of maxLeft and maxRight.
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--.
9. How do you find the median of two sorted arrays?
Why you might get asked this:
Tests advanced binary search, divide and conquer principles, and ability to handle edge cases with even/odd lengths.
How to answer:
Perform a binary search on the smaller array to find the correct partition point such that elements to the left of partition in both arrays are less than elements to the right.
Example answer:
Ensure nums1 is the smaller array. Use binary search on nums1's partition. Calculate partitionX and partitionY. Check if maxLeftX <= minRightY and maxLeftY <= minRightX. If true, median found. Adjust search range based on maxLeftX and minRightY.
10. How do you merge k sorted linked lists into one sorted list?
Why you might get asked this:
Assesses priority queue (min-heap) usage and divide-and-conquer strategies for merging multiple sorted structures efficiently.
How to answer:
Use a min-heap to store the head nodes of all lists. Repeatedly extract the smallest node from the heap, add it to the result, and add its next node to the heap.
Example answer:
Create a min-heap. Add the head of each non-empty list to the heap. While the heap is not empty, extract the smallest node, append it to the result list, and if it has a next node, add that to the heap.
11. How do you perform an inorder traversal of a binary tree?
Why you might get asked this:
Tests fundamental tree traversal techniques, particularly recursion or iterative stack-based approaches.
How to answer:
Recursively: Traverse left subtree, visit root, traverse right subtree. Iteratively: Use a stack, pushing nodes and moving left until null, then pop, visit, move right.
Example answer:
For recursive: inorder(node): if node is null, return. inorder(node.left). Add node.val to result. inorder(node.right). For iterative: use a stack, curr = root. While curr or stack not empty: while curr not null, push curr, curr = curr.left. Pop curr, add curr.val to result, curr = curr.right.
12. How do you find the contiguous subarray with the largest sum?
Why you might get asked this:
A classic dynamic programming problem (Kadane's algorithm) that tests optimization and simple DP state tracking.
How to answer:
Use Kadane's algorithm: Iterate through the array, keeping track of the current subarray sum and the maximum subarray sum 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.
13. How do you find the shortest transformation sequence from one word to another?
Why you might get asked this:
Tests Breadth-First Search (BFS) on a graph where words are nodes and one-letter differences are edges, for shortest path.
How to answer:
Model as a graph problem where words are nodes. Create edges for words differing by one character. Use BFS to find the shortest path from beginWord to endWord.
Example answer:
Build an adjacency list or on-the-fly neighbors. Use BFS, starting with beginWord at level 1. At each step, explore all valid one-character difference neighbors not yet visited. If endWord is reached, return current level.
14. How do you determine if a 9x9 Sudoku board is valid?
Why you might get asked this:
Tests efficient validation logic using hash sets to check uniqueness constraints across rows, columns, and 3x3 sub-grids.
How to answer:
Check each row, each column, and each 3x3 sub-box for unique numbers (1-9). Use hash sets for efficient duplicate checking within each group.
Example answer:
Check row: Is
board[r][c]already inrowSet[r]? Add if not.Check column: Is
board[r][c]already incolSet[c]? Add if not.Check 3x3 box: Is
board[r][c]already inboxSet[(r/3)*3 + (c/3)]? Add if not.
Iterate through the board. For each cell (r, c), if board[r][c] is a digit:
If any check fails, return false.
15. How do you create a deep copy of an undirected graph?
Why you might get asked this:
Assesses graph traversal (DFS/BFS) and handling of visited nodes to avoid cycles and create distinct node copies.
How to answer:
Use DFS or BFS. Maintain a hash map to store mappings from original nodes to their new cloned nodes to avoid redundant cloning and handle cycles.
Example answer:
Start traversal from a given node. For each visited node, create its clone. Store the mapping originalnode -> clonednode in a hash map. For each neighbor of the original node, if it hasn't been cloned, clone it and recursively/iteratively process. Connect the cloned neighbor to the current cloned node.
16. How do you determine if all courses can be finished given prerequisites?
Why you might get asked this:
Tests graph theory concepts, specifically cycle detection in a directed graph using DFS or topological sort.
How to answer:
Model courses and prerequisites as a directed graph. The problem is equivalent to checking if the graph contains a cycle (if so, impossible) or if a topological sort is possible.
Example answer:
Build an adjacency list and an in-degree array. Use Kahn's algorithm (BFS for topological sort): add all 0-in-degree courses to a queue. While queue not empty, pop a course, decrement in-degree of its neighbors. If a neighbor's in-degree becomes 0, add to queue. If all courses are processed, return true.
17. How do you detect if a linked list has a cycle?
Why you might get asked this:
A classic problem testing linked list manipulation and the two-pointer (Floyd's Tortoise and Hare) approach.
How to answer:
Use two pointers, a slow one moving one step at a time and a fast one moving two steps. If they meet, a cycle exists.
Example answer:
Initialize slow = head and fast = head. Move slow one step and fast two steps at each iteration. If fast or fast.next becomes null, no cycle. If slow == fast at any point, a cycle is detected.
18. How do you reverse a singly linked list?
Why you might get asked this:
Tests fundamental linked list manipulation, understanding of pointers, and iterative or recursive thinking.
How to answer:
Iteratively: Maintain three pointers: prev, curr, nextTemp. In each step, curr.next points to prev, then prev and curr advance. Recursively: Reverse the rest of the list and then fix the current node's next pointer.
Example answer:
Initialize prev = null, curr = head. While curr is not null: nextTemp = curr.next. curr.next = prev. prev = curr. curr = nextTemp. Return prev.
19. How do you find the lowest common ancestor of two nodes in a Binary Search Tree?
Why you might get asked this:
Tests recursive tree traversal and leverages BST properties for efficient search, a common Intuit LeetCode tree problem.
How to answer:
Utilize BST properties: if both nodes are smaller than the current node, search left; if both are larger, search right. Otherwise, the current node is the LCA.
Example answer:
If p and q are both less than root.val, recurse on root.left. If p and q are both greater than root.val, recurse on root.right. Otherwise, root is the LCA.
20. How do you count the number of connected components in an undirected graph?
Why you might get asked this:
Assesses graph traversal (DFS/BFS) to identify distinct connected subgraphs within a larger structure.
How to answer:
Iterate through all nodes. For each unvisited node, increment component count and start a DFS/BFS from it to mark all reachable nodes as visited.
Example answer:
Maintain a visited array. Initialize count = 0. Iterate nodes i from 0 to n-1. If i is not visited, increment count and perform a DFS/BFS starting from i to mark all reachable nodes as visited.
21. How would you design a Least Recently Used (LRU) cache?
Why you might get asked this:
A popular system design question that tests data structure choices (hash map + doubly linked list) and understanding of cache eviction policies.
How to answer:
Use a hash map for O(1) lookups and a doubly linked list to maintain the order of usage (most recently used at one end, least at the other) for O(1) eviction/update.
Example answer:
Implement get(key): if key exists, move its node to the front of the list and return value. If not, return -1. Implement put(key, value): if key exists, update value and move to front. If not, create new node, add to front. If cache exceeds capacity, remove least recently used node from back.
22. How do you find the maximum in each sliding window of size k?
Why you might get asked this:
Tests advanced sliding window techniques, often requiring a deque (double-ended queue) for efficient maximum tracking.
How to answer:
Use a deque to store indices of elements in the current window. Keep deque elements in decreasing order, ensuring the front is the maximum.
Example answer:
Initialize an empty deque and result list. Iterate through nums: remove elements from deque's front if out of window k. Remove elements from deque's back if smaller than current nums[i]. Add i to deque's back. If i >= k-1, add nums[deque.front()] to result.
23. How do you compute the product of all elements except the current one?
Why you might get asked this:
Tests array manipulation and clever use of prefix/suffix products to solve without division in O(N) time.
How to answer:
Compute prefix products from left to right, then suffix products from right to left. Multiply corresponding prefix and suffix products for each element.
Example answer:
Create result array. First pass: result[i] stores product of nums[0...i-1]. product = 1. Iterate i from 0 to n-1: result[i] = product, product = nums[i]. Second pass: product = 1. Iterate i from n-1 to 0: result[i] = product, product *= nums[i].
24. How do you rotate a square matrix 90 degrees clockwise in-place?
Why you might get asked this:
Tests matrix manipulation and understanding of in-place transformations, often requiring layers or symmetry.
How to answer:
Perform a transpose (swap matrix[i][j] with matrix[j][i]), then reverse each row. This rotates clockwise.
Example answer:
First, transpose the matrix: swap matrix[i][j] with matrix[j][i] for j > i. Then, for each row, reverse the elements in place. This achieves a 90-degree clockwise rotation.
25. How do you find the Kth largest element in an unsorted array?
Why you might get asked this:
Tests sorting, heap (priority queue) usage, or Quickselect algorithm for efficient selection in a large dataset.
How to answer:
Use a min-heap of size K. Iterate through array, adding elements to heap. If heap size exceeds K, remove the minimum. The root of the heap is the Kth largest. Alternatively, use Quickselect.
Example answer:
Use a min-heap. Add elements to the heap one by one. If the heap size exceeds k, remove the smallest element (heap's root). After iterating through all elements, the heap's root will be the Kth largest element.
26. How do you serialize and deserialize a binary tree?
Why you might get asked this:
A challenging problem that tests tree traversal (BFS/DFS) and string manipulation for converting tree structure to/from a string representation.
How to answer:
Serialization: Use BFS or DFS to traverse the tree and convert it to a string, using a special marker for null nodes. Deserialization: Reconstruct the tree from the string using a queue (for BFS-based) or recursion (for DFS-based).
Example answer:
For serialization (BFS): Use a queue. Append node values to string, with '#' for null. For deserialization: Split string by delimiter. Use a queue of strings to reconstruct nodes and assign children.
27. How do you validate if a given binary tree is a valid Binary Search Tree?
Why you might get asked this:
Tests recursive tree traversal and understanding of BST properties (left child < root < right child) with boundary checks.
How to answer:
Perform an in-order traversal. A BST's in-order traversal should yield elements in strictly increasing order. Or, use recursion with min and max bounds.
Example answer:
Use a recursive helper function isValidBST(node, minVal, maxVal). For each node, check if node.val is within (minVal, maxVal). Recursively call isValidBST(node.left, minVal, node.val) and isValidBST(node.right, node.val, maxVal).
28. How do you search for a target in a rotated sorted array?
Why you might get asked this:
Tests binary search variation, requiring careful handling of the pivot point in a partially sorted array. A common Intuit LeetCode problem.
How to answer:
Apply modified binary search. Determine which half of the array is sorted and if the target lies within that sorted half, then adjust search range accordingly.
Example answer:
Initialize left=0, right=n-1. While left <= right: calculate mid. If nums[mid] == target, return mid. If left half (nums[left] <= nums[mid]) is sorted: if target in [nums[left], nums[mid]], right = mid-1; else left = mid+1. Else (right half is sorted): if target in [nums[mid], nums[right]], left = mid+1; else right = mid-1.
29. How do you generate all possible subsets of a set of distinct integers?
Why you might get asked this:
Tests backtracking or bit manipulation for combinatorics, crucial for power set generation.
How to answer:
Using backtracking: iteratively build subsets by deciding whether to include or exclude each element. Using bit manipulation: each bit position represents an element's inclusion.
Example answer:
For backtracking: Define a recursive function backtrack(startindex, currentsubset). In each call, add currentsubset to result. Iterate i from startindex to n-1: add nums[i] to currentsubset, call backtrack(i+1, currentsubset), then remove nums[i] (backtrack).
30. How do you find the minimum number of coins to make a given amount?
Why you might get asked this:
A classic dynamic programming problem testing optimal substructure and overlapping subproblems for change-making.
How to answer:
Use dynamic programming. Create a dp array where dp[i] is the minimum coins for amount i. Iterate through amounts from 1 to amount, for each, try all coin denominations.
Example answer:
Initialize dp array of size amount+1 with infinity, dp[0]=0. Iterate i from 1 to amount: for each coin in coins: if i - coin >= 0, dp[i] = min(dp[i], 1 + dp[i - coin]). Return dp[amount] (or -1 if dp[amount] is still infinity).
Other Tips to Prepare for an Intuit LeetCode Interview
Effective preparation for an Intuit LeetCode interview goes beyond just memorizing solutions; it's about building a strong foundational understanding and developing robust problem-solving skills. As Benjamin Franklin famously said, "By failing to prepare, you are preparing to fail." Consistent practice is key. Dedicate time daily to solve problems, starting with easier ones and gradually moving to medium and hard difficulties. Focus on understanding the underlying algorithms and data structures rather than just getting the right answer. Can you explain why a hash map is better than an array for a certain problem? Can you articulate the time and space complexity of your solution?
Communication is paramount. Intuit interviewers value candidates who can clearly explain their thought process, discuss trade-offs between different approaches, and walk through their code logic step-by-step. Practice explaining your solutions out loud. Consider using a tool like Verve AI Interview Copilot, which provides real-time feedback on your explanations and coding performance. Another crucial aspect is debugging. Being able to identify and fix errors efficiently is a highly prized skill. Work through various edge cases and test your code rigorously. As an expert in technical interviews once stated, "Your ability to debug live is often as important as your ability to write code." Verve AI Interview Copilot can simulate a live coding environment, helping you hone this skill. Remember to also review common system design patterns if applying for senior roles. Leverage Verve AI Interview Copilot (https://vervecopilot.com) to refine your responses and gain confidence for your Intuit interview.
Frequently Asked Questions
Q1: What programming languages are preferred at Intuit for coding interviews?
A1: Python, Java, and C++ are commonly accepted. Choose the one you're most proficient in to showcase your best work and articulate your thoughts clearly.
Q2: Is system design always part of Intuit coding interviews?
A2: System design questions are typically for more senior engineering roles (Staff, Principal). For entry-level or mid-level positions, the focus is heavily on data structures and algorithms.
Q3: How important is time and space complexity analysis during the interview?
A3: Crucial. Always analyze and optimize your solutions for both time and space complexity. Interviewers expect you to discuss these aspects thoroughly and justify your choices.
Q4: Should I memorize Intuit LeetCode solutions?
A4: Focus on understanding underlying patterns and algorithms, not just memorizing solutions. Apply learned concepts to new problems, demonstrating adaptability.
Q5: How many Intuit LeetCode problems should I solve to be prepared?
A5: Quality over quantity. Aim for a deep understanding of common patterns across 100-200 problems rather than superficial exposure to many. Practice diverse problem types.
Q6: Can Verve AI Interview Copilot help with Intuit LeetCode prep?
A6: Yes, Verve AI Interview Copilot offers real-time feedback on your coding and communication, simulating Intuit interview scenarios effectively to boost your confidence.
