Nvidia, a global leader in AI and graphics processing, seeks top-tier engineering talent. Securing a role at Nvidia often involves navigating rigorous technical interviews, with a significant emphasis on problem-solving skills demonstrated through LeetCode-style questions. These challenges assess your proficiency in data structures, algorithms, and logical thinking under pressure. Understanding the types of questions commonly asked and practicing effective solutions are crucial steps toward success. This comprehensive guide provides insights into frequently encountered Nvidia LeetCode questions, offering strategic approaches and example solutions to help you prepare effectively. Mastering these concepts will not only boost your confidence but also enhance your ability to articulate complex technical solutions during your interview.
What Are Nvidia LeetCode Interview Questions?
Nvidia LeetCode interview questions are algorithmic challenges designed to evaluate a candidate's coding proficiency, problem-solving skills, and understanding of fundamental computer science concepts. These questions typically involve manipulating data structures like arrays, linked lists, trees, and graphs, or applying algorithmic techniques such as sorting, searching, dynamic programming, and recursion. Nvidia uses these questions to gauge how efficiently and accurately you can translate a problem into a working, optimized solution. Unlike theoretical questions, LeetCode problems require hands-on coding, often under time constraints, reflecting the practical demands of engineering roles. Familiarity with common patterns and efficient coding practices is key to excelling in this segment of the Nvidia interview process.
Why Do Interviewers Ask Nvidia LeetCode Interview Questions?
Interviewers at Nvidia ask LeetCode-style questions for several strategic reasons. Firstly, they serve as a standardized benchmark to assess a candidate's core technical abilities across a diverse pool of applicants. These questions reveal a candidate's analytical thinking, their ability to break down complex problems, and their structured approach to problem-solving. Secondly, they evaluate your proficiency in writing clean, efficient, and bug-free code, which is essential for developing robust software solutions. Thirdly, these questions test your knowledge of optimal data structures and algorithms, ensuring you can design scalable and performant systems. Finally, the pressure of a timed coding challenge helps interviewers observe how candidates manage stress, communicate their thought process, and debug their code, all vital qualities for an engineer at Nvidia.
Last Stone Weight
Reverse Linked List
Search in Rotated Sorted Array
Rectangle Area
LRU Cache
Rotate Image
Power of Two
Number of Islands
Design HashMap
Minimum Area Rectangle
Binary Tree Right Side View
Reconstruct Itinerary
Two Sum
Longest Substring Without Repeating Characters
Median of Two Sorted Arrays
Container With Most Water
Longest Palindromic Substring
Merge Two Sorted Lists
Valid Parentheses
Merge K Sorted Lists
Generate Parentheses
Merge Intervals
Climbing Stairs
Maximum Subarray
Subtree of Another Tree
Lowest Common Ancestor of a Binary Search Tree
Course Schedule
Word Break
Coin Change
Kth Largest Element in an Array
Preview List
1. What is the last stone weight?
Why you might get asked this:
This question tests your understanding of heaps (priority queues) for efficiently managing dynamic sets of values where extreme elements need frequent access or removal.
How to answer:
Use a max-heap to store stone weights. Repeatedly extract the two largest stones, calculate their difference, and re-insert the non-zero difference.
Example answer:
Insert all stone weights into a max-heap. While more than one stone remains, pop the two largest (y, x), compute their difference (y-x), and if the difference is greater than zero, push it back. The final remaining stone is the answer, or zero if none.
2. How do you reverse a singly linked list?
Why you might get asked this:
A fundamental linked list manipulation problem, it assesses your ability to handle pointers and iterate through linked data structures.
How to answer:
Iterate through the list, re-pointing each node's next pointer to its previous node. Keep track of the previous, current, and next nodes.
Example answer:
Initialize prev to None and current to head. In a loop, store current.next as nexttemp, then set current.next to prev. Update prev to current and current to nexttemp. Return prev when current is None.
3. How do you search for a target in a rotated sorted array?
Why you might get asked this:
This problem evaluates your mastery of binary search in non-trivial scenarios, requiring careful handling of rotated pivot points.
How to answer:
Adapt binary search. Determine which half is sorted, then check if the target falls within that sorted range to narrow your search.
Example answer:
Use binary search with left and right pointers. In each step, compare nums[mid] with nums[left] to identify the sorted half. If nums[left] to nums[mid] is sorted and target is in this range, narrow right. Else, narrow left. Handle the other half similarly.
4. How do you compute the total area covered by two rectangles?
Why you might get asked this:
Tests geometric reasoning and ability to handle overlapping regions, often seen in graphics or spatial computing contexts.
How to answer:
Calculate the sum of individual areas and subtract the area of their intersection. The intersection area requires finding overlapping dimensions.
Example answer:
Calculate area of rectangle 1 (width1 height1) and rectangle 2 (width2 height2). Determine the intersection's left, right, bottom, and top coordinates by taking max of lefts, min of rights, max of bottoms, and min of tops. If there's an overlap, calculate its area and subtract it from the sum of individual areas.
5. How do you design an LRU Cache?
Why you might get asked this:
Assesses understanding of cache mechanisms, data structure design, and efficient lookup/eviction policies crucial for system design.
How to answer:
Combine a hash map (for O(1) lookup) with a doubly linked list (for O(1) recentness updates and eviction).
Example answer:
Use an OrderedDict (Python) or a custom hash map and doubly linked list. get moves the accessed item to the end (most recently used). put adds/updates an item; if capacity is exceeded, remove the least recently used item from the front.
6. How do you rotate a 2D image by 90 degrees clockwise?
Why you might get asked this:
Tests in-place array manipulation, matrix transformations, and careful index handling, common in image processing.
How to answer:
Perform a transpose (swap matrix[i][j] with matrix[j][i]), then reverse each row. This rotates the image in-place efficiently.
Example answer:
First, iterate through the upper triangle of the matrix (i from 0 to n-1, j from i+1 to n-1) and swap matrix[i][j] with matrix[j][i] to transpose. Second, for each row, reverse the elements to complete the 90-degree clockwise rotation.
7. How do you check if a number is a power of two?
Why you might get asked this:
A classic bit manipulation question that assesses understanding of binary representations and efficient arithmetic.
How to answer:
A number is a power of two if it's positive and has exactly one bit set in its binary representation. Use the n & (n-1) trick.
Example answer:
For a positive integer n, n & (n-1) will be 0 if n is a power of two. This works because n-1 flips the rightmost set bit in n to 0 and all bits to its right to 1. The condition n > 0 is also necessary.
8. How do you count the number of islands in a 2D grid?
Why you might get asked this:
Tests graph traversal algorithms (DFS/BFS) on a grid, a common pattern in spatial data processing or connectivity problems.
How to answer:
Iterate through the grid. When an '1' (land) is found, increment island count and use DFS/BFS to mark all connected land cells as '0' (visited).
Example answer:
Initialize island count to 0. Iterate through each cell. If a cell is '1', increment island count and start a DFS/BFS from that cell. The DFS/BFS function will mark the current cell as '0' and recursively visit all adjacent '1' cells (up, down, left, right).
9. How do you design a basic hash map?
Why you might get asked this:
Assesses understanding of hashing, collision resolution, and fundamental data structure implementation from scratch.
How to answer:
Implement arrays for buckets. Use a hash function to map keys to indices. Handle collisions with chaining (linked lists) or open addressing.
Example answer:
Create an array of lists (buckets). The put method hashes the key to an index and adds/updates the key-value pair in the corresponding list. get hashes the key and searches its list. remove does similarly.
10. How do you find the minimum area rectangle?
Why you might get asked this:
A challenging geometry problem that combines hashing and iterative checking, relevant to computational geometry or graphics.
How to answer:
Store points efficiently (e.g., in a set for O(1) lookup). Iterate through all pairs of points to define diagonals, then check for the other two corner points.
Example answer:
Store all points in a set for quick lookup. Iterate through all pairs of points (x1, y1) and (x2, y2). If x1 != x2 and y1 != y2, these can be a diagonal. Check if (x1, y2) and (x2, y1) exist in the set. If so, calculate area abs(x1-x2)*abs(y1-y2) and update minimum.
11. How do you find the right side view of a binary tree?
Why you might get asked this:
Tests tree traversal (BFS) with a twist, requiring you to identify specific nodes at each level, common in UI rendering or data visualization.
How to answer:
Use a level-order traversal (BFS). For each level, the last node processed is the rightmost node. Add it to the result.
Example answer:
Perform a BFS using a queue. In each level, iterate through all nodes currently in the queue. The last node processed in the current level is the one to add to the result list. Enqueue its children for the next level.
12. How do you reconstruct an itinerary from a list of airline tickets?
Why you might get asked this:
A graph traversal problem (specifically Eulerian path/circuit) that requires DFS and careful backtracking, relevant to pathfinding.
How to answer:
Model tickets as a graph. Use DFS with a stack. When stuck, pop the current airport to the result. Sort destinations for lexicographical order.
Example answer:
Build an adjacency list where destinations are sorted lexicographically. Use DFS. When exploring from an airport, if no more valid tickets from it, add it to the itinerary (reversed). After DFS completes, reverse the itinerary to get the correct order.
13. How do you find two numbers that sum to a target?
Why you might get asked this:
A foundational problem testing basic array manipulation and the use of hash maps for efficient lookups, vital for optimization.
How to answer:
Use a hash map to store numbers and their indices. For each number, check if target - current_number is already in the map.
Example answer:
Iterate through the array. For each num, calculate complement = target - num. Check if complement is in the hash map. If yes, return the current index and the index stored for complement. Otherwise, add num and its index to the map.
14. How do you find the longest substring without repeating characters?
Why you might get asked this:
Tests sliding window technique and character set/map usage for string manipulation, common in text processing.
How to answer:
Use a sliding window. Maintain a set/map of characters in the current window. Expand the window; if a repeat is found, shrink from the left.
Example answer:
Use two pointers, left and right, and a set. Expand right. If s[right] is not in the set, add it and update max length. If s[right] is in the set, remove s[left] from set and increment left until the duplicate is removed, then add s[right].
15. How do you find the median of two sorted arrays?
Why you might get asked this:
A challenging binary search problem on two arrays, requiring careful partition logic, common in data merging scenarios.
How to answer:
Perform a binary search on the smaller array to find the correct partition point such that elements are correctly split for the median.
Example answer:
Binary search on the shorter array to find a partition i. Calculate j for the second array. Check if maxLeft1 <= minRight2 and maxLeft2 <= minRight1. If so, calculate median based on even/odd total length. Adjust search range based on comparisons.
16. How do you find the container with the most water?
Why you might get asked this:
Tests the two-pointer technique for array optimization, requiring smart movement to find the optimal solution.
How to answer:
Use two pointers, one at each end. Move the pointer of the shorter line inwards to potentially find a taller line, maximizing area.
Example answer:
Initialize left at 0, right at n-1, and maxarea to 0. While left < right, calculate current area min(height[left], height[right]) * (right - left). Update maxarea. Move left if height[left] <= height[right], else move right.
17. How do you find the longest palindromic substring?
Why you might get asked this:
A classic dynamic programming or expansion-around-center problem, essential for string algorithms and text processing.
How to answer:
Use dynamic programming to build a table of palindromic substrings, or expand outwards from every possible center (single character or pair).
Example answer:
Iterate through each character as a potential center of a palindrome. Expand outwards for odd-length palindromes (i, i) and even-length (i, i+1). Keep track of the longest palindrome found. DP approach involves dp[i][j] meaning substring s[i:j+1] is palindrome.
18. How do you merge two sorted linked lists?
Why you might get asked this:
A fundamental linked list manipulation problem, assessing pointer handling and iterative/recursive merging.
How to answer:
Iteratively (or recursively) compare the heads of both lists and append the smaller value node to a new merged list.
Example answer:
Create a dummy head for the merged list. Use a current pointer. While both lists have nodes, compare their values. Append the smaller node to current.next and advance that list's pointer. Finally, append any remaining nodes from either list.
19. How do you validate parentheses?
Why you might get asked this:
Tests stack data structure usage for matching opening and closing delimiters, common in compiler design or parsing.
How to answer:
Use a stack. Push opening brackets. When a closing bracket appears, pop from stack and check for a match.
Example answer:
Use a stack. Define a map for bracket pairs. Iterate through string: if opening, push to stack. If closing, check if stack is empty or top doesn't match; if so, invalid. Pop stack. After loop, stack must be empty for valid string.
20. How do you merge K sorted linked lists?
Why you might get asked this:
An advanced linked list problem typically solved using a min-heap or by iteratively merging pairs of lists, assessing efficiency.
How to answer:
Use a min-heap to store the head of each list. Repeatedly extract the smallest head, add it to result, and push its next node to heap.
Example answer:
Create a min-heap. Add the head of each list to the heap (along with its value). Create a dummy head for the result. 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 next node to the heap.
21. How do you generate all valid parentheses?
Why you might get asked this:
A classic backtracking problem that explores combinatorial possibilities with constraints, common in language processing.
How to answer:
Use backtracking. Maintain counts of open and close parentheses. Add '(' if open < n. Add ')' if close < open. Base case when both are n.
Example answer:
Define a recursive backtracking function with currentstring, opencount, closecount. If opencount < n, append '(' and recurse. If closecount < opencount, append ')' and recurse. If opencount == n and closecount == n, add current_string to result.
22. How do you merge overlapping intervals?
Why you might get asked this:
Tests sorting and greedy approach, common in scheduling, calendar applications, or data range management.
How to answer:
Sort intervals by their start times. Iterate through sorted intervals, merging current with next if they overlap.
Example answer:
Sort the input intervals list based on the start time of each interval. Initialize mergedintervals with the first interval. Iterate from the second interval: if current interval overlaps with the last in mergedintervals, update its end. Else, add current interval to merged_intervals.
23. How do you calculate the number of distinct ways to climb stairs?
Why you might get asked this:
A straightforward dynamic programming problem (Fibonacci sequence), testing basic DP formulation.
How to answer:
Recognize it as a 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:
Use dynamic programming. Create a DP array where dp[i] is ways to reach step i. dp[1]=1, dp[2]=2. For i > 2, dp[i] = dp[i-1] + dp[i-2]. Return dp[n]. Alternatively, use two variables to store previous two values for O(1) space.
24. How do you find the maximum subarray sum?
Why you might get asked this:
A classic dynamic programming or Kadane's algorithm problem, fundamental for array analysis and optimization.
How to answer:
Use Kadane's algorithm. Iterate through the array, maintaining a currentmax and a globalmax. currentmax is max(num, num + currentmax).
Example answer:
Initialize maxsofar and currentmax to the first element (or negative infinity). Iterate from the second element. For each number, currentmax = max(num, currentmax + num). Update maxsofar = max(maxsofar, currentmax). Return maxsofar.
25. How do you check if one binary tree is a subtree of another?
Why you might get asked this:
Tests tree traversal (DFS/BFS) and comparison of tree structures, important for hierarchical data processing.
How to answer:
Traverse the main tree (e.g., DFS). At each node, check if the subtree rooted at that node is identical to the target subtree.
Example answer:
Implement two helper functions: isSameTree(p, q) checks if two trees are identical. isSubtree(root, subRoot) recursively calls itself on root.left and root.right, and calls isSameTree(root, subRoot) at each node. Return true if isSameTree is ever true.
26. How do you find the Lowest Common Ancestor (LCA) of two nodes in a Binary Search Tree (BST)?
Why you might get asked this:
Tests understanding of BST properties and tree traversal for finding relationships between nodes.
How to answer:
Leverage BST properties: if both nodes are smaller than current, go left; if both larger, go right. Otherwise, current is LCA.
Example answer:
Start at the root. If both p and q are less than the current node's value, move to the left child. If both are greater, move to the right child. If one is smaller and one is larger (or one is the current node), then the current node is the LCA.
27. How do you determine if a set of courses can be finished (Course Schedule)?
Why you might get asked this:
A classic graph problem (topological sort/cycle detection) common in dependency resolution or task scheduling.
How to answer:
Model as a directed graph. Detect cycles using DFS (if cycle exists, cannot finish) or use Kahn's algorithm (BFS for topological sort).
Example answer:
Build an adjacency list and calculate in-degrees for all nodes. Use a queue for nodes with in-degree 0. While queue not empty, pop a node, decrement in-degree of its neighbors. If a neighbor's in-degree becomes 0, add to queue. Count processed nodes; if less than total, a cycle exists.
28. How do you determine if a string can be segmented into words from a dictionary (Word Break)?
Why you might get asked this:
A dynamic programming problem for string segmentation, relevant to natural language processing or text parsing.
How to answer:
Use dynamic programming. dp[i] is true if s[0...i-1] can be segmented. Iterate and check substrings from j to i.
Example answer:
Create a boolean dp array of size n+1. dp[0] is true (empty string can be segmented). For i from 1 to n, iterate j from 0 to i-1. If dp[j] is true and s[j:i] is in the dictionary, set dp[i] to true and break inner loop. Return dp[n].
29. How do you find the fewest number of coins to make a given amount (Coin Change)?
Why you might get asked this:
A fundamental dynamic programming problem demonstrating optimization and state transitions, common in financial or resource allocation.
How to answer:
Use dynamic programming. dp[i] stores the minimum coins for amount i. Iterate through amounts and coin denominations.
Example answer:
Initialize dp array of size amount+1 with infinity, dp[0]=0. For each coin in coins: iterate i from coin to amount. If dp[i - coin] is not infinity, update dp[i] = min(dp[i], dp[i - coin] + 1). Return dp[amount] or -1 if infinity.
30. How do you find the Kth largest element in an array?
Why you might get asked this:
Tests understanding of sorting algorithms, heap (priority queue) usage, or Quickselect (partitioning) for selection problems.
How to answer:
Use a min-heap of size K. Iterate through array, adding elements. If heap size exceeds K, pop smallest. The heap's root is the answer.
Example answer:
Use a min-heap. Iterate through the input array nums. Push each num onto the heap. If the heap's size exceeds k, pop the smallest element. After iterating through all nums, the top element of the heap will be the Kth largest.
Other Tips to Prepare for a Nvidia LeetCode Interview
Thorough preparation is paramount for excelling in Nvidia's technical interviews. Beyond practicing the common LeetCode questions, focus on understanding the underlying data structures and algorithms, not just memorizing solutions. As renowned computer scientist Donald Knuth once said, "The most important thing in programming is to be able to think." This emphasizes the importance of problem-solving adaptability. Practice articulating your thought process clearly and concisely, explaining your approach, potential trade-offs, and why your chosen solution is optimal. Utilize tools like Verve AI Interview Copilot to simulate real interview environments and receive instant feedback on your coding and communication. Regular mock interviews, perhaps with peers or using a platform like Verve AI Interview Copilot (https://vervecopilot.com), can significantly improve your performance under pressure. Remember to analyze the time and space complexity of your solutions. A solid understanding of these fundamentals, combined with consistent practice using resources like Verve AI Interview Copilot, will set you up for success.
Frequently Asked Questions
Q1: How long is a typical Nvidia coding interview?
A1: Nvidia coding interviews typically last 45 to 60 minutes, with most of that time dedicated to solving one or two LeetCode-style problems.
Q2: What programming languages are allowed at Nvidia interviews?
A2: Common languages like Python, C++, Java, and Go are generally accepted. Choose the language you are most proficient in.
Q3: Should I explain my thought process during the interview?
A3: Absolutely. Articulating your thought process, discussing constraints, and outlining your approach are crucial for demonstrating your problem-solving skills.
Q4: Is it okay to ask clarifying questions about a problem?
A4: Yes, it is highly encouraged. Asking clarifying questions shows attention to detail and helps ensure you understand the problem requirements fully.
Q5: What if I can't solve the problem completely?
A5: Focus on outlining a clear approach, discussing edge cases, and implementing as much as you can. Partial solutions with good explanations are better than silence.
