Navigating the competitive landscape of software engineering interviews requires thorough preparation, especially for companies like Yelp. Yelp, a globally recognized platform connecting consumers with local businesses, seeks engineers with strong problem-solving skills, often assessed through algorithmic challenges. Understanding the types of questions commonly asked in Yelp's technical interviews, particularly those found on platforms like LeetCode, is crucial for success. This guide provides a focused look at 30 frequently reported Yelp LeetCode questions, offering insights into why they are asked and how to approach them effectively. Mastering these concepts will significantly enhance your readiness for Yelp's rigorous interview process.
What Are Yelp LeetCode Questions?
Yelp LeetCode questions are programming challenges typically encountered during the technical interview rounds for software engineering roles at Yelp. These questions are drawn from common algorithmic patterns and data structures, often mirroring medium-level difficulty problems found on LeetCode. They aim to evaluate a candidate's proficiency in areas such as arrays, strings, linked lists, hashmaps, trees, graphs, dynamic programming, and sorting/searching algorithms. Beyond just finding a correct solution, Yelp interviewers look for optimal approaches, clean code, clear communication of thought processes, and an understanding of time and space complexity. The problems often relate conceptually to Yelp's domain, involving data filtering, sorting, search functionalities, or system design elements.
Why Do Interviewers Ask Yelp LeetCode Questions?
Interviewers at Yelp ask LeetCode-style questions for several key reasons. Firstly, these problems provide a standardized way to assess a candidate's foundational computer science knowledge, including their understanding of data structures and algorithms. Secondly, they reveal a candidate's problem-solving methodology: how they break down complex problems, identify patterns, and iterate towards an optimal solution. Thirdly, coding challenges demonstrate a candidate's ability to translate logical thinking into functional code, handling edge cases and considering efficiency. Lastly, discussing these solutions allows interviewers to gauge communication skills, as candidates must articulate their thought process, justify their design choices, and respond to follow-up questions, all vital for collaborative engineering environments at Yelp.
Two Sum
Valid Parentheses
Merge Two Sorted Lists
Group Anagrams
Longest Substring Without Repeating Characters
Number of Islands
Add Two Numbers (Linked List)
LRU Cache Design
Coin Change
Binary Tree Level Order Traversal
Course Schedule (Topological Sort)
Find Median from Data Stream
Minimum Window Substring
Subarray Sum Equals K
Clone Graph
Search in Rotated Sorted Array
Product of Array Except Self
Word Ladder
Alien Dictionary
Maximum Subarray
Binary Search Tree Iterator
Design Twitter
Palindrome Linked List
Top K Frequent Elements
Serialize and Deserialize Binary Tree
Sliding Window Maximum
Number of Connected Components
Minimum Depth of Binary Tree
Kth Largest Element in an Array
Valid Sudoku
Preview List
1. Two Sum
Why you might get asked this:
Tests fundamental array manipulation and hash map usage for efficient lookups. It's a common warm-up or introductory problem to gauge basic problem-solving skills.
How to answer:
Use a hash map to store numbers and their indices. For each number, check if target - current_number exists in the hash map. If so, return the indices.
Example answer:
Iterate through the array. For each element nums[i], calculate complement = target - nums[i]. Check if complement exists in a hash map. If yes, return [map[complement], i]. Otherwise, add nums[i] and i to the map.
2. Valid Parentheses
Why you might get asked this:
Assesses understanding of stack data structures and proper handling of sequential data, relevant for parsing expressions or structured data.
How to answer:
Use a stack. Push opening brackets onto the stack. When a closing bracket appears, pop from the stack and check if it matches the corresponding opening bracket.
Example answer:
Initialize an empty stack. Loop through the string. If an opening bracket is found, push it. If a closing bracket is found, check if the stack is empty or the top doesn't match; if so, it's invalid. Pop and continue. Finally, the stack must be empty.
3. Merge Two Sorted Lists
Why you might get asked this:
Evaluates proficiency with linked lists and the ability to merge sorted data efficiently, a common operation in data processing.
How to answer:
Use a dummy head node. Iterate through both lists, appending the smaller current node to the merged list. Handle remaining elements after one list is exhausted.
Example answer:
Create a dummy node and a current pointer. While both lists have nodes, compare their values and append the smaller one to current.next, then advance current and the respective list pointer. Append any remaining nodes from the non-empty list. Return dummy.next.
4. Group Anagrams
Why you might get asked this:
Tests string manipulation, hash map usage, and understanding of unique identifiers for grouped data, useful for text processing at Yelp.
How to answer:
For each string, sort its characters to create a unique "key" (e.g., "aet" for "eat", "tea"). Store strings in a hash map where keys are sorted strings and values are lists of anagrams.
Example answer:
Initialize a hash map. Iterate through the input strings. For each string, convert it to a character array, sort it, and convert it back to a string (this is the key). Add the original string to the list mapped by this key. Return all values from the hash map.
5. Longest Substring Without Repeating Characters
Why you might get asked this:
Assesses sliding window technique and efficient character tracking, applicable in text search and review analysis.
How to answer:
Use a sliding window (two pointers) and a hash set or hash map to keep track of characters within the current window to quickly check for repetitions.
Example answer:
Use a left pointer and a right pointer for the window. Maintain a HashSet to store characters in the current window. Expand right. If s[right] is in the set, move left inward, removing characters from the set, until s[right] is no longer duplicated. Update max length.
6. Number of Islands
Why you might get asked this:
Tests graph traversal algorithms (DFS/BFS) on a grid, relevant for spatial data or interconnected elements.
How to answer:
Iterate through the grid. When a '1' is found, increment island count and start DFS/BFS from that cell to mark all connected '1's as visited ('0').
Example answer:
Loop through each cell (r, c). If grid[r][c] == '1', increment islandCount and perform DFS/BFS from (r, c). In DFS/BFS, mark current '1' as '0' and recursively/iteratively visit all adjacent '1's.
7. Add Two Numbers (Linked List)
Why you might get asked this:
Evaluates linked list manipulation, handling carry-overs, and constructing new lists, mimicking data aggregation.
How to answer:
Iterate through both linked lists simultaneously. Sum digits at each position plus any carry. Create new nodes for the sum's digit, and propagate the carry.
Example answer:
Create a dummy head and current pointer. Initialize carry = 0. Loop while either list has nodes or carry exists. Sum current digits plus carry. current.next is new ListNode(sum % 10). carry = sum / 10. Advance pointers. Return dummy.next.
8. LRU Cache Design
Why you might get asked this:
Tests system design principles, data structures (hash map + doubly linked list), and performance optimization, crucial for scalable systems.
How to answer:
Implement using a hash map for O(1) lookups and a doubly linked list to maintain access order for O(1) eviction of the least recently used item.
Example answer:
Use a hash map mapping keys to nodes of a doubly linked list. The list's head is the most recently used, tail is the least. get moves node to head. put adds/updates node to head; if capacity exceeded, removes tail.
9. Coin Change
Why you might get asked this:
A classic dynamic programming problem that assesses optimization skills, applicable to resource allocation or recommendation systems.
How to answer:
Use dynamic programming. dp[i] represents the minimum number of coins for amount i. Iterate through amounts from 1 to amount, and for each coin, update dp[i] = min(dp[i], dp[i - coin] + 1).
Example answer:
Initialize dp array of size amount + 1 with infinity, dp[0] = 0. Iterate i from 1 to amount. For each coin c in coins: if i >= c and dp[i - c] is not infinity, dp[i] = min(dp[i], dp[i - c] + 1). Return dp[amount] or -1 if infinity.
10. Binary Tree Level Order Traversal
Why you might get asked this:
Checks understanding of Breadth-First Search (BFS) and queue usage, fundamental for processing hierarchical data like categories or features.
How to answer:
Use a queue for BFS. Enqueue the root. In each level, dequeue all nodes, add their values to a list for that level, and enqueue their children.
Example answer:
Initialize an empty list of lists for results and a queue with the root. While the queue is not empty, get the levelSize. Create a new list for currentLevel. For i from 0 to levelSize-1, dequeue a node, add its value to currentLevel, enqueue its left and right children if they exist. Add currentLevel to results.
11. Course Schedule (Topological Sort)
Why you might get asked this:
Evaluates graph traversal, cycle detection, and topological sorting, relevant for dependency management (e.g., feature rollout).
How to answer:
Build an adjacency list and an in-degree array. Use a queue for nodes with in-degree 0. Perform BFS, decrementing in-degree of neighbors. If count of visited nodes equals total, no cycle.
Example answer:
Represent courses as a graph with prerequisites as directed edges. Compute in-degrees for all nodes. Use a queue to start with nodes having in-degree 0. Perform BFS: dequeue a node, add to result, decrement in-degrees of its neighbors. If a neighbor's in-degree becomes 0, enqueue it. Check if all nodes visited.
12. Find Median from Data Stream
Why you might get asked this:
Tests data structure design for efficient dynamic median finding, applicable to real-time analytics or monitoring.
How to answer:
Use two heaps: a max-heap for the lower half and a min-heap for the upper half. Balance their sizes to maintain floor(n/2) elements in the max-heap and ceil(n/2) in the min-heap.
Example answer:
Maintain a maxHeap (lower half) and a minHeap (upper half). Add numbers to maxHeap first, then move its top to minHeap. Balance sizes: maxHeap can have one more element than minHeap. Median is maxHeap.top() or (maxHeap.top() + minHeap.top()) / 2.
13. Minimum Window Substring
Why you might get asked this:
Assesses advanced sliding window and hash map usage for string matching and optimization, useful for sophisticated search features.
How to answer:
Use a sliding window with two pointers. Maintain character counts for the target string and the current window using hash maps. Shrink/expand window to find minimum valid substring.
Example answer:
Use two hash maps: targetchars for target string T, and windowchars for current S window. Expand right pointer. When windowchars matches targetchars, try to shrink left pointer, updating minimum length and starting index.
14. Subarray Sum Equals K
Why you might get asked this:
Tests prefix sums and hash map usage for efficient sum queries, relevant for analyzing numerical data or time series.
How to answer:
Use a hash map to store (prefixsum, frequency). For each currentsum, check if current_sum - k exists in the map. If so, add its frequency to the total count.
Example answer:
Initialize count = 0, currentsum = 0, and a hash map prefixsums with {0: 1}. Iterate through the array nums. Add nums[i] to currentsum. If currentsum - k is in prefixsums, add prefixsums[currentsum - k] to count. Increment prefixsums[current_sum].
15. Clone Graph
Why you might get asked this:
Evaluates graph traversal (DFS/BFS) and ability to handle complex data structures with cycles, important for network or relationship mapping.
How to answer:
Use DFS or BFS. Maintain a hash map to store (originalnode, clonednode) to avoid infinite loops with cycles and ensure unique cloned nodes.
Example answer:
Perform BFS (or DFS). Use a hash map visited to store mappings from original node to its cloned counterpart. For each node, if not visited, clone it, add to visited, then add its neighbors to the queue/stack and recursively/iteratively process them, adding cloned neighbors to the cloned node.
16. Search in Rotated Sorted Array
Why you might get asked this:
Tests advanced binary search, essential for optimizing searches in partially ordered data.
How to answer:
Modify binary search. Determine which half (left or right) of the array is sorted. Based on the target's value and the sorted half, decide which half to continue searching in.
Example answer:
Perform binary search. At mid, identify if the left half (nums[low] to nums[mid]) is sorted or the right half (nums[mid] to nums[high]) is sorted. Then, check if the target falls within the sorted range. Adjust low or high accordingly.
17. Product of Array Except Self
Why you might get asked this:
Checks array manipulation, efficient calculation without division, and understanding of prefix/suffix products.
How to answer:
Calculate prefix products from left to right. Then calculate suffix products from right to left. The product at i is prefix[i-1] * suffix[i+1].
Example answer:
Create an answer array. First pass: answer[i] stores product of elements to the left of i. Second pass (backward): multiply answer[i] by product of elements to the right of i. No division is used.
18. Word Ladder
Why you might get asked this:
Tests BFS for shortest path in a graph where nodes are words and edges are single-character differences, applicable to lexical search.
How to answer:
Treat words as nodes in a graph. An edge exists between words if they differ 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. Queue stores (word, level). For each word, generate all possible one-character different words. If valid and not visited, add to queue and mark visited. Return level when endWord is found.
19. Alien Dictionary
Why you might get asked this:
Advanced graph problem involving topological sort and detecting cycles in a dictionary, useful for lexical ordering.
How to answer:
Build a directed graph from adjacent words (first differing characters define edge). Perform topological sort. Detect if a cycle exists (invalid ordering).
Example answer:
Iterate through adjacent words to build a graph and calculate in-degrees. If word1 = "abc", word2 = "ab", it's an invalid order. Otherwise, if word1[i] != word2[i], add word1[i] -> word2[i] edge. Use Kahn's algorithm (BFS with in-degrees) for topological sort.
20. Maximum Subarray
Why you might get asked this:
A classic dynamic programming or greedy problem, essential for optimizing contiguous sums in data sequences.
How to answer:
Use Kadane's algorithm. Keep track of currentmax (max sum ending at current position) and globalmax (overall max 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.
21. Binary Search Tree Iterator
Why you might get asked this:
Tests understanding of BST properties and efficient traversal using iterative methods, crucial for ordered data access.
How to answer:
Implement hasNext() and next() using an iterative in-order traversal approach, typically involving a stack to store nodes.
Example answer:
Initialize a stack. In the constructor, push all left children from the root down to the leftmost leaf. next() pops from stack, gets value, then pushes right child and its left children. hasNext() checks if stack is empty.
22. Design Twitter
Why you might get asked this:
A system design question disguised as a LeetCode problem, assessing design patterns for scalable social media features (feed, follow).
How to answer:
Use hash maps for user-to-tweets, user-to-followers/followees. Retrieve recent tweets and merge them efficiently for the news feed.
Example answer:
Use a Map> for user tweets (newest first). Use Map> for follower/followee relationships. getNewsFeed fetches tweets from followed users plus self, merges them, and returns top 10 by timestamp.
23. Palindrome Linked List
Why you might get asked this:
Evaluates linked list manipulation, particularly reversing a portion or using fast/slow pointers, relevant for data validation.
How to answer:
Use fast and slow pointers to find the middle. Reverse the second half of the list. Compare the first half with the reversed second half. Restore the list.
Example answer:
Find the middle of the list using slow/fast pointers. Reverse the second half of the list. Compare nodes from the head of the first half and the head of the reversed second half. Re-reverse the second half to restore the list structure.
24. Top K Frequent Elements
Why you might get asked this:
Tests hash map usage for frequency counting and priority queues (min-heap) for efficient retrieval of top elements.
How to answer:
Count frequencies using a hash map. Then, use a min-heap of size k to store (frequency, number) pairs, ensuring only top k elements are kept.
Example answer:
Count frequencies of each number using a hash map. Create a min-priority queue. Iterate through the map's entries: add (frequency, number) to PQ. If PQ size > k, poll smallest. Finally, extract elements from PQ.
25. Serialize and Deserialize Binary Tree
Why you might get asked this:
Tests tree traversal (e.g., BFS/DFS) and ability to represent complex data structures as strings and reconstruct them.
How to answer:
Choose a traversal (e.g., pre-order or level-order). Serialize by storing node values and null markers in a string. Deserialize by parsing the string and rebuilding the tree recursively or iteratively.
Example answer:
Use pre-order traversal for serialization: root.val, leftsubtree, rightsubtree. Use a delimiter and a special marker for nulls. For deserialization, split the string by the delimiter and recursively build the tree using a queue or index.
26. Sliding Window Maximum
Why you might get asked this:
Advanced sliding window problem, requiring a deque (double-ended queue) to efficiently maintain the maximum element in a window.
How to answer:
Use a deque to store indices of elements in decreasing order. When adding a new element, remove smaller elements from the back. Remove out-of-window elements from the front. The front of deque is the max.
Example answer:
Initialize a deque and a result list. Iterate through nums. Remove indices from deque's front if they are out of current window. Remove indices from deque's back if their corresponding values are less than nums[i]. Add i to deque's back. If window formed, add nums[deque.front()] to result.
27. Number of Connected Components
Why you might get asked this:
Tests graph traversal (DFS/BFS) or Union-Find data structure, crucial for understanding network structures or user groups.
How to answer:
Use DFS/BFS: iterate through nodes, and for each unvisited node, increment count and perform traversal to mark all connected nodes. Or, use Union-Find.
Example answer:
Initialize count = N, and a parent array for Union-Find. For each edge (u, v), perform union(u, v). If union results in merging two distinct sets, decrement count. Return final count.
28. Minimum Depth of Binary Tree
Why you might get asked this:
Checks tree traversal (BFS is optimal for minimum depth) and handling of leaf nodes, relevant for hierarchical data structure efficiency.
How to answer:
Use BFS. The first time you encounter a leaf node (no left or right children), its depth is the minimum depth.
Example answer:
Use a queue for BFS, storing (node, depth). Start with (root, 1). When dequeuing, if a node is a leaf (no children), return its depth. Otherwise, enqueue its children with incremented depth.
29. Kth Largest Element in an Array
Why you might get asked this:
Tests sorting, partitioning (Quickselect), or heap (priority queue) data structures, useful for ranking or filtering data.
How to answer:
Use a min-heap of size k. Iterate through array elements, push to heap. If heap size exceeds k, pop the smallest. The heap's root after processing all elements is the kth largest.
Example answer:
Initialize a min-priority queue. Iterate through nums. Add num to the PQ. If PQ.size() > k, PQ.poll(). After iterating, PQ.peek() will be the Kth largest element.
30. Valid Sudoku
Why you might get asked this:
Tests array/matrix manipulation and hash set usage for checking uniqueness constraints, a common pattern in data validation.
How to answer:
Use three sets (or arrays of sets): one for rows, one for columns, and one for 3x3 sub-boxes. For each cell, check if the number is already present in its respective row, column, or box set.
Example answer:
Initialize three arrays of hash sets: rows[9], cols[9], boxes[9]. Iterate through the 9x9 board. For each number, calculate its box index (r/3)*3 + c/3. If the number already exists in rows[r], cols[c], or boxes[box_idx], it's invalid. Add number to sets.
Other Tips to Prepare for a Yelp LeetCode Interview
Preparing for a Yelp LeetCode interview extends beyond just solving problems; it involves honing your overall technical and communication skills. "The difference between a good engineer and a great engineer often lies in their ability to articulate complex ideas simply," says a seasoned tech recruiter. First, focus on understanding the underlying data structures and algorithms, not just memorizing solutions. Practice explaining your thought process aloud, as if you're talking to an interviewer. This helps clarify your logic and identifies potential gaps in your understanding.
Secondly, simulate interview conditions. Use a whiteboard or a shared coding editor, and set a timer for 45-60 minutes per problem. This builds resilience under pressure. Verve AI Interview Copilot can be an invaluable tool for this, offering real-time feedback on your coding style, complexity, and communication during mock interviews. It helps you refine your explanations and identify areas for improvement. You can access it at https://vervecopilot.com.
Thirdly, dive into system design concepts relevant to Yelp. Think about how Yelp's features, like search, recommendations, or review systems, might be architected. Prepare to discuss scalability, reliability, and performance trade-offs. "Success is not final, failure is not fatal: it is the courage to continue that counts," a sentiment often attributed to Winston Churchill, applies here—consistent practice, even after setbacks, is key. Finally, leverage tools like Verve AI Interview Copilot for targeted practice on specific problem types or for comprehensive interview simulations. Verve AI Interview Copilot helps fine-tune your approach, ensuring you're not just solving problems, but excelling in the entire interview experience.
Frequently Asked Questions
Q1: How difficult are Yelp LeetCode questions?
A1: Most Yelp LeetCode questions are of medium difficulty, focusing on core data structures and algorithms with some requiring specific optimization techniques.
Q2: Should I focus on specific LeetCode topics for Yelp?
A2: Yes, prioritize arrays, strings, linked lists, hash maps, trees, graphs, dynamic programming, and sliding window techniques.
Q3: Is System Design part of Yelp interviews?
A3: Yes, System Design is a crucial component, especially for experienced roles, assessing your ability to design scalable systems like a recommendation engine.
Q4: How important is explaining my thought process?
A4: Extremely important. Interviewers value clear communication of your logic, assumptions, time/space complexity, and handling of edge cases.
Q5: Can Verve AI Interview Copilot help with Yelp interviews?
A5: Yes, Verve AI Interview Copilot provides mock interview simulations with real-time feedback on your coding, communication, and problem-solving approach for various scenarios.
