Prepare for your Expedia LeetCode interview. Discover the top 30 most common coding questions to help you ace your assessment.
Expedia Group is a global leader in travel technology, and securing a position within its engineering teams requires demonstrating strong technical proficiency. For aspiring software engineers, this often means navigating a series of challenging coding interviews. Expedia’s approach to technical assessments typically involves practical coding problems, often leveraging a framework similar to LeetCode, to evaluate a candidate's foundational algorithmic knowledge and problem-solving capabilities. These questions are designed to gauge your ability to write efficient, clean, and bug-free code under pressure. This comprehensive guide highlights the top 30 most common Expedia LeetCode interview questions, providing insights into why they are asked, how to approach them, and examples of effective answers to help you prepare thoroughly for your next interview. Mastering these fundamental problems will significantly boost your confidence and performance in Expedia's rigorous hiring process.
What Are Expedia LeetCode Interview Questions?
Expedia LeetCode interview questions are algorithmic challenges and data structure problems commonly used by Expedia Group in their technical interviews for software engineering roles. Unlike some companies that might delve into highly complex or obscure algorithms, Expedia tends to focus on practical, implementation-oriented problems. These questions typically span an easy to medium difficulty range, covering core computer science topics such as arrays, strings, linked lists, binary trees, basic dynamic programming, and greedy algorithms. The goal is to assess a candidate's fundamental coding skills, logical thinking, and ability to translate problem descriptions into functional and efficient code. Successful navigation of these questions demonstrates a solid grasp of data structures and algorithms, which are crucial for developing scalable and robust travel technology solutions. Preparing for these specific types of problems is key to excelling in an Expedia interview.
Why Do Interviewers Ask Expedia LeetCode Interview Questions?
Expedia interviewers ask LeetCode-style questions for several critical reasons, primarily to assess a candidate's foundational technical skills and problem-solving aptitude. First, these questions evaluate your ability to write clean, efficient, and well-structured code, a crucial skill for maintaining high-quality software within Expedia's large codebase. Second, they reveal your analytical thinking process—how you break down a complex problem, identify edge cases, and devise an optimal solution. Third, they test your knowledge of common data structures and algorithms, ensuring you have the necessary tools to build robust systems. Fourth, these coding challenges provide insight into how you communicate your thought process and defend your design choices, reflecting your collaboration potential. Finally, the ability to solve these problems under time constraints mirrors the demands of real-world software development, where effective and timely solutions are paramount for delivering exceptional travel experiences.
Preview List
1. Best Time to Buy and Sell Stock
2. Two Sum
3. Merge Intervals
4. Jump Game II
5. Valid Parentheses
6. Remove Duplicates from Sorted Array
7. Maximum Subarray
8. Climbing Stairs
9. Linked List Cycle
10. Reverse Linked List
11. Valid Anagram
12. Find Peak Element
13. Rotate Array
14. Intersection of Two Arrays
15. Binary Tree Inorder Traversal
16. Symmetric Tree
17. Maximum Depth of Binary Tree
18. Convert Sorted Array to BST
19. Binary Search
20. Implement strStr()
21. Longest Common Prefix
22. Min Stack
23. Valid Palindrome
24. Maximum Product Subarray
25. Word Break
26. Combination Sum
27. House Robber
28. Unique Paths
29. Search in Rotated Sorted Array
30. Container With Most Water
1. Best Time to Buy and Sell Stock
Why you might get asked this:
This problem tests your ability to find optimal solutions with a single pass through data, which is crucial for efficient data processing in real-world applications. It assesses dynamic programming or greedy approach thinking.
How to answer:
Iterate through the prices, keeping track of the minimum price seen so far and the maximum profit achievable by selling at the current price. Update both as you go.
Example answer:
To find the maximum profit, initialize `minprice` to infinity and `maxprofit` to zero. As you iterate through each price, update `minprice` if a lower price is found. Then, calculate the potential profit if sold today (`currentprice - minprice`) and update `maxprofit` if this is greater. This ensures optimal profit from one transaction.
2. Two Sum
Why you might get asked this:
A fundamental problem assessing your understanding of hash maps (dictionaries) for efficient lookups, which is a common technique in software development for optimizing search operations.
How to answer:
Use a hash map to store numbers and their indices. For each number, check if `target - number` exists in the hash map. If it does, return the current index and the stored index.
Example answer:
Create a hash map to store `(value, index)` pairs. Iterate through the array; for each `num`, calculate `complement = target - num`. Check if `complement` is in the map. If yes, you found the pair. Otherwise, add `num` and its index to the map. This avoids nested loops, providing O(n) time complexity.
3. Merge Intervals
Why you might get asked this:
This problem tests your ability to handle overlapping ranges and sort data effectively, common tasks in scheduling, data aggregation, or calendar applications.
How to answer:
Sort intervals by their start times. Iterate through the sorted intervals, merging the current interval with the last merged interval if they overlap. Otherwise, add the current interval to the result.
Example answer:
First, sort the intervals based on their start times. Initialize an empty list for merged intervals. Iterate through the sorted intervals. If the current interval overlaps with the last one in your merged list, extend the end of the last merged interval. Otherwise, add the current interval as a new merged interval.
4. Jump Game II
Why you might get asked this:
This question evaluates your greedy algorithm skills or dynamic programming approach for pathfinding problems, which is applicable in network routing or resource allocation.
How to answer:
Use a greedy approach. Track the farthest reach from the current position and the number of jumps. When the current jump boundary is reached, increment jumps and update the boundary.
Example answer:
Start at index 0. Maintain `jumps` (number of jumps), `currentend` (max reachable index with current jumps), and `farthest` (max reachable index overall). Iterate through the array: update `farthest` with `i + nums[i]`. If `i` reaches `currentend`, increment `jumps` and set `current_end` to `farthest`.
5. Valid Parentheses
Why you might get asked this:
Assesses your understanding of stack data structures, critical for parsing expressions, compiler design, and validating syntax in various programming contexts.
How to answer:
Use a stack. Push opening parentheses onto the stack. When a closing parenthesis appears, pop from the stack and check for a match. If mismatch or empty stack, it's invalid.
Example answer:
Initialize an empty stack and a map for parenthesis pairs. Iterate through the string. If an opening parenthesis, push onto stack. If closing, check if stack is empty (invalid) or if top matches the closing one. Pop if matched, else invalid. Finally, stack must be empty for validity.
6. Remove Duplicates from Sorted Array
Why you might get asked this:
Tests your in-place array manipulation skills and two-pointer technique, useful for optimizing memory usage in data processing algorithms.
How to answer:
Use two pointers: one (`i`) to track the unique elements' position and another (`j`) to iterate through the array. If `nums[j]` is different from `nums[i]`, increment `i` and set `nums[i] = nums[j]`.
Example answer:
Use a "slow" pointer `i` starting at 0 and a "fast" pointer `j` starting at 1. If `nums[j]` is different from `nums[i]`, increment `i` and then set `nums[i] = nums[j]`. This moves unique elements to the front of the array. The length of unique elements is `i + 1`.
7. Maximum Subarray
Why you might get asked this:
A classic dynamic programming problem (Kadane's Algorithm) that evaluates your ability to optimize solutions for sequence-based problems, common in financial analysis.
How to answer:
Apply Kadane's Algorithm: maintain a `currentmax` sum ending at the current position and a `globalmax` sum overall. Update `currentmax` by taking the maximum of the current element itself or `currentelement + current_max`.
Example answer:
Initialize `maxsofar` and `currentmax` to the first element. Iterate from the second element. For each element, `currentmax` becomes `max(element, currentmax + element)`. Then, `maxsofar` becomes `max(maxsofar, currentmax)`. Return `maxsofar` at the end.
8. Climbing Stairs
Why you might get asked this:
A simple dynamic programming problem that introduces the concept of memoization and recurrence relations, foundational for many optimization problems.
How to answer:
This is a Fibonacci sequence problem. The number of ways to reach step `n` is the sum of ways to reach `n-1` and `n-2`. Use dynamic programming or memoization.
Example answer:
Create a DP array `dp` where `dp[i]` is the number of ways to climb to step `i`. Initialize `dp[1] = 1` and `dp[2] = 2`. For `i` from 3 to `n`, `dp[i] = dp[i-1] + dp[i-2]`. Return `dp[n]`. This builds solutions from smaller subproblems.
9. Linked List Cycle
Why you might get asked this:
Tests your understanding of linked lists and pointer manipulation using Floyd's Tortoise and Hare algorithm, essential for detecting anomalies in data structures.
How to answer:
Use two pointers, 'slow' and 'fast'. The 'slow' pointer moves one step at a time, and the 'fast' pointer moves two steps. If they meet, a cycle exists.
Example answer:
Initialize `slow` and `fast` pointers to the head. Advance `slow` by one step and `fast` by two steps in each iteration. If `fast` or `fast.next` becomes `None`, no cycle exists. If `slow` and `fast` meet at any point, a cycle is detected.
10. Reverse Linked List
Why you might get asked this:
A fundamental linked list manipulation problem that assesses your pointer handling skills, crucial for maintaining data integrity in dynamic data structures.
How to answer:
Iteratively reverse: keep track of `prev`, `curr`, and `nextnode`. In each step, set `curr.next = prev`, then update `prev = curr` and `curr = nextnode`.
Example answer:
Initialize `prev` to `None` and `current` to `head`. Iterate while `current` is not `None`. In each step, store `current.next` as `nexttemp`, then set `current.next` to `prev`. Update `prev` to `current` and `current` to `nexttemp`. Finally, `prev` will be the new head.
11. Valid Anagram
Why you might get asked this:
Checks your ability to manipulate strings and use hash maps or frequency arrays to compare character compositions, a common task in text processing.
How to answer:
Count character frequencies for both strings using an array (for ASCII/Unicode) or a hash map. If the frequency counts for all characters match, they are anagrams.
Example answer:
If lengths of `s` and `t` differ, they aren't anagrams. Create a frequency array (size 26 for lowercase English letters). Iterate `s`, incrementing counts. Iterate `t`, decrementing counts. Finally, check if all counts in the array are zero.
12. Find Peak Element
Why you might get asked this:
Assesses your understanding of binary search in modified scenarios, useful for optimization problems where a specific extremal point needs to be located efficiently.
How to answer:
Apply a modified binary search. If `nums[mid] > nums[mid+1]`, the peak is in the left half (including `mid`). Else, it's in the right half (excluding `mid`).
Example answer:
Use binary search on the array. If `mid` is a peak (greater than its neighbors), return `mid`. If `nums[mid] < nums[mid+1]`, the peak must be to the right, so search `mid+1` to `right`. Otherwise (`nums[mid] > nums[mid+1]`), search `left` to `mid`.
13. Rotate Array
Why you might get asked this:
Evaluates your array manipulation skills, specifically in-place operations, important for optimizing memory and performance in data restructuring.
How to answer:
The reversal algorithm is efficient: reverse the entire array, then reverse the first `k` elements, and finally reverse the remaining `n-k` elements. Handle `k` modulo `n`.
Example answer:
Normalize `k` by `k % n`. Reverse the entire array. Then, reverse the first `k` elements. Finally, reverse the remaining `n - k` elements from index `k` to `n-1`. This rotates the array in-place with O(n) time complexity.
14. Intersection of Two Arrays
Why you might get asked this:
Tests your proficiency with set data structures for efficient membership testing and unique element collection, common in data filtering.
How to answer:
Convert both arrays to sets. The intersection can then be found using set intersection operations, which are very efficient. Alternatively, use a hash map to count elements.
Example answer:
Convert `nums1` into a hash set for O(1) average time lookups. Iterate through `nums2`. If an element from `nums2` is present in the hash set, add it to a result set and remove it from the first set to handle duplicates properly if required (or just add if duplicates are fine). Convert the result set back to an array.
15. Binary Tree Inorder Traversal
Why you might get asked this:
A fundamental tree traversal method that assesses your understanding of recursion or iterative approaches using stacks, essential for processing hierarchical data.
How to answer:
Recursively: traverse left subtree, visit root, traverse right subtree. Iteratively: use a stack; push nodes while going left, pop and visit, then go right.
Example answer:
For iterative inorder traversal: Initialize an empty stack and `current` node to `root`. While `current` is not `None` or stack is not empty, if `current` exists, push it onto stack and move `current = current.left`. Else, `current = stack.pop()`, visit `current.val`, and move `current = current.right`.
16. Symmetric Tree
Why you might get asked this:
Assesses your recursive problem-solving for tree structures and your ability to compare subtrees for structural and value equality, relevant for data validation.
How to answer:
Implement a helper function that checks if two subtrees are mirrors of each other. This involves comparing root values, and then recursively checking `left1` with `right2` and `right1` with `left2`.
Example answer:
Define a helper function, say `ismirror(t1, t2)`. If both are `None`, return `True`. If one is `None` and other is not, return `False`. If their values differ, return `False`. Otherwise, recursively call `ismirror(t1.left, t2.right)` AND `ismirror(t1.right, t2.left)`. Start with `ismirror(root.left, root.right)`.
17. Maximum Depth of Binary Tree
Why you might get asked this:
Tests your recursive thinking and understanding of tree properties, crucial for optimizing tree-based algorithms and managing tree memory.
How to answer:
Use recursion (DFS). The maximum depth of a node is `1 + max(depth(leftchild), depth(rightchild))`. The base case is a `None` node, which has depth 0.
Example answer:
If `root` is `None`, the depth is 0. Otherwise, recursively calculate the maximum depth of the left subtree and the right subtree. The maximum depth of the entire tree is `1 + max(maxdepth(root.left), maxdepth(root.right))`.
18. Convert Sorted Array to BST
Why you might get asked this:
Evaluates your ability to build balanced binary search trees, which are essential for efficient search, insertion, and deletion operations in structured data.
How to answer:
Recursively pick the middle element of the array as the root. The left half becomes the left subtree, and the right half becomes the right subtree.
Example answer:
Define a recursive helper function `buildbst(left, right)`. If `left > right`, return `None`. Calculate `mid = (left + right) // 2`. Create a new node with `nums[mid]`. Recursively set `node.left = buildbst(left, mid - 1)` and `node.right = buildbst(mid + 1, right)`. Return `node`. Call `buildbst(0, len(nums) - 1)`.
19. Binary Search
Why you might get asked this:
A fundamental algorithm for searching in sorted data, assessing your basic algorithmic thinking and ability to optimize search operations.
How to answer:
Repeatedly divide the search interval in half. Compare the target value with the middle element. If they match, return the index. Adjust the interval (left or right) based on the comparison.
Example answer:
Initialize `left = 0` and `right = len(nums) - 1`. While `left <= right`, calculate `mid = left + (right - left) // 2`. If `nums[mid]` is `target`, return `mid`. If `nums[mid] < target`, set `left = mid + 1`. Else, set `right = mid - 1`. Return -1 if not found.
20. Implement strStr()
Why you might get asked this:
Tests your string manipulation skills and basic pattern matching, useful in text editors, search functions, and data parsing.
How to answer:
Iterate through `haystack` with a window of `needle`'s length. Compare the substring at the current window with `needle`. Return the starting index if a match is found.
Example answer:
Iterate `i` from 0 to `len(haystack) - len(needle)`. For each `i`, check if the substring `haystack[i : i + len(needle)]` is equal to `needle`. If they match, return `i`. If no match is found after checking all possible starting positions, return -1.
21. Longest Common Prefix
Why you might get asked this:
Assesses your string processing abilities, specifically identifying common patterns, which is useful in file systems, auto-completion, and data normalization.
How to answer:
Start with the first string as the prefix. Then, for each subsequent string, shorten the prefix from its end until it matches the beginning of the current string.
Example answer:
If the array of strings is empty, return an empty string. Initialize `prefix` with the first string. Iterate from the second string onwards. While the current `prefix` is not a prefix of the current string, shorten `prefix` by one character from the end. Return the final `prefix`.
22. Min Stack
Why you might get asked this:
Tests your ability to extend data structures to maintain additional properties efficiently, crucial for optimizing specific operations beyond standard behavior.
How to answer:
Use a second stack to store minimum values. When pushing, if the new element is less than or equal to the current minimum, push it onto the min stack. When popping, if the popped element is the current minimum, pop from the min stack.
Example answer:
Maintain two stacks: `datastack` for regular elements and `minstack` for tracking minimums. When pushing `x` to `datastack`, push `x` to `minstack` only if `minstack` is empty or `x` is less than or equal to `minstack.top()`. When popping, if `datastack.top()` equals `minstack.top()`, pop from `min_stack` too.
23. Valid Palindrome
Why you might get asked this:
Evaluates your string manipulation skills, specifically filtering characters and performing two-pointer comparisons, useful in data validation and text analysis.
How to answer:
Convert the string to lowercase and remove non-alphanumeric characters. Then, use two pointers (one from start, one from end) to compare characters moving inward.
Example answer:
First, preprocess the string: convert all characters to lowercase and filter out non-alphanumeric characters. Then, use two pointers, `left` starting at 0 and `right` at the end of the processed string. Move `left` forward and `right` backward, comparing characters. If any mismatch, it's not a palindrome.
24. Maximum Product Subarray
Why you might get asked this:
A more complex dynamic programming problem than Maximum Subarray, requiring careful tracking of both maximum and minimum products due to negative numbers.
How to answer:
Track `maxsofar` and `minsofar` at each step. For each number, calculate potential new max/min products by considering `num`, `num maxsofar`, and `num minsofar`. Update `result` with the overall maximum.
Example answer:
Initialize `maxprod = minprod = result = nums[0]`. Iterate from the second element. `tempmaxprod` stores the current `maxprod` before it's updated. `maxprod` becomes `max(num, maxprod * num, minprod num)`. `minprod` becomes `min(num, tempmax_prod num, minprod * num)`. Update `result = max(result, maxprod)`.
25. Word Break
Why you might get asked this:
A classic dynamic programming problem assessing your ability to break down problems into subproblems and optimize overlapping computations, relevant for natural language processing.
How to answer:
Use dynamic programming. `dp[i]` is `True` if `s[0...i-1]` can be segmented. Iterate `j` from 0 to `i-1`. If `dp[j]` is `True` and `s[j...i-1]` is in the `wordDict`, then `dp[i]` is `True`.
Example answer:
Create a boolean DP array `dp` of size `len(s) + 1`, `dp[0] = True` (empty string is always valid). Iterate `i` from 1 to `len(s)`. For each `i`, iterate `j` from 0 to `i-1`. If `dp[j]` is `True` and `s[j:i]` is in the `wordDict`, set `dp[i] = True` and break the inner loop. Return `dp[len(s)]`.
26. Combination Sum
Why you might get asked this:
Tests your backtracking and recursive problem-solving skills for finding all possible combinations, applicable in areas like financial modeling or resource allocation.
How to answer:
Use backtracking (DFS). For each number, you can either include it or not. If included, recursively call with the remaining target. Ensure unique combinations by handling duplicates or sorting.
Example answer:
Implement a recursive backtracking function `findcombinations(target, startindex, currentcombination)`. If `target` is 0, add `currentcombination` to results. If `target < 0`, return. Iterate `i` from `startindex` to `len(candidates)-1`. Add `candidates[i]` to `currentcombination`, then recursively call `findcombinations(target - candidates[i], i, currentcombination)`. Backtrack by removing `candidates[i]`.
27. House Robber
Why you might get asked this:
Another dynamic programming problem focused on optimal substructure, relevant for resource optimization where adjacent selections are constrained.
How to answer:
Use dynamic programming. For each house, the maximum money you can rob is the maximum of: (money from current house + money from `i-2` houses ago) or (money from `i-1` houses ago).
Example answer:
Initialize `dp[0] = nums[0]` (if `nums` exists), `dp[1] = max(nums[0], nums[1])`. For `i` from 2 to `n-1`, `dp[i] = max(dp[i-1], nums[i] + dp[i-2])`. The result is `dp[n-1]`. This calculates the maximum possible loot up to each house.
28. Unique Paths
Why you might get asked this:
A classic dynamic programming or combinatorial problem that assesses your ability to count distinct paths in a grid, applicable in robotics or pathfinding.
How to answer:
Use dynamic programming. The number of unique paths to cell `(i, j)` is the sum of unique paths to `(i-1, j)` and `(i, j-1)`. Initialize the first row and column with 1s.
Example answer:
Create a 2D DP array `dp[m][n]`. Initialize `dp[i][0] = 1` for all `i` and `dp[0][j] = 1` for all `j`. For `i` from 1 to `m-1` and `j` from 1 to `n-1`, `dp[i][j] = dp[i-1][j] + dp[i][j-1]`. Return `dp[m-1][n-1]`.
29. Search in Rotated Sorted Array
Why you might get asked this:
A tricky binary search variation testing your ability to adapt standard algorithms to modified constraints, critical for efficient searching in non-standard data.
How to answer:
Perform a modified binary search. Determine which half of the array is sorted. Then, check if the target falls within that sorted half. Adjust search boundaries accordingly.
Example answer:
Apply binary search. Find `mid`. Determine if the left half (`nums[left]` to `nums[mid]`) is sorted. If `target` is in this sorted left half, search `left` to `mid-1`. Otherwise, search `mid+1` to `right`. If the right half is sorted, apply similar logic.
30. Container With Most Water
Why you might get asked this:
Tests your two-pointer technique and greedy approach for optimizing area calculations, which can be extended to geometric or resource allocation problems.
How to answer:
Use two pointers, one at each end. Calculate area. Move the pointer pointing to the shorter line inward, as moving the taller one won't increase the height and might decrease the width.
Example answer:
Initialize `left = 0`, `right = len(height) - 1`, and `maxarea = 0`. While `left < right`: calculate `currentarea = min(height[left], height[right]) * (right - left)`. Update `maxarea = max(maxarea, current_area)`. If `height[left] < height[right]`, increment `left`. Else, decrement `right`.
Other Tips to Prepare for a Expedia LeetCode Interview
Preparing for Expedia LeetCode interview questions involves more than just memorizing solutions; it's about building a robust problem-solving mindset. Firstly, consistent practice is paramount. Don't just solve problems; understand the underlying data structures and algorithms. As the saying goes, "Confidence comes from preparation." Regularly tackling a diverse set of LeetCode problems, especially those focused on arrays, strings, linked lists, trees, and dynamic programming basics, will solidify your understanding.
Secondly, hone your communication skills. During an Expedia interview, articulating your thought process clearly, explaining your approach, discussing trade-offs, and handling edge cases is as important as the correct code. Practice explaining your solutions out loud as if to an interviewer. Verve AI Interview Copilot can be an invaluable tool here. It offers mock interview simulations and instant feedback, helping you refine your explanations and identify areas for improvement before your actual Expedia interview.
Thirdly, focus on writing clean, efficient, and well-commented code. Expedia values maintainable code. Pay attention to time and space complexity. Consider using Verve AI Interview Copilot (https://vervecopilot.com) to get objective feedback on your code's structure and efficiency. Fourthly, perform mock interviews. Whether with a peer or an AI-powered platform like Verve AI Interview Copilot, simulating the interview environment helps manage nerves and improves performance under pressure. Regularly practicing with tools like Verve AI Interview Copilot can significantly enhance your readiness for any Expedia LeetCode interview questions.
Frequently Asked Questions Q1: How difficult are Expedia LeetCode interview questions generally? A1: Expedia typically asks easy to medium difficulty LeetCode problems, focusing on foundational data structures and algorithms rather than highly complex ones.
Q2: What common topics should I prioritize for Expedia coding interviews? A2: Focus on arrays, strings, linked lists, binary trees, stacks, basic dynamic programming, and greedy algorithms.
Q3: Is system design a part of Expedia's software engineering interviews? A3: Yes, for more senior roles, system design questions are often included alongside coding challenges in Expedia's interview process.
Q4: How important is code efficiency (time/space complexity) at Expedia interviews? A4: Highly important. Interviewers expect candidates to discuss and optimize for time and space complexity, demonstrating an understanding of efficient solutions.
Q5: Should I use a specific programming language for Expedia LeetCode problems? A5: You can generally use any common language (Python, Java, C++, JavaScript). Choose one you are most proficient in to write clean and efficient code.
Kent McAllister
Career Advisor
