Prepare for Shopee interviews! Get the top 30 most common LeetCode questions. Ace your Shopee coding interview and land the job!
Navigating the landscape of technical interviews can be challenging, especially when targeting leading tech companies like Shopee. A key component of Shopee's hiring process for software engineers and other technical roles often involves rigorous coding assessments. These typically center around data structures and algorithms, commonly encountered as LeetCode-style problems. Success in these interviews hinges on a strong grasp of fundamental computer science concepts and the ability to apply them efficiently to solve complex problems under pressure. Preparing effectively means not just memorizing solutions but understanding the underlying logic, time and space complexity, and how to articulate your thought process clearly.
What Are Shopee LeetCode Interview Questions?
Shopee LeetCode interview questions refer to the type of coding challenges often posed during Shopee's technical interviews, which closely resemble problems found on platforms like LeetCode. These questions are designed to assess a candidate's problem-solving skills, algorithmic thinking, and coding proficiency. They span a wide range of topics including, but not limited to, arrays, linked lists, trees, graphs, dynamic programming, sorting, searching, and string manipulation. While Shopee does not publish an official list of questions, the problems frequently align with classic and widely used challenges that test core computer science fundamentals. Candidates are expected to not only produce correct code but also to discuss their approach, potential optimizations, and handle various edge cases, demonstrating a comprehensive understanding of the solution.
Why Do Interviewers Ask Shopee LeetCode Interview Questions?
Interviewers at Shopee ask LeetCode-style questions for several strategic reasons. Primarily, these questions serve as a standardized method to evaluate a candidate's foundational programming skills and analytical capabilities. They help interviewers gauge how effectively a candidate can break down a complex problem into smaller, manageable parts, devise an efficient algorithm, and translate that algorithm into clean, working code. Such questions also reveal a candidate's understanding of time and space complexity, crucial for building scalable and performant software. Beyond technical prowess, these challenges illuminate a candidate's problem-solving methodology, their ability to think under pressure, communicate their thought process, and handle unexpected scenarios or constraints. It's less about finding a perfect solution immediately and more about demonstrating a structured approach to problem-solving.
Preview List
1. Two Sum
2. Best Time to Buy and Sell Stock
3. Contains Duplicate
4. Product of Array Except Self
5. Plus One
6. Longest Substring Without Repeating Characters
7. Group Anagrams
8. Reverse Linked List
9. Merge Two Sorted Lists
10. Linked List Cycle
11. Remove Nth Node From End of List
12. Copy List with Random Pointer
13. Maximum Depth of Binary Tree
14. Validate Binary Search Tree
15. Binary Tree Level Order Traversal
16. Lowest Common Ancestor of a Binary Tree
17. Invert Binary Tree
18. Number of Islands
19. Clone Graph
20. Course Schedule
21. Merge Intervals
22. Search in Rotated Sorted Array
23. Top K Frequent Elements
24. Kth Largest Element in an Array
25. Find Peak Element
26. Climbing Stairs
27. Coin Change
28. Longest Increasing Subsequence
29. Subsets
30. Word Break
1. How do you find two numbers in an array that sum to a target (Two Sum)?
Why you might get asked this:
This classic problem tests your understanding of hash maps for efficient lookups, demonstrating your ability to optimize for time complexity over brute force.
How to answer:
Use a hash map to store numbers and their indices. For each number, check if `target - current_number` exists in the map. If yes, return the indices.
Example answer:
Iterate through the array, for each `num`, calculate `complement = target - num`. Check if `complement` exists in a hash map. If it does, return `[map[complement], index]`. Otherwise, add `num` and its `index` to the map.
2. What's the best strategy to maximize profit from stock transactions (Best Time to Buy and Sell Stock)?
Why you might get asked this:
This problem assesses your ability to track minimums and calculate maximum differences efficiently, often solvable with a single pass.
How to answer:
Iterate through prices, keeping track of the minimum price seen so far. At each step, calculate the potential profit and update the maximum profit.
Example answer:
Initialize `minprice` to infinity and `maxprofit` to zero. Iterate through `prices`. If `price < minprice`, update `minprice`. Else, if `price - minprice > maxprofit`, update `maxprofit`. Return `maxprofit`.
3. How can you detect if an array contains duplicate elements (Contains Duplicate)?
Why you might get asked this:
This tests your knowledge of efficient data structures like hash sets to check for element uniqueness, highlighting optimal time complexity.
How to answer:
Use a hash set to store elements encountered. If an element is already in the set when checking, a duplicate exists.
Example answer:
Initialize an empty hash set. Iterate through each number in the array. If the number is already present in the set, return `true`. Otherwise, add the number to the set. If the loop completes without finding duplicates, return `false`.
4. How do you compute the product of all elements except self in an array (Product of Array Except Self)?
Why you might get asked this:
This problem checks your ability to think about prefix and suffix products, avoiding division, and often requires constant space optimization.
How to answer:
First, calculate prefix products from left to right. Then, calculate suffix products from right to left, multiplying them to get the final result.
Example answer:
Create a `result` array. First pass: `result[i]` stores product of elements before `i`. Second pass: `result[i]` is multiplied by product of elements after `i`. Maintain a `suffix_product` variable.
5. How do you increment a large integer represented as an array of digits (Plus One)?
Why you might get asked this:
This tests your handling of digit arithmetic, especially carry-overs and array resizing, mimicking real-world number representations.
How to answer:
Iterate from the rightmost digit. Add one. If there's a carry-over, continue leftwards. If a carry remains at the start, prepend a '1'.
Example answer:
Loop from `digits.length - 1` down to `0`. Increment `digits[i]`. If `digits[i]` becomes 10, set it to 0 and continue loop (carry). Otherwise, break. If `digits[0]` had a carry, unshift `1` to `digits`.
6. How do you find the longest substring without repeating characters?
Why you might get asked this:
This assesses your skill with sliding window techniques and hash maps/sets for efficient character tracking and uniqueness checks.
How to answer:
Use a sliding window with two pointers (left, right) and a hash set. Expand the window with `right`. If a duplicate is found, shrink from `left`.
Example answer:
Initialize `left = 0`, `maxlen = 0`, and a `charset`. Iterate `right` from 0. If `s[right]` is in `charset`, remove `s[left]` from set and increment `left` until no duplicate. Add `s[right]` and update `maxlen`.
7. How can you group anagrams together from a list of strings?
Why you might get asked this:
This tests your understanding of canonical forms (like sorted strings or character counts) and hash maps for grouping.
How to answer:
For each string, create a canonical representation (e.g., sort the string). Use this sorted string as a key in a hash map, mapping to a list of original strings.
Example answer:
Initialize a hash map where keys are sorted strings and values are lists of original strings. Iterate through the input `strs`. For each `s`, sort `s` to get `key`. Add `s` to `map[key]`. Return `map.values()`.
8. How do you reverse a singly linked list?
Why you might get asked this:
A fundamental linked list problem, it checks your ability to manipulate pointers iteratively or recursively without losing data.
How to answer:
Iteratively, keep track of `prev`, `curr`, and `next_temp`. In each step, make `curr.next` point to `prev`, then update `prev` and `curr`.
Example answer:
Initialize `prev = null` and `curr = head`. While `curr` is not null: store `curr.next` as `nexttemp`, set `curr.next = prev`, update `prev = curr`, then set `curr = nexttemp`. Return `prev`.
9. How do you merge two sorted linked lists into one?
Why you might get asked this:
This evaluates your pointer manipulation skills for sorted data structures, often solved with a dummy head node.
How to answer:
Use a dummy head node. Compare the current nodes of both lists, appending the smaller one to the merged list and advancing its pointer.
Example answer:
Create a `dummy` node and a `tail` pointer pointing to `dummy`. While both lists have nodes, compare their values. Append the smaller node to `tail.next`, then advance `tail` and the chosen list's pointer. Append any remaining list. Return `dummy.next`.
10. How can you detect a cycle in a linked list?
Why you might get asked this:
This classic problem assesses your understanding of the "Floyd's Cycle-Finding Algorithm" (fast and slow pointers).
How to answer:
Use two pointers, `slow` and `fast`. `slow` moves one step at a time, `fast` moves two steps. If they meet, there's a cycle.
Example answer:
Initialize `slow = head` and `fast = head`. While `fast` and `fast.next` are not null, move `slow = slow.next` and `fast = fast.next.next`. If `slow == fast`, return `true`. If loop finishes, return `false`.
11. How do you remove the Nth node from the end of a linked list?
Why you might get asked this:
This tests your ability to use two pointers with an offset to precisely locate a node relative to the end of the list.
How to answer:
Use two pointers, `first` and `second`. Advance `first` `n` steps. Then, advance both until `first` reaches the end. `second` will be at the node before the target.
Example answer:
Create a `dummy` node pointing to `head`. Initialize `first = dummy` and `second = dummy`. Move `first` `n+1` steps. Then, move `first` and `second` simultaneously until `first` is null. `second.next` is the node to remove. Update `second.next = second.next.next`. Return `dummy.next`.
12. How do you copy a linked list with random pointers?
Why you might get asked this:
This advanced linked list problem tests your ability to handle complex pointer relationships, often using a hash map or interwoven lists.
How to answer:
First pass: create copy nodes and interweave them (original -> copy -> original -> ...). Second pass: assign random pointers using this new structure. Third pass: separate original and copy lists.
Example answer:
Iterate to create new nodes and insert them between original nodes (`original -> new -> originalnext`). Then, iterate again to set `newnode.random = originalnode.random.next` (if `originalnode.random` exists). Finally, separate the lists.
13. How do you find the maximum depth of a binary tree?
Why you might get asked this:
A foundational tree problem, it checks your understanding of recursion or iterative traversals (BFS/DFS) for tree properties.
How to answer:
Recursively, the maximum depth is 1 + maximum of (left child's depth, right child's depth). Base case: null node has depth 0.
Example answer:
If `root` is null, return 0. Otherwise, return `1 + Math.max(maxDepth(root.left), maxDepth(root.right))`. This recursive approach efficiently calculates the maximum depth.
14. How do you validate if a binary tree is a Binary Search Tree (BST)?
Why you might get asked this:
This tests your understanding of BST properties (left < root < right) and how to maintain boundary checks during traversal.
How to answer:
Recursively traverse the tree, passing a `min` and `max` bound. Each node's value must be within its current bounds.
Example answer:
Define a helper function `isValid(node, minval, maxval)`. If `node` is null, return true. If `node.val <= minval` or `node.val >= maxval`, return false. Recursively call `isValid(node.left, minval, node.val)` and `isValid(node.right, node.val, maxval)`.
15. How do you perform a level order traversal of a binary tree?
Why you might get asked this:
This assesses your ability to use Breadth-First Search (BFS) with a queue to visit nodes level by level.
How to answer:
Use a queue. Add the root. While the queue is not empty, dequeue a node, add its value to the current level's list, and enqueue its children.
Example answer:
Initialize `result = []` and `queue = [root]` (if root exists). While `queue` is not empty: get `levelsize`, create `currentlevel = []`. Loop `levelsize` times: dequeue `node`, add `node.val` to `currentlevel`, enqueue `node.left`, `node.right`. Add `current_level` to `result`.
16. How do you find the lowest common ancestor (LCA) of two nodes in a binary tree?
Why you might get asked this:
This non-BST tree problem tests your recursive thinking and ability to identify when a node is an ancestor to both targets.
How to answer:
Recursively search. If a node is `p` or `q`, return it. If a node's left and right subtrees both contain `p` and `q` respectively, that node is the LCA.
Example answer:
If `root` is null, `p`, or `q`, return `root`. Recursively call `findLCA` on `root.left` and `root.right`. If both calls return non-null, `root` is LCA. Otherwise, return the non-null result from left or right.
17. How do you invert a binary tree?
Why you might get asked this:
A simple but effective check of tree recursion or iterative traversal with node swapping.
How to answer:
Recursively swap the left and right children of each node. Base case: null node does nothing.
Example answer:
If `root` is null, return null. Swap `root.left` and `root.right`. Recursively call `invertTree(root.left)` and `invertTree(root.right)`. Return `root`.
18. How do you count the number of islands in a 2D binary grid?
Why you might get asked this:
This graph problem assesses your ability to use DFS or BFS to traverse connected components in a grid, often with grid modification.
How to answer:
Iterate through the grid. If '1' is found, increment island count and start DFS/BFS from that cell, turning all connected '1's to '0's to mark as visited.
Example answer:
Initialize `count = 0`. Iterate `row` and `col`. If `grid[row][col] == '1'`: increment `count`, then call `dfs(grid, row, col)` to mark the current island. `dfs` recursively marks neighbors as '0' if '1'.
19. How do you clone a graph?
Why you might get asked this:
This problem tests your understanding of graph traversal (DFS or BFS) and how to manage visited nodes and create deep copies to avoid cycles and redundant copies.
How to answer:
Use DFS or BFS. Maintain a hash map to store mappings from original nodes to cloned nodes. If a node has been visited and copied, return its clone from the map.
Example answer:
Use a hash map `visitedmap` to store `originalnode -> clonednode` mappings. Start DFS/BFS from `node`. Create `clonenode`. Add `node -> clonenode` to map. Recursively/iteratively clone neighbors, setting `clonenode.neighbors`.
20. How do you determine if a set of courses can be taken given prerequisites (Course Schedule)?
Why you might get asked this:
This graph problem involves cycle detection in a directed graph, often solved using topological sort (DFS or BFS with in-degrees).
How to answer:
Model as a directed graph. Detect if there's a cycle. A graph can be topologically sorted if and only if it is a Directed Acyclic Graph (DAG).
Example answer:
Build an adjacency list and an `indegree` array for each course. Add courses with `indegree` 0 to a queue. While queue is not empty, dequeue `course`, decrement `indegree` of its neighbors. If neighbor's `indegree` becomes 0, enqueue it. Count processed courses. If `count == numCourses`, return true.
21. How do you merge overlapping intervals?
Why you might get asked this:
This problem tests your ability to sort intervals and then iterate to combine those that overlap, a common task in scheduling.
How to answer:
Sort the intervals by their start times. Iterate through the sorted intervals, merging current with the next if they overlap, otherwise add current to result.
Example answer:
Sort `intervals` by `interval[0]`. Initialize `merged = []`. For each `interval`: if `merged` is empty or current `interval[0]` > last `merged` end, add `interval`. Else, update last `merged` end to `max(lastmergedend, interval[1])`.
22. How do you search for an element in a rotated sorted array?
Why you might get asked this:
This is a modification of binary search, requiring careful handling of the pivot point introduced by rotation.
How to answer:
Use a modified binary search. Determine which half of the array is sorted, then check if the target falls within that sorted half. Adjust search space accordingly.
Example answer:
Perform binary search. If `nums[mid] == target`, return `mid`. Determine if left half (`nums[low]` to `nums[mid]`) is sorted. If target is in left sorted half, `high = mid - 1`. Else `low = mid + 1`. Handle right half similarly.
23. How do you find the K most frequent elements in an array?
Why you might get asked this:
This problem combines frequency counting (hash map) with efficient selection (min-heap or bucket sort).
How to answer:
Use a hash map to count frequencies. Then, use a min-heap to keep track of the `K` most frequent elements, or use bucket sort based on frequencies.
Example answer:
Count frequencies using a hash map. Create a min-priority queue (min-heap). Iterate through map entries. Push `[frequency, number]` to heap. If heap size > K, pop. Extract numbers from heap.
24. How do you find the Kth largest element in an array?
Why you might get asked this:
This tests your understanding of selection algorithms, often using a min-heap (priority queue) or Quickselect.
How to answer:
Use a min-heap of size `K`. Iterate through the array; add elements to the heap. If heap size exceeds `K`, pop the smallest. The heap's top is the Kth largest.
Example answer:
Initialize a min-heap. Iterate through `nums`. Push each `num` onto the heap. If `heap.size > k`, `heap.pop()`. After iterating, `heap.top()` will be the Kth largest element.
25. How do you find a peak element in an array?
Why you might get asked this:
This problem often involves binary search to efficiently locate a peak, defined as an element greater than its neighbors.
How to answer:
Apply binary search. If `nums[mid]` is a peak, return `mid`. If `nums[mid] < nums[mid+1]`, the peak is on the right. If `nums[mid] < nums[mid-1]`, the peak is on the left.
Example answer:
Use binary search. Set `low = 0`, `high = len(nums) - 1`. While `low < high`: calculate `mid`. If `nums[mid] > nums[mid+1]`, then peak is `mid` or left, so `high = mid`. Else, `low = mid + 1`. Return `low`.
26. How many distinct ways can you climb to the top of n stairs (Climbing Stairs)?
Why you might get asked this:
A classic dynamic programming or Fibonacci-sequence problem, testing recursive thinking with memoization or iterative DP.
How to answer:
This is a Fibonacci sequence: `dp[i] = dp[i-1] + dp[i-2]`. Base cases `dp[1]=1`, `dp[2]=2`.
Example answer:
If `n <= 2`, return `n`. Create `dp` array. `dp[1] = 1`, `dp[2] = 2`. Loop from `i = 3` to `n`, `dp[i] = dp[i-1] + dp[i-2]`. Return `dp[n]`.
27. How do you find the minimum number of coins to make a given amount (Coin Change)?
Why you might get asked this:
This is a core dynamic programming problem testing state transitions and optimization for minimums.
How to answer:
Use dynamic programming. `dp[i]` represents the minimum coins for amount `i`. `dp[i] = min(dp[i - coin] + 1)` for all `coins`.
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`, `dp[i] = min(dp[i], dp[i - coin] + 1)`. Return `dp[amount]` (or -1 if infinity).
28. How do you find the longest increasing subsequence in an array?
Why you might get asked this:
This DP problem assesses your ability to define subproblems and state transitions, often requiring `O(N^2)` or `O(N log N)` solutions.
How to answer:
Use dynamic programming. `dp[i]` is the length of the LIS ending at index `i`. `dp[i] = max(dp[j] + 1)` for all `j < i` where `nums[j] < nums[i]`.
Example answer:
Initialize `dp` array, `dp[i]=1` for all `i`. Iterate `i` from 1 to `n-1`. For each `i`, iterate `j` from 0 to `i-1`. If `nums[i] > nums[j]`, `dp[i] = max(dp[i], dp[j] + 1)`. Return `max(dp)`.
29. How do you generate all possible subsets of a set of numbers?
Why you might get asked this:
This backtracking problem tests your recursive generation of combinations, often involving decision trees (include/exclude).
How to answer:
Use backtracking. At each step, decide whether to include the current number in the subset or not, then recurse.
Example answer:
Define `backtrack(startindex, currentsubset)`. Base case: add `currentsubset` to `results`. Loop `i` from `startindex` to `nums.length - 1`. Add `nums[i]` to `currentsubset`, call `backtrack(i + 1, currentsubset)`, then remove `nums[i]` (backtrack).
30. How do you determine if a string can be segmented into a space-separated sequence of dictionary words (Word Break)?
Why you might get asked this:
This DP problem involves breaking a string into parts and checking if each part is valid, often using a boolean DP array.
How to answer:
Use dynamic programming. `dp[i]` is true if `s[0...i-1]` can be segmented. `dp[i] = true` if `dp[j]` is true and `s[j...i-1]` is in the dictionary.
Example answer:
Initialize `dp` array of size `s.length + 1` with `false`, `dp[0] = true`. Iterate `i` from 1 to `s.length`. Iterate `j` from 0 to `i-1`. If `dp[j]` is true AND `s.substring(j, i)` is in `wordDict`, set `dp[i] = true` and break. Return `dp[s.length]`.
Other Tips to Prepare for a Shopee LeetCode Interview
Preparing for Shopee's technical interviews requires a strategic and consistent approach. Beyond just solving problems, focus on understanding the core concepts behind them. As coding expert John Resig famously said, "Always code as if the guy who ends up maintaining your code will be a violent psychopath who knows where you live." This emphasizes clarity, efficiency, and robustness in your solutions. Practice is paramount; repeatedly tackling problems across different categories helps build muscle memory for common patterns and algorithms. Optimize your solutions for both time and space complexity, and be ready to explain your choices.
Consider using tools like Verve AI Interview Copilot to simulate interview environments and get real-time feedback on your approach. This AI-powered tool can provide immediate insights, helping you refine your communication and problem-solving skills under pressure. Furthermore, actively review common interview topics like Linked Lists, Trees, Arrays, Sorting, and Graph traversal techniques. Leverage online resources, including YouTube tutorials and GitHub repositories focused on interview preparation. Remember, "The only way to do great work is to love what you do," so approach your preparation with curiosity and determination. Verve AI Interview Copilot at https://vervecopilot.com can be an invaluable asset, offering tailored practice sessions that mirror actual interview scenarios. Don't just memorize solutions; internalize the problem-solving process. Regular practice with Verve AI Interview Copilot can significantly boost your confidence and performance.
Frequently Asked Questions
Q1: How important are time and space complexity at Shopee interviews? A1: Very important. Interviewers expect you to analyze and optimize your solutions for both time and space efficiency.
Q2: Should I focus on specific programming languages for Shopee? A2: Shopee typically allows common languages like Python, Java, or C++. Choose the one you're most comfortable with for coding interviews.
Q3: Is memorizing solutions useful for Shopee LeetCode questions? A3: No, memorizing is counterproductive. Focus on understanding the underlying algorithms and problem-solving patterns.
Q4: How many LeetCode problems should I solve to prepare? A4: Aim for quality over quantity. Focus on mastering common patterns and difficult problems rather than just counting solved ones.
Q5: What if I get stuck on a problem during the interview? A5: Communicate your thought process. Explain your initial ideas, what's not working, and propose alternative approaches. Ask clarifying questions.
Kent McAllister
Career Advisor
