15 Must-Solve Algorithm Problems for Big Tech Interviews: Ranked by Pass Rate

15 Must-Solve Algorithm Problems for Big Tech Interviews: Ranked by Pass Rate

Introduction

I've solved over 400 LeetCode problems and interviewed at ByteDance, Alibaba, Meituan, Kuaishou, and Pinduoduo. I discovered that big tech algorithm interviews are highly concentrated. You don't need to solve 1000 problems — mastering these 15 highest-frequency problems covers 80%+ of algorithm test points. I've ranked them by pass rate from high to low, easy to hard, with LeetCode number, difficulty, key concepts, and solution approach for each. I recommend solving them in order, writing each at least twice — first to understand the approach, second to write from memory.

1. Two Sum (LeetCode #1, Easy, Pass Rate 52%)

Key Concepts: Hash table, array traversal

Problem: Given an array and target, find indices of two numbers that sum to target.

Solution: Brute force O(n²) works but isn't interview-worthy. Optimize to O(n) with hash table: iterate array, for each element check if target-nums[i] exists in hash table; if yes, return; if no, add current element. Key point: check before storing to avoid using the same element twice. Interview follow-up: What if multiple answers? Problem guarantees unique solution. Unsorted array? Hash table doesn't require ordering.

2. Reverse Linked List (LeetCode #206, Easy, Pass Rate 73%)

Key Concepts: Linked list operations, pointers

Problem: Reverse a singly linked list.

Solution: Iterative — three pointers prev, curr, next. Each step point curr.next to prev, then advance all three. Until curr is null, return prev. Recursive — recurse to end, reverse direction on backtrack. Must-know: Complexity of both? O(n) time, O(1)/O(n) space. Follow-up: Reverse part of a linked list? That's #92, add interval check.

3. Valid Parentheses (LeetCode #20, Easy, Pass Rate 41%)

Key Concepts: Stack, string processing

Problem: Determine if a parentheses string is valid.

Solution: Use a stack. Push left brackets, check stack top for matching right brackets. Valid if stack is empty after traversal. Optimization: When encountering left brackets, push the corresponding right bracket — then just check equality. Follow-up: Longest valid parentheses substring? That's #32, use stack or DP, difficulty jumps two levels.

4. Maximum Subarray (LeetCode #53, Medium, Pass Rate 55%)

Key Concepts: Dynamic programming, greedy

Problem: Find the contiguous subarray with the largest sum.

Solution: Kadane's algorithm — maintain current subarray sum cur and maximum sum res. If cur < 0, discard and restart (cur=nums[i]); otherwise continue accumulating. Update res=max(res,cur) each step. Time O(n), space O(1). Follow-up: Return subarray indices? Add two pointers. All negative? Kadane naturally handles — returns the largest negative number.

5. Merge Two Sorted Lists (LeetCode #21, Easy, Pass Rate 67%)

Key Concepts: Linked list operations, recursion

Problem: Merge two sorted linked lists into one sorted list.

Solution: Iterative — use dummy head, compare current nodes of both lists, attach smaller to result, advance pointer. When one list is exhausted, attach the remainder. Recursive — compare heads, smaller one's next points to recursive merge result. Follow-up: Merge K sorted lists? Use min-heap, always take smallest head. Time O(nklogk).

6. Binary Tree Level Order Traversal (LeetCode #102, Medium, Pass Rate 65%)

Key Concepts: BFS, queue

Problem: Traverse binary tree level by level, return node values per level.

Solution: BFS with queue. Key point: group by level — record queue length at each level start, loop that many times to dequeue, enqueue children. Follow-up: Bottom-up level order? Reverse result. Zigzag traversal? Reverse even levels. Right side view? Last node of each level. Must know all variants.

7. Climbing Stairs (LeetCode #70, Easy, Pass Rate 53%)

Key Concepts: Dynamic programming, recurrence

Problem: Climb 1 or 2 steps at a time, how many ways to reach step n?

Solution: Classic DP. dp[i]=dp[i-1]+dp[i-2], i.e., Fibonacci sequence. Space optimization: store only previous two values, O(1) space. Follow-up: Climb 1 to m steps? Complete knapsack, dp[i]=sum(dp[i-j]) for j in 1..m. Steps with cost? #746, dp[i]=min(dp[i-1],dp[i-2])+cost[i].

8. Best Time to Buy and Sell Stock (LeetCode #121, Easy, Pass Rate 58%)

Key Concepts: Dynamic programming, greedy

Problem: Only one transaction allowed, find maximum profit.

Solution: Iterate array, maintain minimum price so far (minPrice), calculate profit prices[i]-minPrice each day, update maximum profit. Time O(n), space O(1). Must-ask series: Two transactions? #123. Unlimited? #122, greedy. With cooldown? #309, state machine DP. With fee? #714. Recommended to study all 6 problems in this series together.

9. Longest Palindromic Substring (LeetCode #5, Medium, Pass Rate 37%)

Key Concepts: Dynamic programming, center expansion

Problem: Find the longest palindromic substring.

Solution: Center expansion — expand from each character (and between each pair) as center, record longest palindrome. Time O(n²), space O(1). DP — dp[i][j] indicates if s[i..j] is palindrome, dp[i][j]=dp[i+1][j-1]&&s[i]==s[j]. Time O(n²), space O(n²). Manacher's algorithm O(n) but not required for interviews. Follow-up: Count palindromic substrings? #647, same center expansion.

10. 3Sum (LeetCode #15, Medium, Pass Rate 36%)

Key Concepts: Sorting, two pointers, deduplication

Problem: Find all unique triplets that sum to zero.

Solution: Sort first O(nlogn), fix first number, use two pointers for remaining two. Left pointer starts at i+1, right at end, move based on sum. Key: Deduplication — skip if first number equals previous; after finding answer, skip duplicates for both pointers. Follow-up: 4Sum? #18, add another loop. Closest 3Sum? #16, track closest value to target.

11. Longest Substring Without Repeating Characters (LeetCode #3, Medium, Pass Rate 39%)

Key Concepts: Sliding window, hash table

Problem: Find length of longest substring without repeating characters.

Solution: Sliding window — use Map to track character's most recent position. Right pointer iterates string; if current character is in window (in map and position >= left pointer), jump left pointer to position+1. Update max length each step. Time O(n). Follow-up: Only lowercase letters? Use array instead of Map. At most K distinct characters? #340, use Map counting.

12. Merge Intervals (LeetCode #56, Medium, Pass Rate 49%)

Key Concepts: Sorting, interval operations

Problem: Merge all overlapping intervals.

Solution: Sort by left endpoint, iterate intervals. If current left <= last result's right, merge (update right to max); otherwise add to result. Follow-up: Insert interval? #57, handle in three segments. Interval list intersection? #986, two pointers for intersection. Meeting rooms? #252, check for overlap.

13. Number of Islands (LeetCode #200, Medium, Pass Rate 59%)

Key Concepts: DFS/BFS, grid traversal

Problem: Count the number of islands in a grid.

Solution: Iterate grid, when encountering '1', increment island count, then DFS/BFS to turn all connected '1's to '0's (sink method). DFS: recurse four directions. BFS: use queue. Time O(mn). Follow-up: Max island area? #695, count during DFS. Surrounded regions? #130, DFS from boundary. Island perimeter? #463, mathematical calculation.

14. LRU Cache (LeetCode #146, Medium, Pass Rate 42%)

Key Concepts: Hash table + doubly linked list, design

Problem: Implement LRU cache with O(1) get and put.

Solution: Hash table maps key to node, doubly linked list maintains access order. get: find node, move to head. put: if key exists, update value and move to head; if not, create new node at head, evict tail if over capacity. Must-test design question — I encountered it at ByteDance, Alibaba, and Meituan. Follow-up: Thread-safe LRU? Add locks or use ConcurrentHashMap.

15. Edit Distance (LeetCode #72, Hard, Pass Rate 61%)

Key Concepts: Dynamic programming, strings

Problem: Find minimum operations (insert, delete, replace) to convert word1 to word2.

Solution: Classic DP. dp[i][j] = minimum operations to convert first i chars of word1 to first j chars of word2. Transition: if word1[i-1]==word2[j-1], dp[i][j]=dp[i-1][j-1]; otherwise dp[i][j]=min(dp[i-1][j]+1, dp[i][j-1]+1, dp[i-1][j-1]+1) for delete, insert, replace. Time O(mn), space O(mn), optimizable to O(min(m,n)). Follow-up: Only insert and delete? Remove replace. Delete operation for two strings? #583.

Summary of Real Questions

Frequency statistics from my interviews: Two Sum, Reverse Linked List, LRU Cache are tested almost every time; Valid Parentheses, Maximum Subarray, 3Sum appear 70%+; Edit Distance is marked hard but has a high pass rate — ByteDance and Alibaba love it. I recommend solving from 1 to 15 in order, writing each 2-3 times.

Tips and Advice

1. Quality over quantity — mastering 15 high-frequency problems beats skimming 100. Write each at least twice, second time from memory.

2. Study variants — interviewers rarely give the exact problem, but variants are based on originals. Like Reverse Linked List → Reverse Linked List II, Climbing Stairs → Complete Knapsack, Stock Trading series.

3. Explain approach before coding — algorithm interviews are conversations, not dictation. Confirm understanding with interviewer, state time/space complexity, then code.

4. Test after writing — run edge cases: empty input, single element, all same, all negative. Interviewers value verification habits.

5. Don't panic if stuck — state brute force first, then think about optimization. Brute force earns points, silence earns nothing. At ByteDance's third round, I wrote O(n²) 3Sum first, then the interviewer hinted toward O(nlogn).

FAQ

Q: How proficient should I be?

A: Easy problems in under 5 minutes, medium problems with optimal solution in under 15 minutes, hard problems can state approach. Big tech usually tests easy to medium; ByteDance occasionally tests hard.

Q: Are these 15 problems enough?

A: They cover 80% of test points, but I recommend 15-20 more: quick sort/merge sort, binary search, prefix sum, topological sort, union-find. 30-35 total is enough for most interviews.

Q: What language for problem solving?

A: Backend: Java/Go/C++. Frontend: JavaScript/TypeScript. Use the primary language for your target role.

Q: What if I can't solve the algorithm problem in an interview?

A: 1) State brute force; 2) Analyze brute force's problems; 3) Attempt optimization direction; 4) Pseudocode is acceptable. Showing your thought process is far better than silence.