How to Efficiently Solve LeetCode for Big Tech Interviews: 200 Problems by Category Is Enough

Background

Let me start with the conclusion: more LeetCode problems isn't better—200 problems by category is enough. During my autumn recruitment, I received 6 big tech offers, having solved about 230 LeetCode problems total, but each one I solved 3+ times. Many students solve 500+ problems and still can't pass interviews because they're solving randomly and mindlessly without building a system. Today, I'm sharing my problem-solving methodology to help you efficiently prepare for algorithm interviews.

I also took detours in my problem-solving journey. Initially, I solved problems by number order, starting from Problem 1. After 50 problems, I found I couldn't remember anything—because today's array problem had nothing to do with tomorrow's tree problem, and my brain couldn't make connections. Later, I switched to solving by category, concentrating on the same type of problems, and my efficiency doubled. I went from solving 3 problems per day to 8, and I remembered more.

Interview Process Review

ByteDance—Stuck on the First Algorithm Problem

ByteDance's first round: the interviewer gave me a binary tree level-order traversal variant. I had practiced level-order traversal before, but the variant added a "zigzag output" condition, and I was stumped. The interviewer hinted "First write the standard level-order traversal," which I did, but modifying it for zigzag took forever and still wasn't right. The interviewer finally said "Your fundamentals are okay, but your ability to adapt needs work." I passed, but with a mediocre evaluation.

Alibaba—The Power of Category-Based Practice

Alibaba's second round: the interviewer gave me a "Longest Increasing Subsequence" DP problem. I had practiced similar problems because I concentrated on 10+ subsequence problems in my DP category. I first wrote the O(n²) solution, then proactively said "This problem can also be optimized to O(nlogn) using binary search." The interviewer asked me to write it, and I finished in 10 minutes. The interviewer said "Your understanding of DP is very deep." This problem directly helped me land the Alibaba offer.

Meituan—The Power of Templates

Meituan's first round: the interviewer gave me a sliding window problem—"Find All Anagrams in a String." Not only had I practiced this problem, but I had also summarized a general sliding window template. I directly applied the template, finished writing in 5 minutes, then explained the applicable scenarios and solving patterns for sliding windows. The interviewer said "Your approach of summarizing templates is great—it shows you truly understand, not just memorize answers."

5 Problem-Solving Strategies Explained

Strategy 1: Solve by Category, Not Randomly

Core idea: Concentrate on the same type of problems to build muscle memory.

The biggest problem with random practice is "learn and forget." Today you solve a linked list problem, tomorrow a graph problem, the day after a DP problem—your brain can't build a knowledge network in time. Category-based practice is different—solve 10 binary search problems in a row, and you can write the template with your eyes closed.

My recommended 15 categories and priorities:

- Tier 1 (Must-do): Arrays, Linked Lists, Hash Tables, Two Pointers, Binary Search, DFS/BFS, Trees, DP
- Tier 2 (Important): Stack/Queue, Sliding Window, Greedy, Backtracking
- Tier 3 (Optional): Graph, Sorting, Design Problems

The 8 categories in Tier 1 are the most frequently tested in interviews and must be prioritized. Tier 2 covers common supplementary types. Tier 3 depends on your time and target companies—if interviewing at Google, you must do graph theory; for domestic big tech, it's lower priority.

Strategy 2: Solve 10-15 Problems per Category

Core idea: Cover the main problem types for each category—no more, no less.

How many problems per category? My experience says 10-15. Fewer won't cover the main types; more has diminishing returns. Specific allocation:

- Arrays: 12 (Two pointers 3, Prefix sum 3, Difference array 2, Matrix 2, Other 2)
- Linked Lists: 10 (Reverse 2, Fast-slow pointers 3, Merge 2, Delete 2, Other 1)
- Binary Search: 12 (Standard 3, Left boundary 3, Right boundary 3, Rotated array 3)
- DP: 15 (1D 5, 2D 3, Subsequence 3, Knapsack 2, Other 2)
- Trees: 12 (Traversal 3, Path 3, Construction 2, LCA 2, Other 2)
- Sliding Window: 10 (Fixed-length 3, Variable-length 4, String 3)
- Backtracking: 10 (Subsets 3, Permutations 3, Combinations 2, Other 2)

Strategy 3: Repeat 3 Times

Core idea: Spaced repetition—from "understanding" to "proficiency" to "instinct."

Many students solve problems only once and move on, only to completely forget them a week later. My approach is to solve each problem 3 times:

- 1st time: Understand the problem, read the solution, understand the approach. If you can't figure it out in 15 minutes, just read the solution—don't bang your head against the wall. Focus on understanding "why think this way" rather than "how to write the code."

- 2nd time: 1-2 days later, write it yourself without looking at the solution. This tests whether you truly understood it. If you still can't write it, it means you didn't understand it thoroughly the first time—go back and re-read the solution.

- 3rd time: 1 week later, complete it within 15 minutes. This trains speed and accuracy. In interviews, you typically have 20-30 minutes to write code, so you must be able to write a correct solution within 15 minutes.

After 3 rounds, a problem goes from "seen" to "can do" to "mastered." When you encounter a similar problem in an interview, the solution pops into your head without starting from scratch.

Strategy 4: Summarize Templates

Core idea: Abstract solving patterns into templates that you can directly apply in interviews.

I summarized general templates for each category and apply them directly in interviews. A few examples:

Sliding Window Template:

1. Initialize left=0, right=0, window=empty
2. while right < len: expand window, right++
3. while window meets shrink condition: update result, shrink window, left++
4. Repeat 2-3 until right reaches the end

Binary Search Template:

1. Initialize left=0, right=len-1
2. while left <= right: mid = left + (right-left)/2
3. if nums[mid] == target: return mid
4. elif nums[mid] < target: left = mid + 1
5. else: right = mid - 1
6. return -1

Backtracking Template:

1. Define backtrack(path, choices)
2. if end condition met: result.add(path); return
3. for choice in choices:
4. Make choice
5. backtrack(path, choices)
6. Undo choice

With these templates, you won't be stuck when encountering new problems in interviews—first identify the problem type, then apply the template, and finally fine-tune based on the problem's specifics.

Strategy 5: Mock Interview Practice

Core idea: Practice in a real interview environment to train mindset and communication.

Many students are smooth on LeetCode but freeze up in interviews from nerves. This is because LeetCode's environment is completely different from interviews—LeetCode allows repeated submissions, while interviews use whiteboard coding; LeetCode doesn't require talking, while interviews require explaining as you code.

My mock interview approach:

- 30-minute time limit: From seeing the problem to writing a complete solution, including time to discuss requirements with the interviewer.

- Whiteboard coding: No IDE—use paper/pen or a whiteboard. No autocomplete, no syntax checking—this is the real interview environment.

- Think out loud: State your approach—"I'll first consider a brute force approach with O(n²) time complexity, then try to optimize..."

- Practice with friends: Have them give you problems to solve. Their follow-up questions can reveal blind spots in your thinking.

I did 2 mock interviews per week for 1 month. The first time, I was so nervous my hands shook; by the fourth time, I was completely comfortable. In actual interviews, my mindset was as steady as during practice.

15 Categories Explained

Tier 1: Must-Do Categories

1. Arrays: Two pointers, prefix sum, difference array, matrix traversal. High-frequency: Two Sum, 3Sum, Merge Intervals.

2. Linked Lists: Reverse, fast-slow pointers, merge, delete. High-frequency: Reverse Linked List, Linked List Cycle, Merge Two Sorted Lists.

3. Hash Tables: Counting, grouping, deduplication. High-frequency: Two Sum, Group Anagrams, Longest Consecutive Sequence.

4. Two Pointers: Opposing pointers, fast-slow pointers, sliding window basics. High-frequency: Container With Most Water, 3Sum, Trapping Rain Water.

5. Binary Search: Standard binary search, left boundary, right boundary, rotated array. High-frequency: Search in Rotated Sorted Array, Find Peak Element, Find First and Last Position.

6. DFS/BFS: Grid traversal, graph traversal, shortest path. High-frequency: Number of Islands, Word Search, Rotting Oranges.

7. Trees: Traversal, paths, construction, LCA. High-frequency: Binary Tree Level Order Traversal, Maximum Depth, Lowest Common Ancestor.

8. Dynamic Programming: 1D DP, 2D DP, subsequence, knapsack. High-frequency: Longest Increasing Subsequence, Edit Distance, Coin Change, House Robber.

Tier 2: Important Categories

9. Stack/Queue: Monotonic stack, valid parentheses, expression evaluation. High-frequency: Valid Parentheses, Daily Temperatures, Min Stack.

10. Sliding Window: Fixed-length window, variable-length window, string matching. High-frequency: Longest Substring Without Repeating Characters, Find All Anagrams, Minimum Window Substring.

11. Greedy: Interval problems, jump game, allocation. High-frequency: Jump Game, Partition Labels, Candy.

12. Backtracking: Subsets, permutations, combinations, N-Queens. High-frequency: Permutations, Subsets, Combination Sum, N-Queens.

Tier 3: Optional Categories

13. Graph: Topological sort, shortest path, union-find. High-frequency: Course Schedule, Clone Graph, Redundant Connection.

14. Sorting: Quick sort, merge sort, heap sort. High-frequency: Sort Array, Top K Frequent Elements, Kth Largest Element.

15. Design: LRU cache, LFU cache, data stream median. High-frequency: LRU Cache, Min Stack, Implement Trie.

Real Interview Questions

ByteDance Questions

1. Binary Tree Zigzag Level Order Traversal
2. Longest Increasing Subsequence
3. Trapping Rain Water
4. Merge K Sorted Lists
5. Decode String

Alibaba Questions

1. Edit Distance
2. Minimum Window Substring
3. Binary Tree Maximum Path Sum
4. Median of Two Sorted Arrays
5. Word Break

Meituan Questions

1. Find All Anagrams in a String
2. Number of Islands
3. Jump Game II
4. Course Schedule
5. LRU Cache

Key Takeaways

First, don't chase problem count. 200 problems solved 3 times > 600 problems solved once. Quality always beats quantity. Interviewers don't care how many problems you've solved—they care whether you can write a correct solution in 30 minutes.

Second, Easy before Medium; Hard as needed. Easy problems build confidence and foundations; Medium problems are the mainstay of interviews; Hard problems are only needed for Google/Facebook interviews. 90% of algorithm questions at domestic big tech are Medium difficulty.

Third, explain your approach before coding. In interviews, spend 2-3 minutes confirming your approach with the interviewer before writing code. If the approach is right, the code comes quickly; if it's wrong, no amount of writing helps.

Fourth, focus on time and space complexity. Interviewers will always ask about complexity, so analyze it for every problem. The gap between optimal and brute force solutions is often what interviewers use to differentiate candidates.

Fifth, maintain your problem-solving rhythm. Solve at least 2-3 problems daily to keep your skills sharp. A one-week break makes you noticeably rusty. During my autumn recruitment, I solved 3 problems every day without fail for 3 months.

FAQ

Q1: How many LeetCode problems are enough?

About 200 problems by category, each solved 3 times, is basically enough. If targeting core positions at big tech, you can go up to 250-300. The key is category coverage and repetition count, not total numbers.

Q2: What if I'm burnt out from problem-solving?

Lower the difficulty. If you can't do Medium, start with Easy. If you can't figure out a problem in 15 minutes, just read the solution. Don't bang your head against the wall—it's extremely inefficient and easily destroys confidence. Reading solutions isn't shameful—understanding them after reading is what matters.

Q3: What if I encounter a completely unfamiliar problem in an interview?

Break it down with templates. First identify the problem type (Array? Linked List? Tree? DP?), then apply the corresponding template. Even if you can't fully solve it, at least provide a brute force approach and then try to optimize. Interviewers value your thinking process more than the final answer.

Q4: Python or Java for interviews?

Either works—choose what you're most proficient in. Python is faster to write, but interviewers might ask about underlying implementations; Java is more formal but has more code. I used Java because I work with it daily and it's more natural for me.

Q5: How to balance LeetCode practice with rote question preparation?

I recommend 2 hours of LeetCode + 2 hours of rote questions daily. Morning for LeetCode (clear mind), afternoon for rote questions (requires deep thinking). Weekends for mock interviews and reviews. Don't completely separate the two—many rote question topics (like HashMap, concurrency) overlap with algorithm problems.