FAANG LeetCode Interview Prep – An In-Depth Guide

Hey there 👋! If you’re here, you're likely prepping for coding interviews at big tech companies—those prestigious FAANG names: Facebook (Meta), Amazon, Apple, Netflix, and Google. These places are known for their tough interviews, and one of the best ways to train is by solving LeetCode problems. Let’s walk through the process in detail, understand examples, explore strategies, and get you fully ready for what’s ahead.

1. Why FAANG Questions Matter

FAANG companies brag strong innovation and built the modern tech landscape. That means their screening process has to sift through tons of engineers and pick the very best. The best way to measure excellence? Real-time problem solving. That’s where LeetCode shines—it provides a playground for mastering algorithms and data structures, essential for FAANG-style interviews.

Don’t be fooled—these questions go beyond checking if you know code syntax. They dig deep into how you think, how you model problems, how you reason under constraints, and how you cleanly explain what you're doing. And trust me, it’s every bit as important.

2. Popular FAANG-Style LeetCode Problems & How to Approach Them

Let’s dive into some commonly asked problems from each FAANG company, break them down thoroughly, and include practical strategies to tackle each one. We’ll talk about trees, dynamic programming, hashing, recursion, and more.

2.1 Facebook (Meta): Maximum Depth of a Binary Tree

Problem: Given a binary tree, find its maximum depth (i.e., the longest path from the root node to a leaf).

⚙️ Why it shows up: It tests your understanding of tree data structures and recursion. You need to navigate all paths and find the longest one—an essential skill when working with any nested structure, not just trees.

Approach & Explanation

1. Definition: The depth of an empty tree is 0. For a non-empty node, the depth is 1 + max(depth(left subtree), depth(right subtree)).
2. Use recursion or DFS: Write a function that goes down each subtree, computes its depth, and returns the maximum.
3. Example: A tree with root->left->right has depth 3. If any side is deeper, you always pick the max path.

Example Code (Python)

def maxDepth(root):
    if root is None:
        return 0
    left_depth = maxDepth(root.left)
    right_depth = maxDepth(root.right)
    return 1 + max(left_depth, right_depth)
  

Key Points

• It’s okay to use recursion or iterative DFS placeholders.
• Always check None before exploring children.
• Time Complexity: O(N), where N is the number of nodes.
• Space Complexity: O(H), where H is the tree height, due to the call stack.

2.2 Amazon: Longest Increasing Subsequence

Problem: Given an unsorted array nums, find the length of its longest strictly-increasing subsequence.

🧠 Why it shows up: Dynamic programming and optimization are key here. You must think in terms of "build this from shorter pieces"— classic DP mindset.

Approach & Explanation

1. DP definition: Let dp[i] be the length of the longest increasing subsequence ending at index i.
2. Recurrence: dp[i] = 1 + max(dp[j]) for all j < i where nums[j] < nums[i]; if none, dp[i] = 1.
3. Answer: The overall longest is max(dp[0..n-1]).

Example Code (Python)

def lengthOfLIS(nums):
    if not nums:
        return 0
    dp = [1] * len(nums)
    for i in range(len(nums)):
        for j in range(i):
            if nums[j] < nums[i]:
                dp[i] = max(dp[i], dp[j] + 1)
    return max(dp)
  

Optimizations & Considerations

• This is O(N²) in time. It passes arrays up to ~10,000 length in optimized C++, but might TLE in Python for huge input.
• There’s a O(N log N) method using patience sorting with binary search—interview gold material.
• Understanding both solutions is valuable. If time permits, present simple DP, then mention improved method.

2.3 Apple: Maximum Path Sum in a Binary Tree

Problem: Given a non-empty binary tree, find the maximum path sum. A path can start and end at any node.

🍏 Why it’s asked: This problem tests advanced recursion and combining results from children carefully. It’s powerful because you must return something up, but also track a global best.

Key Concepts

• Path could be from left child → parent → right child, not just from root.
• Negatives are allowed, so you might avoid them by taking max with 0.
• Maintain a global variable while doing DFS.

Example Code (Python)

def maxPathSum(root):
    max_sum = float('-inf')
    def dfs(node):
        nonlocal max_sum
        if not node:
            return 0
        left_gain  = max(dfs(node.left), 0)
        right_gain = max(dfs(node.right), 0)
        current_sum = node.val + left_gain + right_gain
        max_sum = max(max_sum, current_sum)
        return node.val + max(left_gain, right_gain)
    dfs(root)
    return max_sum
  

Walkthrough & Explanation

• Each recursion returns the max gain to use in parent path.
• At every node, you check combining left + node + right (local max), update global, and return upward the best "branch" sum.
• Time Complexity O(N), Space Complexity O(H).

2.4 Netflix: Word Break

Problem: Given a string s and a dictionary dict, check if s can be segmented into space-separated words in dict.

🎬 Why it appears: This blends string processing with DP and hashing. You’ll practice substring checks, memoization, and efficiently scanning.

Approach & Explanation

Use DP with a boolean array dp of size len(s)+1, where dp[i] means "prefix ending at i−1 is breakable."

• dp[0] = True (an empty string is trivially segmented).
• For every i, check all j < i and see if s[j:i] is in dict && dp[j] is true.
• If true, mark dp[i] = True and break inner loop.
• Return dp[len(s)] at the end.

Example Code (Python)

def wordBreak(s, wordDict):
    word_set = set(wordDict)
    dp = [False] * (len(s) + 1)
    dp[0] = True
    for i in range(1, len(s) + 1):
        for j in range(i):
            if dp[j] and s[j:i] in word_set:
                dp[i] = True
                break
    return dp[len(s)]
  

Tips

• Use a hash set for fast O(1) lookups.
• This is O(n²) due to nested loops, acceptable for s up to ~2,000.
• You can optimize using BFS/Trie/DP with greedy pruning—but the DP method is easy to explain.

2.5 Google: Two Sum

Problem: Given array nums and an integer target, find two indices that sum up to target.

🌐 Why it's common: A short, hash map problem. You demonstrate speed, simplicity, and edge-case thinking quickly.

Approach & Explanation

• Use a hash map to store number → index while iterating. For each element num, check if target − num is in the map.
• If yes, you’ve found the pair. Return those two indices.
• This is a single-pass solution—elegant and efficient.

Example Code (Python)

def twoSum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
  

Insights

• Time Complexity: O(N).
• Space Complexity: O(N) for the hash map.
• Always clarify: "Can I assume exactly one solution?" If yes, great. If not, say you’ll store all valid pairs or return [].

3. General Interview Tips & Best Practices

Getting a question right is part of it—but how you handle communication is just as vital. Here's the full process.

3.1 Think Out Loud

Verbalize everything. At each step, share your thought process:

• “I see this is a tree problem, so I'll use DFS.”
• “I need to check all substrings—DP sounds appropriate.”
• “Overflows are possible in Java, so I'll pick the safe middle calculation.”

3.2 Start Simple, Then Optimize

It's perfectly fine to begin with a working solution, then mention optimizations. Interviewers appreciate seeing how you improve solutions.

3.3 Edge Cases & Input Types

• Empty inputs: [], "", null. Always mention them.
• Duplicates, negative numbers, negative weights.
• Signed integer overflow in Java/C++ when using (left + right) / 2.

3.4 Test While You Code

Illustrate your solution with a simple test:

• Two Sum: [2,7,11,15], target=9 → returns [0,1]
• Word Break: "leetcode" with dict ["leet","code"] → returns True

4. Programming Routine and Practice Plan

It’s not about banging out hundreds of random problems. Here’s a smart structure:

• Warm-up: 15 minutes of easy array-based or hash-map problems to get into focus mode.
• Main session: 1 problem timed to simulate an interview. Write code, think out loud, defend your approach.
• Review session: Another 15 minutes—analyze your code. Could you optimize? Did you forget edge cases?
• Knowledge session: 15–20 minutes reviewing a data structure or algorithm pattern—like trees, dynamic programming, O(N log N) LIS.

Practice Frequency

• Daily: 2–3 problems.
• Weekly: 1 mock interview with another person or one recorded session.
• Monthly: 1–2 full mock interviews under time constraints.

5. Beyond LeetCode

While LeetCode is your basecamp, FAANG interviews often include system design, scalability, tradeoffs, and sometimes real code review. It helps to also:

• Read about how distributed systems are designed.
• Practice white-board system design for a 15-minute session.
• Mock interview full-stack questions—frontend, backend, architecture.

6. Mindset Tips

• 💪 Don’t be intimidated. Anyone can learn these patterns with daily practice.
• 📚 Always ask questions. Confirm constraints, ask for clarifications when necessary.
• 🛠️ Mistakes are okay. If you realize a bug—acknowledge it, correct it, and explain why.
• 🎯 Quality over quantity. A few well-understood patterns are better than many forgotten ones.

7. Final Words – Your Path to FAANG

The road to a FAANG-level offer is long but rewarding. Each time you solve a problem, you’re equipping yourself with lifelong problem-solving tools. Here’s a quick recap:

• ✅ Leverage LeetCode to master classic patterns.
• ✅ Understand the “why”—don’t just memorize.
• ✅ Talk out loud, test, optimize, and communicate clearly.
• ✅ Balance coding practice with system design prepping.
• ✅ Keep your hands sharp—consistency beats all.

Remember: FAANG is a label, but your skill is what really matters. Show up prepared, solve problems with clarity, and shine as both an engineer and a thinker. You’ve got this!

🙌 Wishing you focus, confidence, and success on your FAANG journey. Happy coding and keep pushing your limits!