Ultimate Leetcode Coding Interview Cheat Sheet | FAANG Prep for Grads

Coding interviews are challenging, but the right preparation makes all the difference. This cheat sheet provides a structured approach to leetcode prep, covering question patterns for all the relevant DSA types and expert strategies to help you succeed. A good understanding of Big O notation is essential, knowledge of time and space complexity will help you write optimized, efficient code that meets interview standards. With the right focus and practice, you can approach your technical interviews with confidence and secure your dream role.

Table of Contents

• Why Use a Cheat Sheet for Coding Interviews
• Core Concepts Every Aspiring FAANG Engineer Should Know
• Must-Know Leetcode Patterns and Problems
• How to Build Your Own Cheat Sheet
• Pro Tips to Maximize Leetcode Success
• FAQ
• Conclusion

Why Use a Cheat Sheet for Coding Interviews

Preparing for coding interviews can feel overwhelming because of the breadth and depth of topics to cover, a shortage of time or simply not knowing where to start. A cheat sheet simplifies the learning process by organizing key concepts, patterns, and strategies in a handy and accessible format. It helps you stay organized, prioritize frequently tested topics, and save time revisiting core material.

Instead of endlessly browsing Reddit, other forums or textbooks, you can rely on a well-crafted cheat sheet to focus your efforts where it matters most. For instance, problem-solving patterns like sliding windows or two-pointers are common in FAANG interviews, and having a quick reference can make a world of difference.

• Stay Organized: Keep track of key algorithms, data structures, and problem-solving strategies
• Focus on What Matters: Identify high-priority topics that are frequently tested in interviews.
• Save Time: Quickly review critical information before interviews.

Core Concepts Every Aspiring FAANG Engineer Should Know

Having a solid base when it comes to knowledge is essential for FAANG interviews. Every company will test fundemental computer science principles one way or another.

Big-O Complexities

Understanding Big-O complexity is arguably the most important one on this list, as you’ll need to evaluate the efficiency of your solutions. Always aim for the most efficient algorithms and be ready to discuss their trade-offs. Sorting algorithms like QuickSort exemplify trade-offs, with average-case complexity at Ο(n log n) and worst-case at Ο(n²). Hash tables offer efficiency, with insertion and lookup operations running in Ο(1).

Core Data Structures

Additionally, familiarity with basic and advanced data structures is a must. Arrays and strings form the basis for problems involving rotations or substrings, while linked lists are central to reversal or merging tasks. Trees and graphs, such as binary trees and graph traversal algorithms (DFS/BFS), often appear alongside hash tables and heaps for tasks like implementing an LRU Cache.

• Arrays and Strings: For problems like rotations or substrings.
• Linked Lists: For reversal, merging, and detecting cycles.
• Trees and Graphs: Binary trees, binary search trees, and graph traversal algorithms (BFS/DFS).
• Hash Tables and Heaps: For solving problems like LRU Cache or priority queues.

Recursion and Dynamic Programming

Recursion and dynamic programming (DP) further distinguish strong candidates from weak ones. Though not relevant for all companies since Meta, for example, doesn’t ask dynamic programming questions. From solving Fibonacci sequences through recursion to tackling knapsack problems with dynamic programming, these techniques often offer the solutions to otherwise seemingly difficult challenges.

• Recursion: Fibonacci sequence, factorial, and backtracking.
• Dynamic Programming (DP): Knapsack problem, longest common subsequence, and matrix chain multiplication.

Must-Know Leetcode Patterns and Problems

Interviewers will test your ability to identify and apply specific problem-solving patterns. Let’s delve into some of the most essential patterns and their applications.

The sliding window technique is a versatile approach, especially for problems involving contiguous subarrays or substrings. For example, to find the maximum sum of a subarray of size k, you can maintain a sliding window, updating the sum as you progress. This efficient approach reduces complexity to Ο(n).

The two-pointer technique is another favorite, particularly for sorted arrays. Consider the problem of finding all pairs in a sorted array that sum to a target value. By using pointers at both ends of the array and adjusting them based on their summed values, you achieve a linear time solution.

Divide and conquer techniques break problems into smaller subproblems, solve them independently, and combine results. Implementing merge sort is a classic example, with its logarithmic complexity (Ο(n log n)). Backtracking, on the other hand, is invaluable for generating permutations, combinations, and solving Sudoku puzzles.

Familiarity with specific problems, such as Two Sum (efficiently solved with hash maps), Longest Substring Without Repeating Characters (using the sliding window), and Merge Intervals, provides a strong foundation for tackling the unexpected.

Some coding question patterns for all relevant DSA types to practice:

1. Arrays and Strings

• a. Two Pointers

  • Example Problem: Find all pairs in a sorted array that sum up to a target value.
  • Approach: Use two pointers starting at both ends of the array. Move the pointers inward based on the sum compared to the target.
  • Time Complexity: O(n)

• b. Sliding Window

  • Example Problem: Find the longest substring without repeating characters.
  • Approach: Maintain a sliding window to track the current substring without duplicates. Expand the window by moving the right pointer and contract by moving the left pointer when a duplicate is found.
  • Time Complexity: O(n)

• c. Binary Search

  • Example Problem: Search for a target value in a rotated sorted array.
  • Approach: Apply binary search by determining which part of the array is sorted and adjusting the search range accordingly.
  • Time Complexity: O(log n)

• d. Prefix Sum

  • Example Problem: Find the subarray with a given sum in an array.
  • Approach: Compute cumulative sums and use a hashmap to store the frequencies of these sums to identify subarrays with the target sum efficiently.
  • Time Complexity: O(n)

2. Trees

• a. Depth-First Search (DFS)

  • Example Problem: Perform an inorder traversal of a binary tree.
  • Approach: Recursively visit the left subtree, process the current node, and then visit the right subtree.
  • Time Complexity: O(n)

• b. Breadth-First Search (BFS)

  • Example Problem: Perform a level-order traversal of a binary tree.
  • Approach: Use a queue to traverse the tree level by level, enqueuing child nodes as you go.
  • Time Complexity: O(n)

• c. Binary Search Tree (BST) Operations

  • Example Problem: Insert a value into a BST.
  • Approach: Recursively traverse the tree to find the correct spot for the new value, ensuring BST properties are maintained.
  • Time Complexity: O(log n) on average; O(n) in the worst case.

• d. Tree Construction

  • Example Problem: Construct a binary tree from its preorder and inorder traversal sequences.
  • Approach: Use the preorder sequence to identify root nodes and the inorder sequence to determine the structure of subtrees.
  • Time Complexity: O(n)

3. Hashtables

a. Frequency Counting Example Problem: Determine if two strings are anagrams. Approach: Use a hashtable to count the frequency of each character in both strings and compare the results. Time Complexity: O(n) b. Two Sum Pattern Example Problem: Find two numbers in an array that add up to a specific target. Approach: Use a hashtable to store the difference between the target and each element as you iterate through the array. Time Complexity: O(n) c. Anagram Detection Example Problem: Group a list of strings into anagram groups. Approach: Sort each string and use the sorted version as a key in a hashtable to group anagrams together. Time Complexity: O(n * k log k), where n is the number of strings and k is the maximum length of a string d. Caching Example Problem: Implement an LRU (Least Recently Used) cache. Approach: Use a combination of a hashtable and a doubly linked list to store cache entries and track their usage order. Time Complexity: O(1) for both get and put operations

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4. Graphs

• a. Depth-First Search (DFS)

  • Example Problem: Find all connected components in an undirected graph.
  • Approach: Use DFS to explore each component, marking nodes as visited to avoid revisiting.
  • Time Complexity: O(V + E), where V is the number of vertices and E is the number of edges.

• b. Breadth-First Search (BFS)

  • Example Problem: Find the shortest path in an unweighted graph.
  • Approach: Use BFS to explore the graph level by level, tracking the distance from the start node to each node.
  • Time Complexity: O(V + E).

• c. Topological Sort

  • Example Problem: Determine a valid order of courses to take given their prerequisites.
  • Approach: Use DFS to perform a topological sort on the directed graph representing course dependencies.
  • Time Complexity: O(V + E).

• d. Union Find

  • Example Problem: Detect cycles in an undirected graph.
  • Approach: Use the Union-Find data structure to keep track of connected components and identify cycles during edge addition.
  • Time Complexity: O(α(n)), where α is the inverse Ackermann function.

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5. Stacks

• a. Parentheses Matching

  • Example Problem: Validate whether a string contains balanced parentheses.
  • Approach: Use a stack to push opening brackets and pop when encountering a closing bracket. Ensure the stack is empty at the end.
  • Time Complexity: O(n).

• b. Monotonic Stack

  • Example Problem: Find the next greater element for each element in an array.
  • Approach: Use a stack to maintain a decreasing sequence and update the next greater element for elements when popped.
  • Time Complexity: O(n).

• c. Expression Evaluation

  • Example Problem: Evaluate a mathematical expression containing operators and parentheses.
  • Approach: Use a stack to process numbers and operators, respecting operator precedence.
  • Time Complexity: O(n).

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6. Queues

• a. BFS Implementation

  • Example Problem: Find the shortest path in an unweighted graph.
  • Approach: Use a queue to explore nodes level by level, tracking distances from the source node.
  • Time Complexity: O(V + E).

• b. Task Scheduling

  • Example Problem: Find the minimum time required to complete tasks with cooldown constraints.
  • Approach: Use a priority queue (heap) or queue to track tasks based on availability and execution order.
  • Time Complexity: O(n log k).

• c. Sliding Window Problems

  • Example Problem: Find the maximum element in each sliding window of size k in an array.
  • Approach: Use a deque to maintain the indices of useful elements in each window.
  • Time Complexity: O(n).

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7. Heaps (Priority Queue)

• a. Top-K Elements Pattern

  • Example Problem: Find the k largest elements in an array.
  • Approach: Use a min-heap of size k, pushing elements and removing the smallest.
  • Time Complexity: O(n log k).

• b. Merge K Sorted Lists

  • Example Problem: Merge k sorted linked lists into a single sorted list.
  • Approach: Use a min-heap to always extract the smallest element from the lists.
  • Time Complexity: O(n log k).

• c. Two Heaps Pattern

  • Example Problem: Find the median of a stream of numbers.
  • Approach: Use two heaps (max-heap for smaller numbers, min-heap for larger numbers) to maintain balance.
  • Time Complexity: O(log n) per insertion.

• d. Sliding Window Median

  • Example Problem: Find the median of each sliding window of size k in an array.
  • Approach: Use two heaps to maintain the window’s median efficiently.
  • Time Complexity: O(n log k).

How to Build Your Own Cheat Sheet

A personalized cheat sheet can be a game-changer for your interview preparation. To create one, start with tools like Google Sheets, Excel or Notion to structure your notes. Divide your sheet into sections covering core concepts, patterns, and practice problems, linking to specific Leetcode examples for easy access. For instance, use a table format to categorize topics, list example problems, and provide direct links to solutions. This method ensures that you can quickly reference critical material during your preparation.

Include sections for Big-O notation, recursion techniques, and DP examples. Add detailed notes for sliding window, two-pointer, and backtracking strategies, with pseudocode or code snippets. Such a resource not only saves time but also reinforces learning as you curate and organize your notes.

Tools to Use

• Google Sheets or Excel: Organize algorithms and patterns into rows and columns.
• Notion or Evernote: Create a structured, searchable repository of notes.
• Markdown files: Use GitHub to store and version-control your notes.

Structure of a Cheat Sheet

• Core Concepts: Big-O notation, recursion, and dynamic programming.
• Patterns and Examples: Brief explanations with pseudo-code or solutions.
• Practice Problems: Links to Leetcode problems categorized by difficulty and topic.

Pro Tips to Maximize Leetcode Success

Maximizing your Leetcode preparation involves adopting effective strategies. Timeboxing your problem-solving is a crucial habit; set aside 15 minutes for easy problems, 30 minutes for medium ones, and up to an hour for hard problems. This disciplined approach prevents over-investment in a single problem while increasing your ability to identify when to seek hints or solutions.

Simulating interviews using platforms like Pramp or Leetcode’s Mock Interview feature helps you practice your communication skills and builds confidence. Meanwhile, using analytics tools within Leetcode Premium can help pinpoint weak areas. By targeting these areas, you can divide your study time more effectively.

Joining a study group or finding a coding partner also fosters collaboration, offering new perspectives and keeping you accountable. These interactions often lead to insights that would be difficult to achieve in isolation.

1. Timeboxing

Set strict time limits for solving problems:

• Easy Problems: 15 minutes
• Medium Problems: 30 minutes
• Hard Problems: 60 minutes

2. Mock Interviews

Simulate real interviews using platforms like Pramp or Leetcode’s Mock Interview feature. This builds confidence and improves your ability to communicate solutions.

3. Focus on Weak Areas

Use analytics tools in Leetcode Premium to identify patterns where you struggle. Spend extra time mastering these topics.

4. Join a Study Group

Collaborating with peers can provide new perspectives and keep you accountable. There are a lot of study groups available on the Leetcode subreddit that you can join.

What to Do When You’re Stuck

This cheat sheet provides a solid foundation, but it’s just the tip of the iceberg. Mastering Leetcode and cracking FAANG interviews require consistent effort, deeper dives into hard concepts, and lots of practice.

If you ever feel stuck or unsure about where to focus next, platforms like Phantom Code just the bit of guidance needed to help you navigate the journey to landing a job. Sometimes, having an extra hand to guide you can make all the difference in transforming frustration into confidence.

Frequently Asked Questions

1. What is a Leetcode cheat sheet, and why is it important?

A cheat sheet is a concise compilation of essential concepts and problem-solving strategies designed to streamline preparation and focus on high-priority topics.

2. Which are the most critical patterns to include in a cheat sheet?

Patterns such as sliding windows, two-pointer techniques, divide-and-conquer, and backtracking are indispensable for FAANG interviews.

3. How much time should I spend preparing for FAANG interviews?

Experts recommend dedicating 2-3 hours daily for 2-3 months to achieve comprehensive preparation.

4. Do I need Leetcode Premium to succeed?

While not essential, Leetcode Premium offers categorized problems and company-specific insights, saving time and providing a targeted approach. If you do not want to pay for Leetcode Premium you can also find a full list of all company-specific problems right here on our website.

5. How can I simulate FAANG interviews effectively?

Using platforms like Pramp or Leetcode’s Mock Interview feature allows you to practice live coding and receive constructive feedback.

Conclusion

FAANG interviews are undoubtedly challenging, but with a strategic approach and consistent effort, they are very much doable. Use this cheat sheet as your roadmap, training your skills in essential areas and leveraging tools and platforms to maximize your preparation.