What Leetcode Questions Are Most Commonly Asked During Interviews? We Asked Our Users

Coding interviews at FAANG (Facebook, Amazon, Apple, Netflix, Google) companies are known for their hard difficulty and low pass rates. They focus on testing candidates' problem-solving skills using data structures and algorithms. The most common way these skills are tested is with Leetcode. Leetcode problems are a popular method that aspiring software engineers have to practice and refine to be able to pass such interviews. Developing the skills needed to be good at these problems can take months. We asked some of the top Leetcode Wizard users which questions they've seen the most during interviews and created this list.

Understanding LeetCode and FAANG Interviews

Leetcode is closely related to competitive programming. A subtype of programming where you are rated on the speed and efficiency with which you solve a certain coding problem. These coding problems are designed to test your understanding of algorithms and data structures. FAANG companies emphasize these problems in interviews because they assess a candidate's ability to think logically, optimize solutions, and write clean, efficient code. A solid grasp of data structures and algorithms is essential for success in these interviews. Most companies refresh their questions on a regular basis and use questions that can't be found on the internet. That's why it's important to have a good understanding of the underlying algorithms and be able to recognize patterns. Let's take a look at some of the most common types of problems you will encounter in these interviews.

Common Problem Types

Array and Hashing

Arrays are fundamental data structures that store elements in contiguous memory locations. Hashing involves mapping data to specific locations using a hash function, which allows for efficient data retrieval. Hash maps, which are implemented using arrays, are particularly useful for problems involving frequent lookups.

Common Problems

• Two Sum (Easy) - Find two numbers in an array that add up to a specific target.
• Longest Consecutive Sequence (Medium) - Return the length of the longest consecutive elements sequence.

Two Pointers

The Two Pointers technique involves using two pointers to iterate through an array or string, typically from opposite ends or from both ends towards the middle. This approach is often used for problems involving sorting, merging, or detecting patterns.

Common Problems

• Reverse String (Easy) - Reverse a string using the two-pointer approach.
• Two Sum II - Input Array Is Sorted (Medium) - Find two numbers in a sorted array that add up to a target value.
• Trapping Rain Water (Hard) - Calculate the amount of rainwater that can be trapped between non-negative integer heights representing an elevation map.

Sliding Window

Sliding Window is a technique used for solving problems that involve a subset or subarray of a given size, or where you need to find a specific condition in a dynamic window. The window "slides" over the data structure to check for conditions efficiently.

Common Problems

• Best Time to Buy and Sell Stock (Easy) - Maximize your profit by choosing a day to buy one stock and a different day in the future to sell that stock.
• Longest Repeating Character Replacement (Medium) - Find the longest substring with up to k character replacements.
• Minimum Window Substring (Hard) - Find the smallest substring containing all characters of a target string.

Stack

A stack is a linear data structure that follows the Last-In-First-Out (LIFO) principle. It's particularly useful for problems involving nested structures, undo mechanisms, or balancing symbols.

Common Problems

• Valid Parentheses (Easy) - Check if a string of parentheses is correctly balanced.
• Daily Temperatures (Medium) - Find the number of days until a warmer temperature for each day.
• Largest Rectangle in Histogram (Hard) - Find the largest rectangular area in a histogram.

Binary Search

Binary Search is an efficient algorithm for finding an element in a sorted array by repeatedly dividing the search interval in half. This approach reduces the time complexity to O(log n).

Common Problems

• Binary Search (Easy) - Find the target value's index in a sorted array using binary search.
• Search in Rotated Sorted Array (Medium) - Find the target's index in a rotated sorted array.
• Median of Two Sorted Arrays (Hard) - Find the median of two sorted arrays combined.

Linked List

A Linked List is a data structure where elements are stored in nodes, with each node pointing to the next. This structure allows for efficient insertion and deletion of elements, especially in scenarios where dynamic memory allocation is needed.

Common Problems

• Reverse Linked List (Easy) - Reverse a singly linked list.
• Add Two Numbers (Medium) - Add two numbers represented by linked lists.
• Merge k Sorted Lists (Hard) - Merge k sorted linked lists into one sorted list.

Trees

Trees are hierarchical data structures with a root node and child nodes, where each node may have zero or more children. Binary Trees, where each node has at most two children, are particularly common and are used in various algorithms, including searching and sorting.

Common Problems

• Subtree of Another Tree (Easy) - Check if one tree is a subtree of another tree.
• Validate Binary Search Tree (Medium) - Determine if a binary tree is a valid binary search tree.
• Binary Tree Maximum Path Sum (Hard) - Find the maximum path sum in a binary tree.

Heap / Priority Queue

A Heap is a special tree-based data structure that satisfies the heap property. A min-heap always has the smallest element at the root, and a max-heap has the largest. Heaps are often used to implement priority queues, where the element with the highest priority is served first.

Common Problems

• Last Stone Weight (Easy) - Simulate repeatedly smashing stones together until one or none are left.
• Kth Largest Element in an Array (Medium) - Find the k-th largest element in an unsorted array.
• Find Median from Data Stream (Hard) - Continuously find the median of a stream of numbers.

Backtracking

Backtracking is a recursive algorithm used to solve problems by exploring all potential solutions and discarding those that fail to satisfy the problem constraints. It's often applied in problems involving permutations, combinations, and searching for a solution in a decision tree.

Common Problems

• Subsets (Medium) - Generate all possible subsets of a given set of numbers.
• Word Search (Medium) - Determine if a word exists in a grid by tracing adjacent cells.
• N-Queens (Hard) - Find all ways to place n queens on an n x n chessboard such that no two queens threaten each other.

Tries

A Trie (pronounced "try") is a specialized tree structure used for storing a dynamic set of strings, where keys are usually strings. Tries are particularly useful for solving problems involving prefix matching or autocomplete features.

Common Problems

• Implement Trie (Prefix Tree) (Medium) - Build a trie data structure to efficiently store and search prefixes of words.
• Word Search II (Hard) - Find all words from a list that exist in a grid by tracing adjacent cells.

Graphs

Graphs are data structures consisting of nodes (vertices) and edges that connect them. Graph algorithms are used for traversing or searching through these connections, making them essential for solving problems involving networks, relationships, or paths.

Common Problems

• Number of Islands (Medium) - Count the number of distinct islands in a 2D grid of land and water.
• Surrounded Regions (Medium) - Capture all regions in a grid that are surrounded by borders.
• Word Ladder (Hard) - Find the shortest transformation sequence from a start word to an end word by changing one letter at a time.

Dynamic Programming

Dynamic Programming (DP) is an optimization technique used to solve problems by breaking them down into simpler subproblems and storing the results of these subproblems to avoid redundant computations. DP is particularly effective for problems involving overlapping subproblems and optimal substructure.

One-Dimensional vs Two-Dimensional

The difference between 1D and 2D dynamic programming lies primarily in how the state and transitions are represented and stored.

1D Dynamic Programming

• State Representation: The state is usually represented by a single index or parameter. For example, in the "Climbing Stairs" problem, the state can be represented by dp[i], where i is the number of steps.
• Storage: The DP table is a one-dimensional array (or list) where each element stores the result for a particular state.
• Usage: 1D DP is used when the problem can be broken down into stages that depend on a single variable or when the problem can be solved by keeping track of a linear sequence of states.

2D Dynamic Programming

• State Representation: The state is represented by two indices or parameters. For example, in the "Unique Paths" problem, the state can be represented by dp[i][j], where i and j are the row and column indices of the grid.
• Storage: The DP table is a two-dimensional array (or matrix) where each cell stores the result for a particular pair of states.
• Usage: 2D DP is used when the problem involves two varying dimensions or when a decision depends on two factors simultaneously.

Common Problems (1D)

• Climbing Stairs (Easy) - Calculate the number of ways to reach the top of a staircase with n steps.
• Coin Change (Medium) - Find the minimum number of coins needed to make a given amount.
• Word Break (Medium) - Determine if a string can be segmented into valid dictionary words.

Common Problems (2D)

• Unique Paths (Medium) - Calculate the number of unique paths from the top-left to the bottom-right of a grid.
• Target Sum (Medium) - Find the number of ways to assign + or - to numbers to reach a target sum.
• Longest Increasing Path in a Matrix (Hard) - Find the longest path in a matrix where each step increases in value.

Greedy

The Greedy algorithm is a problem-solving technique that builds up a solution piece by piece, always choosing the next piece that offers the most immediate benefit. This approach is often used when the local optimal choice leads to a globally optimal solution.

Common Problems

• Jump Game (Medium) - Determine if you can reach the last index of an array by jumping forward based on the values at each index.
• Jump Game II (Medium) - Find the minimum number of jumps required to reach the last index of an array.
• Valid Parenthesis String (Medium) - Check if a string with (, ), and * (wildcard) can be a valid parentheses sequence.

Interval

Interval problems involve ranges of values, and solving them often requires sorting or merging intervals. These problems are common in scheduling, where you need to manage or optimize overlapping intervals.

Common Problems

• Insert Interval (Medium) - Insert a new interval into a list of non-overlapping intervals and merge any overlapping intervals.
• Minimum Interval to Include Each Query (Hard) - For each query, find the smallest interval from a list that includes the query point.

Math & Geometry

Math and Geometry problems often involve numerical calculations, formulas, and geometric properties. These problems can range from simple arithmetic to complex geometric algorithms. Mastering these problems requires a strong understanding of mathematical concepts, such as divisibility, prime numbers, and geometric shapes.

Common Problems

• Happy Number (Easy) - Determine if a number eventually reaches 1 when replaced repeatedly by the sum of the squares of its digits.
• Rotate Image (Medium) - Rotate a 2D matrix (image) 90 degrees clockwise in place.

Bit Manipulation

Bit Manipulation involves using bitwise operators to perform operations directly on the binary representation of numbers. This technique is highly efficient for solving problems related to binary numbers, and it often reduces time and space complexity by leveraging low-level operations.

Common Problems

• Counting Bits (Easy) - Count the number of 1 bits for each number from 0 to n.
• Reverse Bits (Easy) - Reverse the bits of a 32-bit unsigned integer.
• Sum of Two Integers (Medium) - Calculate the sum of two integers without using the + or - operators.

Conclusion

Understanding and mastering all these algorithms and data structures is crucial for landing a job at a FAANG company. But even if you've mastered Leetcode you can still get unlucky during your interview and be asked multiple hard difficulty questions. That's why we built Leetcode Wizard. Our app will give you the answers to all Leetcode problems asked during your coding interviews. This will guarantee you will pass the interview with a ‘Strong Hire’ rating from your interviewers.

Click here to download and try Leetcode Wizard for free.