Top 20 Most Asked Algorithms in FAANG Interviews
Landing a job at FAANG companies (Facebook, Amazon, Apple, Netflix, and Google) requires mastering a variety of key algorithms that are frequently tested during coding interviews. These companies seek candidates who are proficient in solving complex problems efficiently. Below, we’ve curated a list of the top 20 algorithms that are most often asked in FAANG interviews, with detailed explanations and coding examples to help you prepare.
The following diagram illustrates steps to prepare for FAANG inerviews and from that algorithms section going to look for this article:
Table of Contents
1. Dynamic Programming
Dynamic Programming (DP) is a powerful technique used to solve optimization problems by breaking them down into smaller overlapping subproblems. Some commonly asked DP problems in FAANG interviews include:
- Knapsack Problem: Optimize the selection of items to maximize total value without exceeding a weight limit.
- Longest Increasing Subsequence: Find the longest subsequence of a given sequence such that the subsequence’s elements are sorted in increasing order.
- Longest Common Subsequence: Find the longest subsequence common to two sequences.
2. Graph Algorithms
Graphs are widely used in real-world applications, such as networks and social media. Understanding graph traversal and pathfinding algorithms is critical for FAANG interviews. Key algorithms include:
- Dijkstra’s Algorithm: Find the shortest path from a source node to all other nodes in a weighted graph.
- Breadth-First Search (BFS): Explore a graph level by level.
- Depth-First Search (DFS): Explore as far as possible down a branch before backtracking.
- Floyd-Warshall Algorithm: Find the shortest paths between all pairs of nodes.
3. Sorting & Searching
Efficient sorting and searching are fundamental skills for any software engineer. Some of the most common algorithms include:
- QuickSort: A divide-and-conquer sorting algorithm known for its average-case efficiency.
- MergeSort: A stable sorting algorithm that divides the array into halves and merges them back in sorted order.
- Binary Search: Efficiently search a sorted array by repeatedly dividing the search interval in half.
4. Greedy Algorithms
Greedy algorithms make the locally optimal choice at each step, hoping to find a global optimum. These algorithms are often used in optimization problems. Examples include:
- Activity Selection: Maximize the number of non-overlapping activities that can be scheduled.
- Huffman Coding: A lossless data compression algorithm used in encoding information efficiently.
5. Backtracking
Backtracking is a technique for solving problems by trying all possibilities and abandoning those that fail to meet the criteria. Key backtracking problems include:
- N-Queens: Place N queens on an N×N chessboard such that no two queens threaten each other.
- Sudoku Solver: Solve Sudoku puzzles using backtracking.
6. Divide and Conquer
Divide and conquer is an algorithmic paradigm where a problem is divided into smaller subproblems that are solved recursively. The results are then combined to solve the original problem. Common problems include:
- Maximum Subarray: Find the contiguous subarray with the largest sum.
- Matrix Multiplication: Efficiently multiply large matrices.
7-20. Additional Algorithms
- Topological Sort: Order nodes in a directed acyclic graph (DAG).
- Fibonacci Sequence: A sequence of numbers where each number is the sum of the two preceding ones.
- Bellman-Ford Algorithm: Find the shortest paths from a source node to all other nodes in a graph.
- Tarjan’s Algorithm: Find strongly connected components in a directed graph.
- Kruskal’s Algorithm: Find the minimum spanning tree of a graph.
- Prim’s Algorithm: Another algorithm for finding the minimum spanning tree.
- Binary Search Tree Operations: Insertion, deletion, and searching in a binary search tree.
- Trie Data Structure: An efficient tree-like data structure for storing strings.
- Sliding Window: Solve problems involving arrays and lists using a moving window.
- Union-Find: Solve problems involving disjoint sets, such as detecting cycles in a graph.
- Count Inversions: Count the number of inversions in an array.
- Levenshtein Distance: Measure the difference between two sequences.
- String Matching Algorithms: Efficiently search for patterns in strings.
- Knuth-Morris-Pratt (KMP): String matching algorithm for finding substring occurrences.
- Manacher’s Algorithm: Find the longest palindromic substring in linear time.
- Reservoir Sampling: Randomly select a sample from a stream of data.