150 Questions | 2.5 Hours | 3 Attempts Each
50 Easy · 50 Hard · 50 Advanced
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3 attempts per question
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Algorithms are the heart of computer science and software engineering. An algorithm is a step-by-step procedure for solving a problem or accomplishing a task. From sorting and searching to graph traversal and dynamic programming, algorithms form the foundation of efficient computing. Mastering algorithms is essential for cracking coding interviews at top tech companies like Google, Amazon, Microsoft, and for competitive exams like GATE CS.
This Algorithms quiz online free features 150 expertly crafted questions: 50 Easy, 50 Hard, and 50 Advanced. Each question includes detailed explanations covering sorting algorithms (Quick Sort, Merge Sort, Heap Sort), searching algorithms (Binary Search, Linear Search), graph algorithms (BFS, DFS, Dijkstra, Floyd-Warshall, Kruskal, Prim), dynamic programming (Knapsack, LCS, Matrix Chain Multiplication), greedy algorithms, divide and conquer, backtracking, and complexity analysis.
Every software engineer needs strong algorithmic thinking. Key topics include time complexity (Big-O notation), space complexity, sorting algorithms (Quick Sort O(n log n), Merge Sort, Bubble Sort), searching algorithms (Binary Search O(log n)), graph algorithms (BFS, DFS, Shortest Path, MST), dynamic programming (optimal substructure, overlapping subproblems), greedy algorithms, and NP-completeness. These concepts are tested in every major tech interview.
Quick Sort: Divide-and-conquer algorithm with average O(n log n) time complexity. Merge Sort: Stable sorting with O(n log n) time and O(n) space. Heap Sort: In-place sorting using heap data structure. Bubble Sort: Simple but slow O(n²) algorithm. Insertion Sort: Efficient for small datasets.
BFS (Breadth-First Search): Finds shortest path in unweighted graphs. DFS (Depth-First Search): Explores as far as possible before backtracking. Dijkstra's Algorithm: Finds shortest paths from source to all vertices. Floyd-Warshall: All-pairs shortest paths. Kruskal & Prim: Minimum Spanning Tree algorithms.
DP solves problems by breaking them into overlapping subproblems. Classic DP problems: Fibonacci, Knapsack, Longest Common Subsequence (LCS), Matrix Chain Multiplication, Edit Distance, and Coin Change. DP techniques include memoization (top-down) and tabulation (bottom-up).
Big-O notation describes algorithm performance. Common complexities: O(1) constant, O(log n) logarithmic, O(n) linear, O(n log n) linearithmic, O(n²) quadratic, O(2^n) exponential. Understanding complexity helps choose the right algorithm for your use case.
Software engineers and data scientists with strong algorithm skills earn $90,000-$200,000 annually. Algorithms are essential for GATE CS, NTA NET, and technical interviews at FAANG companies. Mastering algorithms opens doors to software development, AI/ML, and research roles.
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