Quiz (3)
Grokking Algorithm Complexity and Big-O Rate this course Ask Author Contribute Back to course home 0% completed Vote For New Content Introduction Course Overview Basics of Algorithm Analysis Introduction to Algorithm Analysis Measuring Efficiency Functions and Their Growth Rates Asymptotic Notations Overview of Asymptotic Analysis Big-O Notation (O-notation) Big-Omega Notation (Ω-notation) Big-Theta Notation (Θ-notation) Little-o and Little-omega Notations Comparing Asymptotic Notations Time Complexity Analysis Understanding Time Complexity Analyzing Control Structures Analyzing Simple Algorithms Best, Worst, and Average Cases Constant Time: O(1) Linear Time: O(n) Quadratic Time: O(n²) Quiz Space Complexity Analysis Understanding Space Complexity Analyzing Space Complexity of Algorithms Constant Space: O(1) Linear Space: O(n) Quadratic Space: O(n²) Quiz Analyzing Recursive Algorithms Introduction to Recursion Recursion Tree Method Recurrence Relation Method Master Theorem Method Space Complexity Analysis of Recursive Algorithm Logarithmic Time and Space: O(log n) Linearithmic Time: O(n log n) Exponential Time and Space: O(2ⁿ) Quiz Data Structures and Complexity Analysis Array Linked List Hash Table and Set Stack and Queue Binary Search Tree Quiz Practical Applications and Case Studies Sorting Algorithms Graph Algorithms Dynamic Programming Algorithms Coding Interview Problems Trade-offs in Algorithm Design Quiz Question 1 What is the time complexity of the following recursive function? public void printNumbers(int n) { if (n == 0) return; System.out.println(n); printNumbers(n - 1); } A O(1) B O(n) C O(n 2 ) D O(log n) Question 2 What is the space complexity of the following recursive function? public int factorial(int n) { if (n == 0) return 1; return n * factorial(n - 1); } A O(1) B O(log n) C O(n) D O(n 2 ) Question 3 Analyze the time complexity of the following code: public int fibonacci(int n) { if (n <= 1) return n; return fibonacci(n - 1) + fibonacci(n - 2); } A O(n) B O(n 2 ) C O(2 n ) D O(log n) Question 4 What is the space complexity of the following recursive function using memoization? import java.util.HashMap; public class Solution { private HashMap<Integer, Integer> memo = new HashMap<>(); public int fib(int n) { if (n <= 1) return n; if (memo.containsKey(n)) return memo.get(n); int result = fib(n - 1) + fib(n - 2); memo.put(n, result); return result; } } A O(1) B O(n log n) C O(n 2 ) D O(n) Question 5 What is the time complexity of the following recursive function? public void nestedRecursion(int n) { if (n <= 0) return; nestedRecursion(n - 1); nestedRecursion(n - 1); } A O(n) B O(2 n ) C O(n 2 ) D O(logn) Question 6 Analyze the space complexity of the following function: public int power(int x, int n) { if (n == 0) return 1; int temp = power(x, n / 2); if (n % 2 == 0) { return temp * temp; } else { return x * temp * temp; } } A O(n) B O(log n) C O(1) D O(n 2 ) Question 7 What is the time complexity of the following recursive function? public void printPattern(int n) { if (n == 0) return; for (int i = 0; i < n; i++) { System.out.print("*"); } System.out.println(); printPattern(n - 1); } A O(n) B O(2 n ) C O(logn) D O(n 2 ) Question 8 What is the time complexity of the following recursive function that calculates the depth of a binary tree? public int sumOfNodes(TreeNode root) { if (root == null) return 0; return root.val + sumOfNodes(root.left) + sumOfNodes(root.right); } A O(n) B O(log n) C O(n 2 ) D O(2 n ) Exponential Time and Space: O(2ⁿ) Array Mark as Completed On this page
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