Quiz (2)
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 Which of the following statements is true about space complexity? A Space complexity only considers the space taken by input variables. B Space complexity measures the total memory used by an algorithm, including output, and auxiliary space. C Space complexity is independent of the input size. D Space complexity is only important for recursive algorithms. Question 2 What is auxiliary space? A The space taken by the input itself. B Extra space or temporary space used by an algorithm, excluding the input. C The total space taken by an algorithm, including input. D The space taken by the output of an algorithm. Question 3 What is the space complexity of the following code? public void printArray(int[] arr) { for (int i = 0; i < arr.length; i++) { System.out.println(arr[i]); } } A O(1) B O(n) C O(n 2 ) D O(log n) Question 4 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 2 ) D O(n) Question 5 Analyze the space complexity of the following code: public int uniquePaths(int m, int n) { int[][] dp = new int[m][n]; for (int i = 0; i < m; i++) { dp[i][0] = 1; } for (int j = 0; j < n; j++) { dp[0][j] = 1; } for (int i = 1; i < m; i++) { for (int j = 1; j < n; j++) { dp[i][j] = dp[i - 1][j] + dp[i][j - 1]; } } return dp[m - 1][n - 1]; } A O(m * n) B O(m + n) C O(n) D O(1) Question 6 What is the space complexity of the following code? public int[] calculatePrefixSum(int[] arr) { int[] prefixSum = new int[arr.length]; prefixSum[0] = arr[0]; for (int i = 1; i < arr.length; i++) { prefixSum[i] = prefixSum[i - 1] + arr[i]; } return prefixSum; } A O(1) B O(n 2 ) C O(n) D O(log n) Quadratic Space: O(n²) Introduction to Recursion Mark as Completed On this page
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