Quiz
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 time complexity analysis? A Time complexity is a measure of the number of operations an algorithm performs relative to the input size. B Time complexity helps measure the space used by an algorithm. C Time complexity analysis is not useful for comparing algorithms. D Time complexity is independent of the input size. Question 2 What is the time complexity of the following code? public void printPairs(int[] arr) { for (int i = 0; i < arr.length; i++) { // Loop 1 for (int j = i + 1; j < arr.length; j++) { // Loop 2 System.out.println(arr[i] + ", " + arr[j]); } } } A O(n) B O(n 2 ) C O(log n) D O(n 3 ) Question 3 What is the time complexity of the following code snippet? public void printArray(int[] arr) { for (int i = 0; i < arr.length; i++) { System.out.println(arr[i]); } for (int i = arr.length - 1; i >= 0; i--) { System.out.println(arr[i]); } } A O(n) B O(n 2 ) C O(log n) D O(1) Question 4 Which of the following best describes Big-O notation? A It gives the exact runtime of an algorithm. B It measures the algorithm's best-case scenario. C It provides the upper bound of an algorithm's growth rate. D It is used for measuring space complexity only. Question 5 Analyze the time complexity of the following code: public boolean canJump(int[] nums) { int maxReach = 0; for (int i = 0; i < nums.length; i++) { if (i > maxReach) { return false; } maxReach = Math.max(maxReach, i + nums[i]); } return true; } A O(n) B O(n 2 ) C O(log n) D O(n log n) Question 6 What is the time complexity of the following code snippet? public int lengthOfLIS(int[] nums) { int n = nums.length; int[] dp = new int[n]; Arrays.fill(dp, 1); for (int i = 1; i < n; i++) { for (int j = 0; j < i; j++) { if (nums[i] > nums[j]) { dp[i] = Math.max(dp[i], dp[j] + 1); } } } int max = 0; for (int i = 0; i < n; i++) { max = Math.max(max, dp[i]); } return max; } A O(n) B O(n log n) C O(2 n ) D O(n 2 ) Question 7 What is the time complexity of the following code? public int matrixChainOrder(int[] p) { int n = p.length; int[][] dp = new int[n][n]; for (int len = 2; len < n; len++) { for (int i = 1; i < n - len + 1; i++) { int j = i + len - 1; dp[i][j] = Integer.MAX_VALUE; for (int k = i; k <= j - 1; k++) { int q = dp[i][k] + dp[k + 1][j] + p[i - 1] * p[k] * p[j]; dp[i][j] = Math.min(dp[i][j], q); } } } return dp[1][n - 1]; } A O(n) B O(n 2 ) C O(n 3 ) D O(2 n ) Question 8 What is the time complexity of the following code? public int jump(int[] nums) { int jumps = 0, currentEnd = 0, farthest = 0; for (int i = 0; i < nums.length - 1; i++) { farthest = Math.max(farthest, i + nums[i]); if (i == currentEnd) { jumps++; currentEnd = farthest; } } return jumps; } A O(n) B O(n 2 ) C O(n log n) D O(2 n ) Question 9 Analyze the time complexity of the following code: public int coinChange(int[] coins, int amount) { int[] dp = new int[amount + 1]; Arrays.fill(dp, amount + 1); dp[0] = 0; for (int i = 1; i <= amount; i++) { for (int coin : coins) { if (i >= coin) { dp[i] = Math.min(dp[i], dp[i - coin] + 1); } } } return dp[amount] > amount ? -1 : dp[amount]; } A O(n) B O(amount * coins.length) C O(n 2 ) D O(2 n ) Quadratic Time: O(n²) Understanding Space Complexity Mark as Completed On this page
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