Explore topic-wise MCQs in Data Structures and Algorithms.

This section includes 11 Mcqs, each offering curated multiple-choice questions to sharpen your Data Structures and Algorithms knowledge and support exam preparation. Choose a topic below to get started.

1.

What is the space complexity of the following dynamic programming implementation of the Knapsack problem?

A. O(n)
B. O(n + w)
C. O(nW)
D. O(n2)View Answer
Answer» D. O(n2)View Answer
Discussion
2.

What is the time complexity of the following dynamic programming implementation of the Knapsack problem with n items and a maximum weight of W?

A. O(n)
B. O(n + w)
C. O(nW)
D. O(n2)View Answer
Answer» D. O(n2)View Answer
Discussion
3.

Consider the following dynamic programming implementation of the Knapsack problem: Which of the following lines completes the above code?

A. find_max(ans[itm – 1][w – wt[itm – 1]] + val[itm – 1], ans[itm – 1][w])
B. find_max(ans[itm – 1][w – wt[itm – 1]], ans[itm – 1][w])
C. ans[itm][w] = ans[itm – 1][w];
D. ans[itm+1][w] = ans[itm – 1][w];View Answer
Answer» B. find_max(ans[itm – 1][w – wt[itm – 1]], ans[itm – 1][w])
Discussion
4.

WHAT_IS_THE_SPACE_COMPLEXITY_OF_THE_ABOVE_DYNAMIC_PROGRAMMING_IMPLEMENTATION_OF_THE_KNAPSACK_PROBLEM??$

A. O(n)
B. O(n + w)
C. O(nW)
D. O(n<sup>2</sup>)
Answer» E.
Discussion
5.

What is the time complexity of the above dynamic programming implementation of the Knapsack problem with n items and a maximum weight of W?

A. O(n)
B. O(n + w)
C. O(nW)
D. O(n<sup>2</sup>)
Answer» D. O(n<sup>2</sup>)
Discussion
6.

The 0-1 Knapsack problem can be solved using Greedy algorithm.

A. True
B. False
Answer» C.
Discussion
7.

What is the time complexity of the brute force algorithm used to solve the Knapsack problem?

A. O(n)
B. O(n!)
C. O(2<sup>n</sup>)
D. O(n<sup>3</sup>)
Answer» D. O(n<sup>3</sup>)
Discussion
8.

Which of the following problems is equivalent to the 0-1 Knapsack problem?

A. You are given a bag that can carry a maximum weight of W. You are given N items which have a weight of {w1, w2, w3,…., wn} and a value of {v1, v2, v3,…., vn}. You can break the items into smaller pieces. Choose the items in such a way that you get the maximum value
B. You are studying for an exam and you have to study N questions. The questions take {t1, t2, t3,…., tn} time(in hours) and carry {m1, m2, m3,…., mn} marks. You can study for a maximum of T hours. You can either study a question or leave it. Choose the questions in such a way that your score is maximized
C. You are given infinite coins of denominations {v1, v2, v3,….., vn} and a sum S. You have to find the minimum number of coins required to get the sum S
D. None of the mentioned
Answer» C. You are given infinite coins of denominations {v1, v2, v3,‚Äö√Ñ√∂‚àö√묨‚àÇ.., vn} and a sum S. You have to find the minimum number of coins required to get the sum S
Discussion
9.

You are given a knapsack that can carry a maximum weight of 60. There are 4 items with weights {20, 30, 40, 70} and values {70, 80, 90, 200}. What is the maximum value of the items you can carry using the knapsack?

A. 160
B. 200
C. 170
D. 90
Answer» B. 200
Discussion
10.

Which of the following methods can be used to solve the Knapsack problem?

A. Brute force algorithm
B. Recursion
C. Dynamic programming
D. All of the mentioned
Answer» E.
Discussion
11.

The Knapsack problem is an example of ____________

A. Greedy algorithm
B. 2D dynamic programming
C. 1D dynamic programming
D. Divide and conquer
Answer» C. 1D dynamic programming
Discussion