Welcome to the Algorithm Complexity MCQs Page

Dive deep into the fascinating world of Algorithm Complexity with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Algorithm Complexity, a crucial aspect of Data Structures and Algorithms. In this section, you will encounter a diverse range of MCQs that cover various aspects of Algorithm Complexity, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Data Structures and Algorithms.

Check out the MCQs below to embark on an enriching journey through Algorithm Complexity. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Data Structures and Algorithms.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Algorithm Complexity. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Algorithm Complexity MCQs | Page 5 of 10

Explore more Topics under Data Structures and Algorithms

01

Data Structures Basics

02

Data Types and Abstraction

03

Arrays and Pointers

04

Linked Lists

05

Stacks and Queues

06

Recursion

07

Trees

08

Graphs

09

String Operations

10

Sorting and Searching

11

Object Oriented Programming

12

Dynamic Programming

Q41.

Space complexity of an algorithm is the maximum amount of _______ required by it during execution.

Time

Operations

Memory space

None of the above

Discuss

Answer: (c).Memory space

Q42.

Frequently, the memory space required by an algorithm is a multiple of the size of input. State if the statement is True or False or Maybe.

True

False

Maybe

None of the above

Discuss

Answer: (a).True

Q43.

For many problems such as sorting, there are many choices of algorithms to use, some of which are extremely___________.

Space efficient

Time efficient

Both (a) and (b)

None of the above

Discuss

Answer: (c).Both (a) and (b)

Q44.

In the analysis of algorithms, what plays an important role?

Text Analysis

Growth factor

Time

None of the above

Discuss

Answer: (b).Growth factor

Q45.

An algorithm performs lesser number of operations when the size of input is small, but performs more operations when the size of input gets larger. State if the statement is True or False or Maybe.

True

False

Maybe

None of the above

Discuss

Answer: (a).True

Q46.

To verify whether a function grows faster or slower than the other function, we have some asymptotic or mathematical notations, which is_________.

Big Omega Ω (f)

Big Theta θ (f)

Big Oh O (f)

All of the above

Discuss

Answer: (d).All of the above

Q47.

A function in which f(n) is Ω(g(n)), if there exist positive values k and c such that f(n)>=c*g(n), for all n>=k. This notation defines a lower bound for a function f(n):

Big Omega Ω (f)

Big Theta θ (f)

Big Oh O (f)

None of the above

Discuss

Answer: (a).Big Omega Ω (f)

Q48.

An algorithm that indicates the amount of temporary storage required for running the algorithm, i.e., the amount of memory needed by the algorithm to run to completion is termed as_____.

Big Theta θ (f)

Space complexity

Big Oh O (f)

None of the above

Discuss

Answer: (b).Space complexity

Q49.

The amount of time the computer needs to run to completion is known as_____.

Space complexity

Time complexity

Recursive function

None of the above

Discuss

Answer: (b).Time complexity

Q50.

___________algorithm is one which utilizes minimum processor time and requires minimum memory space during its execution.

Best

Efficient

Both (a) and (b)

None of the above

Discuss

Answer: (c).Both (a) and (b)

Page 5 of 10

Welcome to the Algorithm Complexity MCQs Page

Algorithm Complexity MCQs | Page 5 of 10

Explore more Topics under Data Structures and Algorithms

Data Structures Basics

Data Types and Abstraction

Arrays and Pointers

Linked Lists

Stacks and Queues

Recursion

Trees

Graphs

String Operations

Sorting and Searching

Object Oriented Programming

Dynamic Programming

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