Computer Science & IT

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Computer Science & IT MCQ & Objective Questions

Computer Science & IT is a crucial subject for students preparing for school and competitive exams in India. Mastering this field not only enhances your understanding of technology but also significantly boosts your exam scores. Practicing MCQs and objective questions is an effective way to reinforce your knowledge and identify important questions that frequently appear in exams.

What You Will Practise Here

Fundamentals of Computer Science
Data Structures and Algorithms
Operating Systems Concepts
Networking Basics and Protocols
Database Management Systems
Software Engineering Principles
Programming Languages Overview

Exam Relevance

Computer Science & IT is an integral part of the curriculum for CBSE, State Boards, and competitive exams like NEET and JEE. Questions often focus on theoretical concepts, practical applications, and problem-solving skills. Common patterns include multiple-choice questions that test your understanding of key concepts, definitions, and the ability to apply knowledge in various scenarios.

Common Mistakes Students Make

Confusing similar concepts in data structures, such as arrays and linked lists.
Overlooking the importance of algorithms and their time complexities.
Misunderstanding the functions and roles of different operating system components.
Neglecting to practice coding problems, leading to difficulty in programming questions.
Failing to grasp the fundamentals of networking, which can lead to errors in related MCQs.

FAQs

Question: What are the best ways to prepare for Computer Science & IT exams?
Answer: Regular practice of MCQs, understanding key concepts, and reviewing past exam papers are effective strategies.

Question: How can I improve my problem-solving skills in Computer Science?
Answer: Engage in coding exercises, participate in study groups, and tackle a variety of practice questions.

Start your journey towards mastering Computer Science & IT today! Solve our practice MCQs to test your understanding and enhance your exam preparation. Remember, consistent practice is the key to success!

Artificial Intelligence & ML Compiler Design Computer Networks Data Structures & Algorithms Databases Operating Systems Programming & DS Programming Fundamentals Theory of Computation Web Development

Q. What is the time complexity for deleting a node in an AVL tree?

A. O(n)
B. O(log n)
C. O(n log n)
D. O(1)

Solution

The time complexity for deleting a node in an AVL tree is O(log n) as the tree remains balanced after deletion.

Correct Answer: B — O(log n)

Q. What is the time complexity for deleting an element from a Red-Black tree?

A. O(n)
B. O(log n)
C. O(n log n)
D. O(1)

Solution

The time complexity for deleting an element from a Red-Black tree is O(log n) due to its balanced structure.

Correct Answer: B — O(log n)

Q. What is the time complexity for deleting an element from an AVL tree?

A. O(n)
B. O(log n)
C. O(n log n)
D. O(1)

Solution

Deleting an element from an AVL tree also takes O(log n) time, as it may require rebalancing.

Correct Answer: B — O(log n)

Q. What is the time complexity for searching an element in a balanced AVL tree?

A. O(n)
B. O(log n)
C. O(n log n)
D. O(1)

Solution

The time complexity for searching an element in a balanced AVL tree is O(log n) due to its balanced nature.

Correct Answer: B — O(log n)

Q. What is the time complexity for searching an element in a Red-Black tree?

A. O(n)
B. O(log n)
C. O(n log n)
D. O(1)

Solution

Searching for an element in a Red-Black tree takes O(log n) time due to its balanced nature.

Correct Answer: B — O(log n)

Q. What is the time complexity for searching an element in an AVL tree?

A. O(n)
B. O(log n)
C. O(n log n)
D. O(1)

Solution

The time complexity for searching an element in an AVL tree is O(log n) due to its balanced nature.

Correct Answer: B — O(log n)

Q. What is the time complexity of a binary search algorithm on a sorted array?

A. O(1)
B. O(log n)
C. O(n)
D. O(n log n)

Solution

The time complexity of binary search on a sorted array is O(log n) because it repeatedly divides the search interval in half.

Correct Answer: B — O(log n)

Q. What is the time complexity of a binary search on a sorted array?

A. O(n)
B. O(log n)
C. O(n log n)
D. O(1)

Solution

Binary search divides the array in half with each step, leading to a time complexity of O(log n).

Correct Answer: B — O(log n)

Q. What is the time complexity of a breadth-first search (BFS) in a graph?

A. O(V)
B. O(E)
C. O(V + E)
D. O(V^2)

Solution

BFS visits each vertex and edge once, leading to a time complexity of O(V + E), where V is vertices and E is edges.

Correct Answer: C — O(V + E)

Q. What is the time complexity of a breadth-first search (BFS) on a graph with V vertices and E edges?

A. O(V)
B. O(E)
C. O(V + E)
D. O(V * E)

Solution

BFS visits each vertex and edge once, leading to a time complexity of O(V + E).

Correct Answer: C — O(V + E)

Q. What is the time complexity of a breadth-first search (BFS) on a graph?

A. O(V)
B. O(E)
C. O(V + E)
D. O(V^2)

Solution

BFS visits each vertex and edge once, resulting in a time complexity of O(V + E).

Correct Answer: C — O(V + E)

Q. What is the time complexity of a bubble sort algorithm in the worst case?

A. O(n)
B. O(n log n)
C. O(n^2)
D. O(log n)

Solution

Bubble sort compares adjacent elements and requires O(n^2) time in the worst case.

Correct Answer: C — O(n^2)

Q. What is the time complexity of a depth-first search (DFS) in a graph?

A. O(V)
B. O(E)
C. O(V + E)
D. O(V^2)

Solution

DFS visits each vertex and edge once, leading to a time complexity of O(V + E), where V is vertices and E is edges.

Correct Answer: C — O(V + E)

Q. What is the time complexity of a depth-first search (DFS) on a graph with V vertices and E edges?

A. O(V + E)
B. O(V)
C. O(E)
D. O(V^2)

Solution

DFS visits each vertex and edge once, leading to a time complexity of O(V + E).

Correct Answer: A — O(V + E)

Q. What is the time complexity of a depth-first search (DFS) on a graph?

A. O(V)
B. O(E)
C. O(V + E)
D. O(V * E)

Solution

DFS visits each vertex and edge once, leading to a time complexity of O(V + E), where V is vertices and E is edges.

Correct Answer: C — O(V + E)

Q. What is the time complexity of a depth-first search (DFS) on a tree?

A. O(V)
B. O(E)
C. O(V + E)
D. O(n log n)

Solution

DFS visits each vertex once, resulting in a time complexity of O(V) for trees.

Correct Answer: A — O(V)

Q. What is the time complexity of a level order traversal of a binary tree?

A. O(n)
B. O(log n)
C. O(n log n)
D. O(1)

Solution

Level order traversal visits each node exactly once, resulting in a time complexity of O(n), where n is the number of nodes in the tree.

Correct Answer: A — O(n)

Q. What is the time complexity of a linear search algorithm?

A. O(1)
B. O(n)
C. O(log n)
D. O(n^2)

Solution

Linear search checks each element one by one, resulting in a time complexity of O(n).

Correct Answer: B — O(n)

Q. What is the time complexity of a linear search in an array?

A. O(1)
B. O(n)
C. O(log n)
D. O(n log n)

Solution

Linear search checks each element one by one, resulting in a time complexity of O(n).

Correct Answer: B — O(n)

Q. What is the time complexity of a linear search in an unsorted array?

A. O(1)
B. O(n)
C. O(log n)
D. O(n log n)

Solution

A linear search requires checking each element, leading to a time complexity of O(n).

Correct Answer: B — O(n)

Q. What is the time complexity of a queue's enqueue operation in a linked list implementation?

A. O(1)
B. O(n)
C. O(log n)
D. O(n log n)

Solution

The enqueue operation in a linked list implementation of a queue has a time complexity of O(1) because it adds an element to the end of the list.

Correct Answer: A — O(1)

Q. What is the time complexity of a queue's enqueue operation using a linked list?

A. O(1)
B. O(n)
C. O(log n)
D. O(n log n)

Solution

Enqueuing an element at the end of a linked list can be done in constant time, O(1).

Correct Answer: A — O(1)

Q. What is the time complexity of a queue's enqueue operation?

A. O(1)
B. O(n)
C. O(log n)
D. O(n^2)

Solution

Enqueue operation in a queue is done in constant time, hence O(1).

Correct Answer: A — O(1)

Q. What is the time complexity of a recursive function that divides the problem size by half at each step?

A. O(n)
B. O(log n)
C. O(n log n)
D. O(2^n)

Solution

The time complexity is O(log n) because the problem size is halved at each recursive call.

Correct Answer: B — O(log n)

Q. What is the time complexity of accessing an element in a linked list by index?

A. O(1)
B. O(n)
C. O(log n)
D. O(n log n)

Solution

Accessing an element in a linked list by index is O(n) because it requires traversing the list from the head to the desired index.

Correct Answer: B — O(n)

Q. What is the time complexity of accessing an element in a queue implemented using a linked list?

A. O(1)
B. O(n)
C. O(log n)
D. O(n^2)

Solution

Accessing an element in a queue implemented using a linked list requires traversing the list, resulting in a time complexity of O(n).

Correct Answer: B — O(n)

Q. What is the time complexity of accessing an element in a queue?

A. O(1)
B. O(n)
C. O(log n)
D. O(n^2)

Solution

Accessing an element in a queue typically requires O(n) time because you may need to traverse the queue to find the element.

Correct Answer: B — O(n)

Q. What is the time complexity of accessing an element in a stack?

A. O(n)
B. O(log n)
C. O(1)
D. O(n log n)

Solution

Accessing the top element of a stack is a constant time operation, hence O(1).

Correct Answer: C — O(1)

Q. What is the time complexity of accessing an element in an array by index?

A. O(n)
B. O(log n)
C. O(1)
D. O(n log n)

Solution

Accessing an element in an array by index is O(1) because it requires a constant amount of time regardless of the size of the array.

Correct Answer: C — O(1)

Q. What is the time complexity of accessing an element in an array?

A. O(1)
B. O(n)
C. O(log n)
D. O(n^2)

Solution

Accessing an element in an array by index is done in constant time, O(1).

Correct Answer: A — O(1)

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