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!
Q. In a clustering case study, which of the following is a real-world application?
A.
Spam detection in emails
B.
Customer segmentation in marketing
C.
Predicting stock prices
D.
Image classification
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Solution
Customer segmentation in marketing is a common application of clustering to identify distinct customer groups.
Correct Answer:
B
— Customer segmentation in marketing
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Q. In a complete binary tree, how many nodes are there at height 'h'?
A.
h + 1
B.
2^h
C.
2^(h+1) - 1
D.
h^2
Show solution
Solution
In a complete binary tree, the number of nodes at height 'h' is 2^(h+1) - 1.
Correct Answer:
C
— 2^(h+1) - 1
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Q. In a complete binary tree, how many nodes are there at level k?
A.
2^k
B.
2^(k+1)
C.
2^(k-1)
D.
k^2
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Solution
In a complete binary tree, the number of nodes at level k is given by 2^k, where k starts from 0 for the root level.
Correct Answer:
A
— 2^k
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Q. In a complete binary tree, how many nodes are there at the last level if the total number of levels is 'h'?
A.
2^(h-1)
B.
2^h
C.
2^(h+1)
D.
2^(h-1) - 1
Show solution
Solution
In a complete binary tree, the last level can have up to 2^(h-1) nodes, where h is the total number of levels.
Correct Answer:
A
— 2^(h-1)
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Q. In a complete binary tree, what is the maximum number of nodes at level 'h'?
A.
2^h
B.
2^(h+1) - 1
C.
h^2
D.
h!
Show solution
Solution
In a complete binary tree, the maximum number of nodes at level 'h' is 2^h.
Correct Answer:
A
— 2^h
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Q. In a complete binary tree, what is the relationship between the number of nodes and the height of the tree?
A.
Nodes = 2^height
B.
Nodes = 2^(height + 1) - 1
C.
Nodes = height^2
D.
Nodes = height!
Show solution
Solution
In a complete binary tree, the number of nodes is given by the formula Nodes = 2^(height + 1) - 1.
Correct Answer:
B
— Nodes = 2^(height + 1) - 1
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Q. In a complete binary tree, what is the relationship between the number of nodes and the height?
A.
Height = log(n)
B.
Height = n
C.
Height = n/2
D.
Height = n^2
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Solution
In a complete binary tree, the height is approximately log(n), where n is the number of nodes.
Correct Answer:
A
— Height = log(n)
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Q. In a complete binary tree, what is the time complexity of DFS?
A.
O(log n)
B.
O(n)
C.
O(n log n)
D.
O(1)
Show solution
Solution
In a complete binary tree, the time complexity of DFS is O(n) as it visits every node once.
Correct Answer:
B
— O(n)
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Q. In a confusion matrix, what does the term 'specificity' refer to?
A.
True Positive Rate
B.
False Positive Rate
C.
True Negative Rate
D.
False Negative Rate
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Solution
Specificity is the True Negative Rate, indicating the proportion of actual negatives that are correctly identified.
Correct Answer:
C
— True Negative Rate
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Q. In a Decision Tree, what does the Gini impurity measure?
A.
The accuracy of the model.
B.
The likelihood of misclassifying a randomly chosen element.
C.
The depth of the tree.
D.
The number of features used.
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Solution
Gini impurity measures the likelihood of misclassifying a randomly chosen element from the dataset, helping to determine the best splits.
Correct Answer:
B
— The likelihood of misclassifying a randomly chosen element.
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Q. In a Decision Tree, what does the term 'Gini impurity' refer to?
A.
A measure of the tree's depth
B.
A metric for evaluating model performance
C.
A criterion for splitting nodes
D.
A method for pruning trees
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Solution
Gini impurity is a criterion used to measure the impurity of a node, helping to determine the best feature to split on.
Correct Answer:
C
— A criterion for splitting nodes
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Q. In a Decision Tree, what does the term 'node' refer to?
A.
A point where a decision is made.
B.
The final output of the tree.
C.
The data used to train the model.
D.
The overall structure of the tree.
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Solution
A 'node' in a Decision Tree is where a decision is made based on the feature values.
Correct Answer:
A
— A point where a decision is made.
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Q. In a depth-first search, what happens when a node is visited?
A.
It is added to the queue.
B.
It is marked as visited and all its adjacent nodes are explored.
C.
It is removed from the graph.
D.
It is added to the stack.
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Solution
In DFS, when a node is visited, it is marked as visited and all its adjacent nodes are explored.
Correct Answer:
B
— It is marked as visited and all its adjacent nodes are explored.
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Q. In a double-ended queue (deque), which operations can be performed at both ends?
A.
Enqueue only
B.
Dequeue only
C.
Enqueue and Dequeue
D.
None
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Solution
A double-ended queue (deque) allows both enqueue and dequeue operations to be performed at both ends, making it more flexible than a standard queue.
Correct Answer:
C
— Enqueue and Dequeue
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Q. In a doubly linked list, how do you delete a node given only a pointer to that node?
A.
Set next and previous pointers
B.
Traverse from head
C.
Use a stack
D.
Not possible
Show solution
Solution
In a doubly linked list, you can delete a node by adjusting the next and previous pointers of the adjacent nodes.
Correct Answer:
A
— Set next and previous pointers
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Q. In a doubly linked list, how many pointers does each node contain?
A.
One
B.
Two
C.
Three
D.
Four
Show solution
Solution
Each node in a doubly linked list contains two pointers: one to the next node and one to the previous node.
Correct Answer:
B
— Two
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Q. In a doubly linked list, what is the time complexity for deleting a node given a pointer to that node?
A.
O(1)
B.
O(n)
C.
O(log n)
D.
O(n^2)
Show solution
Solution
Deletion of a node in a doubly linked list can be done in O(1) time if you have a pointer to the node.
Correct Answer:
A
— O(1)
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Q. In a doubly linked list, what is the time complexity of deleting a node given a pointer to that node?
A.
O(1)
B.
O(n)
C.
O(log n)
D.
O(n^2)
Show solution
Solution
If you have a pointer to the node to be deleted, you can adjust the pointers of the adjacent nodes in constant time, so the time complexity is O(1).
Correct Answer:
A
— O(1)
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Q. In a doubly linked list, what is the time complexity of inserting a new node after a given node?
A.
O(1)
B.
O(n)
C.
O(log n)
D.
O(n^2)
Show solution
Solution
Inserting a new node after a given node in a doubly linked list involves changing a few pointers, which takes constant time, hence O(1).
Correct Answer:
A
— O(1)
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Q. In a dynamic programming approach, what is the primary technique used to optimize recursive solutions?
A.
Memoization
B.
Backtracking
C.
Greedy Method
D.
Divide and Conquer
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Solution
Memoization is the technique used in dynamic programming to store the results of expensive function calls and reuse them when the same inputs occur again.
Correct Answer:
A
— Memoization
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Q. In a dynamic programming solution for the Longest Common Subsequence (LCS), what does the DP table represent?
A.
The length of the LCS
B.
The characters of the LCS
C.
The indices of the LCS
D.
The number of subsequences
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Solution
The DP table in LCS represents the length of the longest common subsequence between two strings.
Correct Answer:
A
— The length of the LCS
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Q. In a feature engineering case study, what is the role of domain knowledge?
A.
To automate model training
B.
To inform feature selection and creation
C.
To evaluate model performance
D.
To visualize data
Show solution
Solution
Domain knowledge helps identify relevant features that can significantly impact model performance.
Correct Answer:
B
— To inform feature selection and creation
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Q. In a graph represented as an adjacency list, what is the space complexity of storing the graph?
A.
O(V)
B.
O(E)
C.
O(V + E)
D.
O(V^2)
Show solution
Solution
The space complexity of storing a graph in an adjacency list is O(V + E), where V is the number of vertices and E is the number of edges.
Correct Answer:
C
— O(V + E)
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Q. In a graph represented as an adjacency list, what is the space complexity?
A.
O(V + E)
B.
O(V^2)
C.
O(E)
D.
O(V)
Show solution
Solution
The space complexity of a graph represented as an adjacency list is O(V + E), where V is the number of vertices and E is the number of edges.
Correct Answer:
A
— O(V + E)
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Q. In a graph represented by an adjacency list, what is the space complexity?
A.
O(V)
B.
O(E)
C.
O(V + E)
D.
O(V * E)
Show solution
Solution
The space complexity of a graph represented by an adjacency list is O(V + E), where V is the number of vertices and E is the number of edges.
Correct Answer:
C
— O(V + E)
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Q. In a graph with V vertices and E edges, what is the time complexity of DFS?
A.
O(V)
B.
O(E)
C.
O(V + E)
D.
O(V * E)
Show solution
Solution
DFS has a time complexity of O(V + E) as it visits each vertex and edge once.
Correct Answer:
C
— O(V + E)
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Q. In a graph, if all edges have the same weight, which algorithm can be used to find the shortest path?
A.
Dijkstra's algorithm
B.
Breadth-First Search (BFS)
C.
Depth-First Search (DFS)
D.
A* Search
Show solution
Solution
When all edges have the same weight, Breadth-First Search (BFS) can be used to find the shortest path.
Correct Answer:
B
— Breadth-First Search (BFS)
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Q. In a graph, if there are multiple paths to a node, how does Dijkstra's algorithm determine which path to take?
A.
It chooses the path with the most edges
B.
It chooses the path with the least weight
C.
It randomly selects a path
D.
It chooses the first path it encounters
Show solution
Solution
Dijkstra's algorithm always chooses the path with the least cumulative weight to ensure the shortest path is found.
Correct Answer:
B
— It chooses the path with the least weight
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Q. In a graph, if there are multiple paths to reach a node, how does Dijkstra's algorithm choose the path?
A.
It chooses the path with the maximum weight
B.
It chooses the path with the minimum weight
C.
It chooses the first path it encounters
D.
It randomly selects a path
Show solution
Solution
Dijkstra's algorithm always chooses the path with the minimum weight to ensure the shortest path is found.
Correct Answer:
B
— It chooses the path with the minimum weight
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Q. In a graph, if you want to check for cycles, which traversal method is more suitable?
A.
BFS
B.
DFS
C.
Both are equally suitable
D.
Neither can check for cycles
Show solution
Solution
DFS is more suitable for cycle detection as it can track back edges effectively.
Correct Answer:
B
— DFS
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