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 which scenario would you prefer using Dijkstra's algorithm over A* search algorithm?
A.
When you need to find the shortest path in a grid
B.
When the heuristic is not available
C.
When the graph has negative weights
D.
When you need to find all paths
Show solution
Solution
Dijkstra's algorithm is preferred when there is no heuristic available, as A* relies on heuristics to guide its search.
Correct Answer:
B
— When the heuristic is not available
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Q. In which scenario would you prefer using Dijkstra's algorithm over the Bellman-Ford algorithm?
A.
When the graph has negative weights
B.
When the graph is dense
C.
When the graph has non-negative weights
D.
When you need to find all pairs shortest paths
Show solution
Solution
Dijkstra's algorithm is preferred when the graph has non-negative weights, as it is more efficient in such cases compared to Bellman-Ford.
Correct Answer:
C
— When the graph has non-negative weights
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Q. In which scenario would you prefer using linear regression over other algorithms?
A.
When the relationship between variables is non-linear
B.
When you need to classify data into categories
C.
When you want to predict a continuous outcome with a linear relationship
D.
When the data is unstructured
Show solution
Solution
Linear regression is preferred when predicting a continuous outcome variable that has a linear relationship with the independent variables.
Correct Answer:
C
— When you want to predict a continuous outcome with a linear relationship
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Q. In which scenario would you prefer using LSTMs over traditional RNNs?
A.
When the input data is static.
B.
When the sequences are very short.
C.
When the sequences have long-term dependencies.
D.
When computational resources are limited.
Show solution
Solution
LSTMs are preferred when dealing with sequences that have long-term dependencies due to their ability to remember information over time.
Correct Answer:
C
— When the sequences have long-term dependencies.
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Q. In which scenario would you prefer using Support Vector Machines over other algorithms?
A.
When the dataset is very large
B.
When the data is linearly separable
C.
When the data has a high dimensionality
D.
When interpretability is crucial
Show solution
Solution
Support Vector Machines are particularly effective in high-dimensional spaces, making them suitable for datasets with many features.
Correct Answer:
C
— When the data has a high dimensionality
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Q. In which scenario would you prefer using SVM over decision trees?
A.
When interpretability is crucial
B.
When the dataset is very large
C.
When the data is high-dimensional and sparse
D.
When the data is categorical
Show solution
Solution
SVM is preferred in high-dimensional and sparse datasets, as it can effectively find the optimal hyperplane in such spaces.
Correct Answer:
C
— When the data is high-dimensional and sparse
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Q. In which scenario would you prefer using SVM over logistic regression?
A.
When the dataset is small
B.
When the classes are linearly separable
C.
When the dataset has a high number of features
D.
When interpretability is crucial
Show solution
Solution
SVMs are particularly effective in high-dimensional spaces, making them suitable for datasets with many features.
Correct Answer:
C
— When the dataset has a high number of features
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Q. In which scenario would you prefer using SVM over other algorithms?
A.
When the dataset is very large
B.
When the data is linearly separable
C.
When the data has a high dimensionality
D.
When the data is highly imbalanced
Show solution
Solution
SVM is particularly effective in high-dimensional spaces, making it suitable for datasets with many features.
Correct Answer:
C
— When the data has a high dimensionality
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Q. In which scenario would you prefer using SVM over other classification algorithms?
A.
When the dataset is very large
B.
When the data is linearly separable
C.
When the data has a high dimensionality
D.
When the data is highly imbalanced
Show solution
Solution
SVM is particularly effective in high-dimensional spaces, making it suitable for datasets with many features.
Correct Answer:
C
— When the data has a high dimensionality
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Q. In which scenario would you prefer using the Matthews correlation coefficient?
A.
When dealing with binary classification problems
B.
When evaluating multi-class classification problems
C.
When the dataset is highly imbalanced
D.
All of the above
Show solution
Solution
The Matthews correlation coefficient is a robust metric that can be used for both binary and multi-class classification, especially in imbalanced datasets.
Correct Answer:
D
— All of the above
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Q. In which scenario would you prioritize recall over precision?
A.
When false positives are more costly than false negatives
B.
When false negatives are more costly than false positives
C.
When the dataset is balanced
D.
When you need a high overall accuracy
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Solution
In scenarios where missing a positive case (false negative) is more critical, such as in medical diagnoses, recall should be prioritized over precision.
Correct Answer:
B
— When false negatives are more costly than false positives
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Q. In which scenario would you typically use a CNN?
A.
Predicting stock prices
B.
Classifying images
C.
Analyzing text data
D.
Clustering customer segments
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Solution
CNNs are primarily used for classifying images due to their ability to capture spatial hierarchies in visual data.
Correct Answer:
B
— Classifying images
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Q. In which scenario would you typically use a Convolutional Neural Network (CNN)?
A.
Time series prediction
B.
Image classification
C.
Text generation
D.
Reinforcement learning
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Solution
CNNs are specifically designed for processing structured grid data like images, making them ideal for image classification tasks.
Correct Answer:
B
— Image classification
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Q. In which scenario would you use a binary tree for data storage?
A.
When data needs to be accessed in a sorted manner
B.
When data is static and does not change
C.
When data is accessed randomly
D.
When data is sequentially processed
Show solution
Solution
Binary trees are ideal for scenarios where data needs to be stored and accessed in a sorted manner, allowing for efficient searching.
Correct Answer:
A
— When data needs to be accessed in a sorted manner
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Q. In which scenario would you use a binary tree to represent hierarchical data?
A.
File system structure
B.
Social network connections
C.
Web page links
D.
All of the above
Show solution
Solution
Binary trees can effectively represent hierarchical data structures such as file systems, where each node can represent a directory or file.
Correct Answer:
A
— File system structure
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Q. In which scenario would you use a shadow deployment strategy?
A.
When you want to completely replace an old model
B.
When you want to test a new model without affecting users
C.
When you want to gather user feedback
D.
When you want to scale the model
Show solution
Solution
Shadow deployment allows testing a new model in production without affecting the user experience, as it runs alongside the existing model.
Correct Answer:
B
— When you want to test a new model without affecting users
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Q. In which scenario would you use linear regression?
A.
Predicting customer churn
B.
Forecasting sales revenue based on advertising spend
C.
Classifying emails as spam or not spam
D.
Segmenting customers into different groups
Show solution
Solution
Linear regression is suitable for forecasting sales revenue based on advertising spend, as it involves predicting a continuous variable.
Correct Answer:
B
— Forecasting sales revenue based on advertising spend
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Q. In which scenario would you use reinforcement learning?
A.
When you have labeled data for training
B.
When the model needs to learn from interactions with an environment
C.
When you want to cluster data points
D.
When you need to predict a continuous outcome
Show solution
Solution
Reinforcement learning is used when a model learns from interactions with an environment, optimizing actions based on rewards.
Correct Answer:
B
— When the model needs to learn from interactions with an environment
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Q. In which scenario would you use unsupervised learning for embeddings?
A.
When labeled data is available
B.
When you want to classify text
C.
When you want to discover patterns in unlabeled text
D.
When you need to evaluate model performance
Show solution
Solution
Unsupervised learning is used to discover patterns in unlabeled text, such as clustering or generating embeddings.
Correct Answer:
C
— When you want to discover patterns in unlabeled text
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Q. In which traversal method are nodes visited in ascending order for a binary search tree?
A.
Pre-order
B.
In-order
C.
Post-order
D.
Level-order
Show solution
Solution
In In-order traversal of a binary search tree, nodes are visited in ascending order.
Correct Answer:
B
— In-order
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Q. In which traversal method are nodes visited in the order of left child, root, right child?
A.
Pre-order
B.
Post-order
C.
In-order
D.
Level-order
Show solution
Solution
In In-order traversal, nodes are visited in the order of left child, root, and then right child.
Correct Answer:
C
— In-order
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Q. In which traversal method are nodes visited in the order of left subtree, root, right subtree?
A.
In-order
B.
Pre-order
C.
Post-order
D.
Level-order
Show solution
Solution
In In-order traversal, nodes are visited in the order of left subtree, root, and then right subtree.
Correct Answer:
A
— In-order
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Q. In which traversal method are nodes visited level by level?
A.
In-order
B.
Post-order
C.
Pre-order
D.
Level-order
Show solution
Solution
Level-order traversal visits nodes level by level, starting from the root.
Correct Answer:
D
— Level-order
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Q. In which traversal method do you visit the left subtree, then the root, and finally the right subtree?
A.
Pre-order
B.
In-order
C.
Post-order
D.
Level-order
Show solution
Solution
In In-order traversal, the left subtree is visited first, followed by the root node, and then the right subtree.
Correct Answer:
B
— In-order
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Q. What assumption is made about the residuals in linear regression?
A.
They should be normally distributed
B.
They should be correlated with the predictors
C.
They should have a non-constant variance
D.
They should be positive
Show solution
Solution
One of the key assumptions of linear regression is that the residuals (errors) should be normally distributed.
Correct Answer:
A
— They should be normally distributed
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Q. What color is the root node of a Red-Black tree?
A.
Red
B.
Black
C.
It can be either
D.
None of the above
Show solution
Solution
The root node of a Red-Black tree is always black, which helps maintain the properties of the tree.
Correct Answer:
B
— Black
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Q. What condition must be met for Dijkstra's algorithm to work correctly?
A.
All edge weights must be positive
B.
The graph must be a tree
C.
All nodes must be connected
D.
The graph must be directed
Show solution
Solution
Dijkstra's algorithm requires that all edge weights be non-negative to ensure that the shortest path is correctly identified.
Correct Answer:
A
— All edge weights must be positive
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Q. What data structure is commonly used to implement Dijkstra's algorithm efficiently?
A.
Array
B.
Linked List
C.
Priority Queue
D.
Stack
Show solution
Solution
A priority queue is used in Dijkstra's algorithm to efficiently retrieve the next node with the smallest tentative distance.
Correct Answer:
C
— Priority Queue
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Q. What data structure is commonly used to implement Dijkstra's algorithm?
A.
Array
B.
Stack
C.
Priority Queue
D.
Linked List
Show solution
Solution
A priority queue is commonly used in Dijkstra's algorithm to efficiently retrieve the next node with the smallest tentative distance.
Correct Answer:
C
— Priority Queue
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Q. What data structure is commonly used to implement the priority queue in Dijkstra's algorithm?
A.
Array
B.
Linked List
C.
Binary Heap
D.
Stack
Show solution
Solution
A binary heap is commonly used to implement the priority queue in Dijkstra's algorithm, allowing efficient extraction of the minimum element.
Correct Answer:
C
— Binary Heap
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