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. What does CI/CD stand for in the context of MLOps?
A.
Continuous Integration/Continuous Deployment
B.
Cyclic Integration/Cyclic Deployment
C.
Constant Improvement/Constant Development
D.
Collaborative Integration/Collaborative Deployment
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Solution
CI/CD stands for Continuous Integration and Continuous Deployment, which are practices used to automate the deployment of machine learning models.
Correct Answer:
A
— Continuous Integration/Continuous Deployment
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Q. What does CNN stand for in the context of deep learning?
A.
Convolutional Neural Network
B.
Cyclic Neural Network
C.
Complex Neural Network
D.
Conditional Neural Network
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Solution
CNN stands for Convolutional Neural Network, which is a class of deep neural networks commonly used for analyzing visual imagery.
Correct Answer:
A
— Convolutional Neural Network
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Q. What does common subexpression elimination achieve?
A.
It reduces the number of variables
B.
It eliminates duplicate calculations
C.
It simplifies control flow
D.
It increases the number of function calls
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Solution
Common subexpression elimination achieves the elimination of duplicate calculations, thus optimizing the code.
Correct Answer:
B
— It eliminates duplicate calculations
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Q. What does cross-validation help to prevent?
A.
Overfitting
B.
Underfitting
C.
Data leakage
D.
Bias
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Solution
Cross-validation helps to prevent overfitting by ensuring that the model performs well on unseen data.
Correct Answer:
A
— Overfitting
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Q. What does dead code elimination refer to?
A.
Removing code that is never executed
B.
Optimizing loops
C.
Reducing variable declarations
D.
Simplifying expressions
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Solution
Dead code elimination refers to removing code that is never executed, which can help reduce the size of the code.
Correct Answer:
A
— Removing code that is never executed
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Q. What does HTTPS stand for?
A.
HyperText Transfer Protocol Secure
B.
HyperText Transfer Protocol Standard
C.
HyperText Transfer Protocol Service
D.
HyperText Transfer Protocol Socket
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Solution
HTTPS stands for HyperText Transfer Protocol Secure, which is the secure version of HTTP that uses encryption.
Correct Answer:
A
— HyperText Transfer Protocol Secure
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Q. What does it mean if a linear regression model has a p-value less than 0.05 for a predictor variable?
A.
The predictor is not statistically significant
B.
The predictor is statistically significant
C.
The model is overfitting
D.
The model has high bias
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Solution
A p-value less than 0.05 indicates that the predictor variable is statistically significant in predicting the dependent variable.
Correct Answer:
B
— The predictor is statistically significant
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Q. What does LR stand for in LR parsing?
A.
Left-to-right
B.
Right-to-left
C.
Left-to-right with lookahead
D.
Right-to-left with lookahead
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Solution
LR parsing stands for Left-to-right parsing with a rightmost derivation in reverse.
Correct Answer:
A
— Left-to-right
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Q. What does multicollinearity in linear regression refer to?
A.
High correlation between the dependent variable and independent variables
B.
High correlation among independent variables
C.
Low variance in the dependent variable
D.
Independence of errors
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Solution
Multicollinearity occurs when two or more independent variables in a regression model are highly correlated, which can affect the stability of coefficient estimates.
Correct Answer:
B
— High correlation among independent variables
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Q. What does NAT stand for in networking?
A.
Network Address Translation
B.
Network Access Technology
C.
Network Application Transfer
D.
Network Allocation Table
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Solution
NAT stands for Network Address Translation, a method used to remap one IP address space into another by modifying network address information.
Correct Answer:
A
— Network Address Translation
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Q. What does overfitting refer to in machine learning?
A.
A model that performs well on training data but poorly on unseen data
B.
A model that generalizes well to new data
C.
A model that is too simple for the data
D.
A model that has too few features
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Solution
Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize.
Correct Answer:
A
— A model that performs well on training data but poorly on unseen data
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Q. What does overfitting refer to in supervised learning?
A.
The model performs well on unseen data
B.
The model is too simple to capture the data patterns
C.
The model learns noise in the training data
D.
The model has high bias
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Solution
Overfitting occurs when a model learns noise in the training data, leading to poor performance on unseen data.
Correct Answer:
C
— The model learns noise in the training data
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Q. What does PCA stand for in the context of feature engineering?
A.
Partial Component Analysis
B.
Principal Component Analysis
C.
Predictive Component Analysis
D.
Probabilistic Component Analysis
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Solution
PCA stands for Principal Component Analysis, a technique used for dimensionality reduction.
Correct Answer:
B
— Principal Component Analysis
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Q. What does precision indicate in a classification task?
A.
The ratio of true positives to the sum of true positives and false negatives
B.
The ratio of true positives to the sum of true positives and false positives
C.
The ratio of true negatives to the sum of true negatives and false positives
D.
The overall correctness of the model
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Solution
Precision measures the accuracy of positive predictions, calculated as true positives divided by the sum of true positives and false positives.
Correct Answer:
B
— The ratio of true positives to the sum of true positives and false positives
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Q. What does precision indicate in a confusion matrix?
A.
The ratio of true positives to the total predicted positives
B.
The ratio of true positives to the total actual positives
C.
The overall correctness of the model
D.
The ability to identify all relevant instances
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Solution
Precision measures the accuracy of positive predictions, calculated as the number of true positives divided by the sum of true positives and false positives.
Correct Answer:
A
— The ratio of true positives to the total predicted positives
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Q. What does pruning refer to in the context of Decision Trees?
A.
Adding more nodes to the tree
B.
Removing nodes to reduce complexity
C.
Increasing the depth of the tree
D.
Changing the splitting criterion
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Solution
Pruning refers to the process of removing nodes from a Decision Tree to reduce its complexity and prevent overfitting.
Correct Answer:
B
— Removing nodes to reduce complexity
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Q. What does R-squared indicate in a linear regression analysis?
A.
The strength of the relationship between variables
B.
The proportion of variance in the dependent variable explained by the independent variables
C.
The average error of predictions
D.
The number of predictors in the model
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Solution
R-squared indicates the proportion of variance in the dependent variable that can be explained by the independent variables in the model.
Correct Answer:
B
— The proportion of variance in the dependent variable explained by the independent variables
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Q. What does R-squared indicate in a linear regression model?
A.
The strength of the relationship between the independent and dependent variables
B.
The proportion of variance in the dependent variable that can be explained by the independent variable(s)
C.
The average error of the predictions
D.
The number of predictors in the model
Show solution
Solution
R-squared measures the proportion of variance in the dependent variable that is explained by the independent variable(s) in the model.
Correct Answer:
B
— The proportion of variance in the dependent variable that can be explained by the independent variable(s)
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Q. What does R-squared measure in a linear regression model?
A.
The strength of the relationship between the independent and dependent variables
B.
The average error of the predictions
C.
The number of predictors in the model
D.
The slope of the regression line
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Solution
R-squared measures the proportion of variance in the dependent variable that can be explained by the independent variable(s) in the model.
Correct Answer:
A
— The strength of the relationship between the independent and dependent variables
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Q. What does recall measure in a classification model?
A.
The ratio of true positives to the total actual positives
B.
The ratio of true positives to the total predicted positives
C.
The ratio of true negatives to the total actual negatives
D.
The ratio of false negatives to the total actual positives
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Solution
Recall, also known as sensitivity, measures the ability of a model to find all the relevant cases (true positives).
Correct Answer:
A
— The ratio of true positives to the total actual positives
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Q. What does recall measure in a classification task?
A.
The ratio of true positives to the total actual positives
B.
The ratio of true positives to the total predicted positives
C.
The overall accuracy of the model
D.
The number of false negatives
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Solution
Recall, also known as sensitivity, measures the ability of a model to find all relevant cases (true positives) in the dataset.
Correct Answer:
A
— The ratio of true positives to the total actual positives
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Q. What does recursion mean in programming?
A.
A function calling itself
B.
A loop that iterates
C.
A data structure
D.
A variable declaration
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Solution
Recursion in programming refers to a function that calls itself to solve a problem.
Correct Answer:
A
— A function calling itself
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Q. What does RMSE stand for in evaluation metrics?
A.
Root Mean Square Error
B.
Relative Mean Square Error
C.
Root Mean Squared Estimation
D.
Relative Mean Squared Estimation
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Solution
RMSE stands for Root Mean Square Error, which measures the average magnitude of the errors between predicted and observed values.
Correct Answer:
A
— Root Mean Square Error
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Q. What does RMSE stand for in the context of evaluation metrics?
A.
Root Mean Square Error
B.
Relative Mean Square Error
C.
Random Mean Square Error
D.
Root Mean Squared Evaluation
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Solution
RMSE stands for Root Mean Square Error, which measures the average magnitude of the errors between predicted and observed values.
Correct Answer:
A
— Root Mean Square Error
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Q. What does RNN stand for in the context of neural networks?
A.
Recurrent Neural Network
B.
Radial Neural Network
C.
Recursive Neural Network
D.
Regularized Neural Network
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Solution
RNN stands for Recurrent Neural Network, which is designed to recognize patterns in sequences of data.
Correct Answer:
A
— Recurrent Neural Network
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Q. What does ROC AUC measure in a classification model?
A.
The area under the Receiver Operating Characteristic curve
B.
The average precision of the model
C.
The total number of true positives
D.
The mean error of predictions
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Solution
ROC AUC measures the area under the ROC curve, indicating the model's ability to distinguish between classes.
Correct Answer:
A
— The area under the Receiver Operating Characteristic curve
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Q. What does ROC AUC measure?
A.
The area under the Receiver Operating Characteristic curve
B.
The accuracy of the model
C.
The precision of the model
D.
The recall of the model
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Solution
ROC AUC measures the area under the Receiver Operating Characteristic curve, indicating the model's ability to distinguish between classes.
Correct Answer:
A
— The area under the Receiver Operating Characteristic curve
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Q. What does ROC AUC stand for in model evaluation?
A.
Receiver Operating Characteristic Area Under Curve
B.
Regression Output Curve Area Under Control
C.
Randomized Output Classification Area Under Curve
D.
Receiver Output Classification Area Under Control
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Solution
ROC AUC stands for Receiver Operating Characteristic Area Under Curve, measuring the model's ability to distinguish between classes.
Correct Answer:
A
— Receiver Operating Characteristic Area Under Curve
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Q. What does ROC stand for in the context of evaluation metrics?
A.
Receiver Operating Characteristic
B.
Randomized Output Curve
C.
Relative Operating Curve
D.
Receiver Output Classification
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Solution
ROC stands for Receiver Operating Characteristic, which is a graphical representation of a classifier's performance.
Correct Answer:
A
— Receiver Operating Characteristic
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Q. What does ROC stand for in the context of model evaluation?
A.
Receiver Operating Characteristic
B.
Receiver Output Curve
C.
Rate of Classification
D.
Random Output Curve
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Solution
ROC stands for Receiver Operating Characteristic, which is a graphical representation of a classifier's performance.
Correct Answer:
A
— Receiver Operating Characteristic
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