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. Which evaluation metric is commonly used to assess the performance of a Support Vector Machine model?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
Confusion Matrix
Show solution
Solution
Accuracy is a common evaluation metric for classification tasks, including those performed by Support Vector Machines.
Correct Answer:
B
— Accuracy
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Q. Which evaluation metric is commonly used to assess the performance of a Support Vector Machine?
A.
Accuracy
B.
Mean Squared Error
C.
Silhouette Score
D.
F1 Score
Show solution
Solution
Accuracy is a common evaluation metric used to assess the performance of classification models, including Support Vector Machines.
Correct Answer:
A
— Accuracy
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Q. Which evaluation metric is commonly used to assess the performance of an SVM model?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
Confusion Matrix
Show solution
Solution
Accuracy is a common metric used to evaluate the performance of classification models, including SVM.
Correct Answer:
B
— Accuracy
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Q. Which evaluation metric is commonly used to assess the performance of classification models in cloud ML services?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
R-squared
Show solution
Solution
Accuracy is a common evaluation metric for classification models, measuring the proportion of correct predictions.
Correct Answer:
B
— Accuracy
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Q. Which evaluation metric is commonly used to assess the performance of Decision Trees in classification tasks?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
R-squared
Show solution
Solution
Accuracy is a common metric for evaluating the performance of classification models like Decision Trees.
Correct Answer:
B
— Accuracy
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Q. Which evaluation metric is commonly used to assess the performance of Decision Trees?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
F1 Score
Show solution
Solution
Accuracy is a common metric for evaluating the performance of classification models like Decision Trees.
Correct Answer:
B
— Accuracy
Learn More →
Q. Which evaluation metric is commonly used to assess the quality of clustering results?
A.
Accuracy
B.
Silhouette score
C.
F1 score
D.
Mean squared error
Show solution
Solution
The Silhouette score is a popular metric for evaluating clustering quality, measuring how similar an object is to its own cluster compared to other clusters.
Correct Answer:
B
— Silhouette score
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Q. Which evaluation metric is commonly used to assess the quality of clustering?
A.
Accuracy
B.
Silhouette score
C.
F1 score
D.
Mean squared error
Show solution
Solution
The Silhouette score is a popular metric for evaluating clustering quality, measuring how similar an object is to its own cluster compared to other clusters.
Correct Answer:
B
— Silhouette score
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Q. Which evaluation metric is commonly used to assess the quality of embeddings?
A.
Accuracy
B.
F1 Score
C.
Cosine Similarity
D.
Mean Squared Error
Show solution
Solution
Cosine similarity is commonly used to assess the quality of embeddings by measuring the angle between two vectors.
Correct Answer:
C
— Cosine Similarity
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Q. Which evaluation metric is most appropriate for a binary classification problem with imbalanced classes?
A.
Accuracy
B.
F1 Score
C.
Mean Squared Error
D.
R-squared
Show solution
Solution
F1 Score is a better evaluation metric for imbalanced classes as it considers both precision and recall.
Correct Answer:
B
— F1 Score
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Q. Which evaluation metric is most appropriate for a binary classification problem?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
R-squared
Show solution
Solution
Accuracy is a common evaluation metric for binary classification problems, measuring the proportion of correct predictions.
Correct Answer:
B
— Accuracy
Learn More →
Q. Which evaluation metric is most appropriate for a model predicting rare events?
A.
Accuracy
B.
Recall
C.
F1 Score
D.
Mean Squared Error
Show solution
Solution
Recall is crucial for rare event prediction as it focuses on capturing as many positive instances as possible.
Correct Answer:
B
— Recall
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Q. Which evaluation metric is most appropriate for a multi-class classification problem?
A.
Accuracy
B.
F1 Score
C.
Log Loss
D.
All of the above
Show solution
Solution
All of these metrics can be used to evaluate multi-class classification problems, depending on the specific context and requirements.
Correct Answer:
D
— All of the above
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Q. Which evaluation metric is most appropriate for a regression model predicting house prices?
A.
Accuracy
B.
F1 Score
C.
Mean Absolute Error
D.
Precision
Show solution
Solution
Mean Absolute Error (MAE) is most appropriate for regression models predicting continuous values like house prices.
Correct Answer:
C
— Mean Absolute Error
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Q. Which evaluation metric is most appropriate for a regression model?
A.
Accuracy
B.
F1 Score
C.
Mean Absolute Error
D.
Confusion Matrix
Show solution
Solution
Mean Absolute Error (MAE) is a common evaluation metric used to assess the performance of regression models.
Correct Answer:
C
— Mean Absolute Error
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Q. Which evaluation metric is most appropriate for a regression problem?
A.
Accuracy
B.
F1 Score
C.
Mean Absolute Error
D.
Confusion Matrix
Show solution
Solution
Mean Absolute Error (MAE) is a common evaluation metric for regression problems, measuring the average magnitude of errors in a set of predictions.
Correct Answer:
C
— Mean Absolute Error
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Q. Which evaluation metric is most appropriate for assessing a model deployed for a binary classification task?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
R-squared
Show solution
Solution
Accuracy is a common evaluation metric for binary classification tasks, as it measures the proportion of correct predictions made by the model.
Correct Answer:
B
— Accuracy
Learn More →
Q. Which evaluation metric is most appropriate for assessing the performance of a Decision Tree on a binary classification problem?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
R-squared
Show solution
Solution
Accuracy is a common metric for evaluating the performance of classification models, including Decision Trees.
Correct Answer:
B
— Accuracy
Learn More →
Q. Which evaluation metric is most appropriate for assessing the performance of a Decision Tree on an imbalanced dataset?
A.
Accuracy
B.
F1 Score
C.
Mean Squared Error
D.
R-squared
Show solution
Solution
F1 Score is more appropriate for imbalanced datasets as it considers both precision and recall, providing a better measure of the model's performance.
Correct Answer:
B
— F1 Score
Learn More →
Q. Which evaluation metric is most appropriate for assessing the performance of a linear regression model?
A.
Accuracy
B.
F1 Score
C.
Mean Absolute Error (MAE)
D.
Confusion Matrix
Show solution
Solution
Mean Absolute Error (MAE) is a suitable metric for evaluating the performance of a linear regression model.
Correct Answer:
C
— Mean Absolute Error (MAE)
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Q. Which evaluation metric is most appropriate for assessing the performance of an SVM classifier?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
Adjusted Rand Index
Show solution
Solution
Accuracy is a common evaluation metric for classification tasks, including those performed by SVM classifiers.
Correct Answer:
B
— Accuracy
Learn More →
Q. Which evaluation metric is most appropriate for assessing the performance of an SVM model on an imbalanced dataset?
A.
Accuracy
B.
Precision
C.
Recall
D.
F1 Score
Show solution
Solution
The F1 Score is a better evaluation metric for imbalanced datasets as it considers both precision and recall.
Correct Answer:
D
— F1 Score
Learn More →
Q. Which evaluation metric is most appropriate for assessing the performance of an SVM model in a binary classification task?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
Adjusted R-squared
Show solution
Solution
Accuracy is a common evaluation metric for binary classification tasks, measuring the proportion of correctly classified instances.
Correct Answer:
B
— Accuracy
Learn More →
Q. Which evaluation metric is most appropriate for assessing the performance of an SVM model on imbalanced datasets?
A.
Accuracy
B.
Precision
C.
Recall
D.
F1 Score
Show solution
Solution
The F1 Score is a better evaluation metric for imbalanced datasets as it considers both precision and recall, providing a balance between the two.
Correct Answer:
D
— F1 Score
Learn More →
Q. Which evaluation metric is most appropriate for assessing the performance of an SVM model?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
Confusion Matrix
Show solution
Solution
Accuracy is a common evaluation metric for classification tasks, including those performed by SVM models.
Correct Answer:
B
— Accuracy
Learn More →
Q. Which evaluation metric is most appropriate for imbalanced classification problems?
A.
Accuracy
B.
F1 Score
C.
Mean Squared Error
D.
R-squared
Show solution
Solution
The F1 Score is a better evaluation metric for imbalanced classification problems as it considers both precision and recall, providing a balance between the two.
Correct Answer:
B
— F1 Score
Learn More →
Q. Which evaluation metric is most appropriate for regression models during deployment?
A.
Accuracy
B.
F1 Score
C.
Mean Absolute Error (MAE)
D.
Confusion Matrix
Show solution
Solution
Mean Absolute Error (MAE) is a suitable evaluation metric for regression models, as it measures the average magnitude of errors in predictions.
Correct Answer:
C
— Mean Absolute Error (MAE)
Learn More →
Q. Which evaluation metric is most appropriate for regression tasks?
A.
Accuracy
B.
Mean Absolute Error (MAE)
C.
F1 Score
D.
Precision
Show solution
Solution
Mean Absolute Error (MAE) is commonly used for evaluating regression tasks as it measures the average magnitude of errors.
Correct Answer:
B
— Mean Absolute Error (MAE)
Learn More →
Q. Which evaluation metric is most sensitive to class imbalance?
A.
Accuracy
B.
Precision
C.
Recall
D.
F1 Score
Show solution
Solution
Accuracy can be misleading in imbalanced datasets, as it may give a false sense of model performance by favoring the majority class.
Correct Answer:
A
— Accuracy
Learn More →
Q. Which evaluation metric is most suitable for assessing clustering performance?
A.
Accuracy
B.
F1 Score
C.
Adjusted Rand Index
D.
Mean Absolute Error
Show solution
Solution
The Adjusted Rand Index measures the similarity between two data clusterings, making it suitable for evaluating clustering performance.
Correct Answer:
C
— Adjusted Rand Index
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