Computer Science & IT

Download Q&A

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 the 'C' parameter in SVM control?
  • A. The number of support vectors
  • B. The trade-off between maximizing the margin and minimizing classification error
  • C. The complexity of the kernel function
  • D. The learning rate of the model
Q. What does the 'GET' method in HTTP do?
  • A. Sends data to the server
  • B. Requests data from the server
  • C. Deletes data on the server
  • D. Updates data on the server
Q. What does the 'K' in K-means represent?
  • A. The number of iterations the algorithm runs
  • B. The number of clusters to form
  • C. The number of features in the dataset
  • D. The distance metric used
Q. What does the 'malloc' function do in C?
  • A. Allocates memory on the stack
  • B. Allocates memory on the heap
  • C. Frees allocated memory
  • D. Initializes a pointer
Q. What does the 'reduce' action do in an LR parser?
  • A. It shifts the next input symbol onto the stack.
  • B. It pops symbols from the stack and replaces them with a non-terminal.
  • C. It accepts the input string.
  • D. It generates an error.
Q. What does the acronym 'NAT' stand for in networking?
  • A. Network Address Translation
  • B. Network Access Technology
  • C. Network Application Transfer
  • D. Network Allocation Table
Q. What does the acronym 'TCP' stand for?
  • A. Transmission Control Protocol
  • B. Transfer Control Protocol
  • C. Transport Communication Protocol
  • D. Transmission Communication Protocol
Q. What does the acronym URI stand for in the context of web protocols?
  • A. Uniform Resource Identifier
  • B. Universal Resource Indicator
  • C. Uniform Resource Interface
  • D. Universal Resource Identifier
Q. What does the acronym URL stand for?
  • A. Uniform Resource Locator
  • B. Universal Resource Link
  • C. Uniform Resource Link
  • D. Universal Resource Locator
Q. What does the area under the ROC curve (AUC) represent?
  • A. The probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance
  • B. The overall accuracy of the model
  • C. The precision of the model
  • D. The recall of the model
Q. What does the Area Under the ROC Curve (AUC-ROC) represent?
  • A. Model accuracy
  • B. Probability of false positives
  • C. Trade-off between sensitivity and specificity
  • D. Model complexity
Q. What does the AUC represent in the context of the ROC curve?
  • A. The area under the curve, indicating the model's ability to distinguish between classes
  • B. The average of the true positive rates
  • C. The total number of false positives
  • D. The accuracy of the model
Q. What does the coefficient in a linear regression model represent?
  • A. The strength of the relationship between variables
  • B. The predicted value of the dependent variable
  • C. The error in predictions
  • D. The number of features in the model
Q. What does the F1 Score evaluate in a classification model?
  • A. The balance between precision and recall
  • B. The overall accuracy of the model
  • C. The speed of the model
  • D. The number of false positives
Q. What does the F1 score represent in model evaluation?
  • A. The harmonic mean of precision and recall
  • B. The average of precision and recall
  • C. The ratio of true positives to total predicted positives
  • D. The ratio of true positives to total actual positives
Q. What does the Gini impurity measure in a Decision Tree?
  • 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.
Q. What does the Gini impurity measure in Decision Trees?
  • A. The accuracy of the model.
  • B. The purity of a node in the tree.
  • C. The depth of the tree.
  • D. The number of features used.
Q. What does the parameter 'C' control in SVM?
  • A. The complexity of the model
  • B. The margin width
  • C. The number of support vectors
  • D. The learning rate
Q. What does the parameter 'C' in SVM control?
  • A. The complexity of the model
  • B. The margin of the hyperplane
  • C. The number of support vectors
  • D. The learning rate
Q. What does the R-squared value indicate in a linear regression model?
  • A. The proportion of variance explained by the model
  • B. The slope of the regression line
  • C. The number of predictors in the model
  • D. The correlation between independent variables
Q. What does the ROC curve represent in classification problems?
  • A. The relationship between precision and recall
  • B. The trade-off between true positive rate and false positive rate
  • C. The accuracy of the model over different thresholds
  • D. The distribution of predicted probabilities
Q. What does the ROC curve represent in model evaluation?
  • A. Relationship between precision and recall
  • B. Trade-off between true positive rate and false positive rate
  • C. Model training time vs accuracy
  • D. Data distribution visualization
Q. What does the ROC curve represent?
  • A. Relationship between precision and recall
  • B. Trade-off between true positive rate and false positive rate
  • C. Model training time vs accuracy
  • D. Data distribution visualization
Q. What does the silhouette score measure in clustering?
  • A. The accuracy of predictions
  • B. The compactness and separation of clusters
  • C. The number of clusters
  • D. The speed of the algorithm
Q. What does the status code 404 indicate in an HTTP response?
  • A. OK
  • B. Created
  • C. Not Found
  • D. Bad Request
Q. What does the status code 404 indicate in HTTP?
  • A. OK
  • B. Created
  • C. Not Found
  • D. Bad Request
Q. What does the term 'AUC' stand for in the context of ROC analysis?
  • A. Area Under the Curve
  • B. Average Utility Coefficient
  • C. Algorithmic Uncertainty Coefficient
  • D. Area Under Classification
Q. What does the term 'backpropagation' refer to in neural networks?
  • A. The process of forward propagation of inputs
  • B. The method of updating weights based on error
  • C. The initialization of network parameters
  • D. The evaluation of model performance
Q. What does the term 'bagging' refer to in the context of Random Forests?
  • A. Using a single Decision Tree for predictions
  • B. Combining predictions from multiple models
  • C. Randomly selecting features for each tree
  • D. Aggregating predictions by averaging
Q. What does the term 'centroid' refer to in K-Means clustering?
  • A. The point that represents the center of a cluster
  • B. The maximum distance between points in a cluster
  • C. The average distance of points from the origin
  • D. The total number of clusters formed
Showing 751 to 780 of 3237 (108 Pages)
Soulshift Feedback ×

On a scale of 0–10, how likely are you to recommend The Soulshift Academy?

Not likely Very likely