Unit 2: Advanced Concept of AI Modeling Class 10 Question Answer
Unit 2: Advanced Concept of AI Modeling Class 10 Question Answer
Students looking for Unit 2 Advanced Concept of AI Modeling Class 10 Question Answer will find complete solutions in this article. These Class 10 AI Chapter 2 Question Answers cover all important topics including Machine Learning, Neural Networks, Classification, Regression, Clustering, and Perceptron. Along with detailed explanations, students can also use these Advanced Concept of AI Modeling MCQ questions for exam preparation and self-assessment. These Class 10 Artificial Intelligence Unit 2 Notes are designed according to the latest CBSE curriculum and are helpful for revision, assignments, practical exams, and board exam preparation. If you are searching for CBSE AI Class 10 Question Answers, this guide provides easy-to-understand answers and important concepts in one place.
Key Points for Exam Revision
• Supervised Learning uses labeled data.
• Unsupervised Learning uses unlabeled data.
• Reinforcement Learning uses rewards and penalties.
• Classification predicts categories.
• Regression predicts numerical values.
• Clustering groups similar data points.
• Neural Networks are inspired by the human brain.
• Perceptron make decisions using inputs, weights, and thresholds.
Summary of detailed classification of ML models
Choose the most appropriate answer for each question.
Q1. In which type of machine learning is the data labeled with the desired output?
a)Supervised Learning
b)Unsupervised Learning
c)Reinforcement Learning
d)Deep Learning
Q2. An email spam filter that learns to identify spam emails based on labeled examples is an application of:
a)Supervised Learning
b)Unsupervised Learning
c)Reinforcement Learning
d)Transfer Learning
Q3. A machine learning algorithm that groups similar customer purchases into clusters for recommendation systems uses:
a)Supervised Learning
b)Unsupervised Learning
c)Reinforcement Learning
d)Neural Networks
Q4. An AI agent playing a game and learning from its rewards and penalties is an example of:
a)Supervised Learning
b)Unsupervised Learning
c)Reinforcement Learning
d)Evolutionary Learning
Q5. Which of the following statements is NOT true about supervised learning?
a)Requires labeled data for training.
b)Used for classification and regression tasks.
c)Can be less efficient for large datasets.
d)Often used in image recognition applications.
Q6. In an unsupervised learning scenario, the goal is to:
a)Predict a specific output based on labeled data.
b)Identify patterns and relationships within unlabeled data.
c)Train an AI agent through rewards and penalties.
d)Develop complex neural network architectures.
Q7. Clustering algorithms are commonly used in unsupervised learning for:
a)Spam filtering
b)Image classification
c)Stock price prediction
d)Grouping similar data points
Q8. Reinforcement learning is particularly useful for scenarios where:
a)Large amounts of labeled data are available.
b)The desired outcome is clear, but the path to achieve it is unknown.
c)The data is structured and easily categorized.
d)The task requires reasoning and logical deduction.
Q9. Imagine an AI playing a game and learning to win by trial and error. This is an example of:
a)Supervised Learning
b)Unsupervised Learning
c)Reinforcement Learning
d)Natural Language Processing
Q10. Artificial neural networks are inspired by the structure and function of:
a)The human brain
b)Quantum computers
c)Complex mathematical models
d)High-speed processors
Q11. The process of adjusting the weights in a neural network to improve performance is called:
a)Activation
b)Learning
c)Optimization
d)Training
Q12. A neural network with multiple layers of interconnected neurons is called a:
a)Single-layer network
b)Deep Neural Network
c)Linear network
d)Perceptron
Q13. Neural networks are particularly well-suited for tasks involving:
a)Simple calculations and mathematical operations
b)Recognizing patterns in complex data like images and text
c)Performing logical deductions and reasoning tasks
d)Storing and retrieving large amounts of information
Q14. Training a neural network often requires:
a)A small set of labeled data samples
b)A significant amount of data and computational resources
c)A specific set of programming instructions
d)A human expert to guide the learning process
Assertion and reasoning-based questions:
Q1. Assertion: Unsupervised Learning is a type of learning without any guidance.
Reasoning: Unsupervised learning models work on unlabeled datasets, where the data fed into the machine is random and the person training the model may not have any prior information about it.
Options:
(a) Both A and R are true and R is the correct explanation for A
(b) Both A and R are true and R is not the correct explanation for A
(c) A is True but R is False
(d) A is false but R is True
Q2. Assertion (A): Information processing in a neural network relies on weights and biases assigned to nodes.
Reasoning (R): These weights and biases determine how strongly a node is influenced by its inputs and its overall contribution to the next layer.
Options:
(a) Both A and R are true and R is the correct explanation for A
(b) Both A and R are true and R is not the correct explanation for A
(c) A is True but R is False
(d) A is false but R is True
Answer the following questions:
Q1. Give difference between rule based and learning based AI models.
Q2. What is supervised, unsupervised and reinforcement learning? Explain with examples.
Q3. What is clustering and how is it different from classification?
Q4. Explain neural networks. Also give functions of three layers of neural networks.
Q5. Differentiate between classification and regression model.
Q6. What is neural network? Give the functioning of its three layers?
Q7. Identify the type of learning (supervised, unsupervised, reinforcement learning) are the following case studies most likely based on?
a) Case Study 1:
A company wants to predict customer churn based on past purchasing behavior, demographics, and customer interactions. They have a dataset with labeled examples of customers who churned and those who did not.
b) Case Study 2:
A social media platform wants to group users based on their interests and behavior to recommend relevant content. They have a large dataset of user interactions but no predefined categories. Which type of learning is this case study most likely based on?
c) Case Study 3:
An autonomous vehicle is learning to navigate through a city environment. It receives feedback in the form of rewards for reaching its destination safely and penalties for traffic violations. Which type of learning is this case study most likely based on?
d)Case Study 4:
A healthcare provider wants to identify patterns in patient data to personalize treatment plans. They have a dataset with various patient attributes but no predefined labels indicating specific treatment plans. Which type of learning is this case study most likely based on?
e)Case Study 5:
A manufacturing company wants to optimize its production process by detecting anomalies in sensor data from machinery. They have a dataset with examples of normal and anomalous behavior. Which type of learning is this case study most likely based on?
Q8. Identify the type of model (classification, regression, clustering, association model) are the following case studies most likely based on?
a) A bank wants to predict whether a loan applicant will “default” or “non-default” on their loan payments. They have a dataset containing information such as income, credit score, loan amount, and employment status.
b) A real estate agency wants to predict the selling price of houses based on various features such as size, location, number of bedrooms, and bathrooms. They have a dataset containing historical sales data.
c) A marketing company wants to segment its customer base into distinct groups based on purchasing behavior for targeted marketing campaigns. They have a dataset containing information such as purchase history, frequency of purchases, and amount spent.
d) A grocery store wants to identify associations between different products purchased by customers to understand which products are commonly bought together. They have a transaction dataset containing records of items purchased together during each transaction.
Q9. A healthcare provider wants to improve patient care by predicting the length of hospital stays for different medical conditions. They have a dataset containing patient demographics, medical history, and treatment details. The task involves:
a)To predict whether a patient will have a short or long hospital stay.
b)To predict the number of days a patient will stay in the hospital.
c)To segment patients into groups with similar characteristics for personalized treatment plans.
d)To identify patterns in patient treatments and outcomes.
Identify the type of model (classification, regression, clustering, and association model) in the above tasks.
Q10. Convert the following scenarios to perceptron:
a) Context: A manager is deciding whether to approve a work-from-home request from an employee.
Factors:
Does the employee perform well when working remotely?
Are there any upcoming team meetings or collaborative projects?
Does the company’s policy support remote work?
Is it beneficial for both the employee and the company?
b)Context: A homeowner is deciding whether to invest in solar panels for their house.
Factors:
Do I have a sufficient average amount of sunlight in my area?
Are there any available incentives or rebates for installing solar panels?
Does installing solar panels impact the value of my home?
Does solar energy lead to environmental benefits?
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Conclusion
In this article, we covered Unit 2: Advanced Concept of AI Modeling Class 10 Question Answers in an easy format. These include MCQs, assertion-reasoning questions, case studies, neural networks, machine learning concepts, and perceptron-based questions. Students can use these Class 10 Artificial Intelligence Unit 2 Notes for quick revision, exam preparation, and better understanding of important AI concepts.
Unit 2: Advanced Concept of AI Modeling Class 10 Question Answer
FAQs
What is supervised learning?
Supervised learning is a machine learning approach where the model learns from labeled data to predict outputs for new data.
What is the difference between classification and regression?
Classification predicts categories, while regression predicts continuous numerical values.
What is a perceptron?
A perceptron is a simple neural network model that makes decisions based on weighted inputs and a threshold
How many layers does a neural network have?
Neural network have 3 layers
Name the three layers of neural network.
Input layer, Hidden layer, Output layer
What is Machine Learning?
Machine Learning is a branch of AI that enables computers to learn from data without being explicitly programmed.
What is Reinforcement Learning?
Reinforcement learning is a type of learning in which a machine learns to perform a task through a repeated trial-and-error method
Important links of Class X (Artificial Intelligence)
Chapter 1 Introduction to AI MCQ
Chapter 1 Introduction to AI Class 10 NOTES
Chapter 2 AI Project Cycle MCQ
Chapter 3 Natural Language Processing MCQ
Important links of Class IX (IT-402)
Unit 1 : Introduction to IT–ITeS Industry BOOK SOLUTIONS
Unit 1 : Introduction to IT–ITeS Industry NOTES
Unit 1 : Introduction to IT-ITeS MCQ
Unit 3 : Digital Documentation NOTES
Unit 3 : Digital Documentation BOOK SOLUTIONS
Unit 3 : Digital Documentation MCQ
Unit 4 : Electronic Spreadsheet BOOK SOLUTIONS
Unit 4 : Electronic Spreadsheet MCQ
Unit 5 : Digital Presentation MCQ
Important links of Class X (IT – 402)
Unit 1: Digital Documentation (Advanced) using LibreOffice Writer
Chapter 1. Introduction to Styles – NOTES
Chapter 1. Introduction to Styles – Question Answers
Chapter 2. Working with Images – NOTES
Chapter 2. Working with Images – Question Answers
Chapter 3. Advanced features of Writer – NOTES
Chapter 3. Advanced features of Writer – Question Answers
Unit 2: Electronic Spreadsheet (Advanced) using LibreOffice Calc
Chapter 4. Analyse Data using Scenarios and Goal Seek – NOTES
Chapter 4. Analyse Data using Scenarios and Goal Seek – Question Answers
Chapter 5. Using Macros in Spreadsheet – NOTES
Chapter 5. Using Macros in Spreadsheet – Question Answers
Chapter 6. Linking Spreadsheet Data – NOTES
Chapter 6. Linking Spreadsheet Data – Question Answers
Chapter 7. Share and Review a Spreadsheet – NOTES
Chapter 7. Share and Review a Spreadsheet – Question Answers
Unit 3: Database Management system using LibreOffice Base
Chapter 8. Introduction to DBMS – NOTES
Chapter 8. Introduction to DBMS – Question Answers
Chapter 9. Starting with LibreOffice Base – NOTES
Chapter 9. Starting with LibreOffice Base – Question Answers
Chapter 10. Working with Multiples Tables – NOTES
Chapter 10. Working with Multiples Tables – Question Answers
Chapter 11. Queries in LibreOffice Base – NOTES
Chapter 11. Queries in LibreOffice Base – Question Answers
Chapter 12. Forms and Reports – NOTES
Chapter 12. Forms and Reports – Question Answers
Unit 4: Prevent Accident and Emergencies
Chapter 13. Health, Safety and Security at Workplace – NOTES
Chapter 13. Health, Safety and Security at Workplace – Question Answers
Chapter 14. Workplace Safety Measures – NOTES
Chapter 14. Workplace Safety Measures – Question Answers
Chapter 15. Prevent Accidents and Emergencies – NOTES
Chapter 15. Prevent Accidents and Emergencies – Question Answers
Important links of Class X (IT – 402)
UNIT 1: DIGITAL DOCUMENTATION (ADVANCED) MCQ
UNIT-2: ELECTRONIC SPREADSHEET (ADVANCED) MCQ
UNIT-3 RELATIONAL DATABASE MANAGEMENT SYSTEMS (BASIC) MCQ
UNIT-4 WEB APPLICATIONS AND SECURITY MCQ