Class 10 AI Chapter - Modelling in AI Topic - Assertion/Reasoning - Arvindzeclass

Class 10 Artificial Intelligent Code 417 Solutions

Session 2025-26

Artificial Intelligence code 417 syllabus pdf class 10 solutions. Class 10 AI Book. Part - A, Part - B, and Python. This article provides complete solution for class 10 AI (Artificial Intelligence) Code - 417 solution according to new syllabus 2025 – 26. In this article first of all go through the AI Code - 417 syllabus and then follow chapter wise notes. Class 10 AI MCQs Quiz with answers.

Part A - Employability Skills Question/Answers

Chapter 1 -Communication Skills - II

Chapter 2 -Self - Management Skills - II

Chapter 3 - ICT Skills - II

Chapter 4 - Entrepreneurial Skills - II

Chapter 5 - Green Skills - II

Part B - Subject Specific Skills Question/Answers

Chapter 1 -AI Project Cycle & Ethics

Chapter 2 - Modelling in AI

Chapter 3 - Evaluating Models

Chapter 4 - Statistical Data

Chapter 5 - Computer Vision

Chapter 6 - Natural Language Processing

Chapter 7 - Advance Python

Assertion and Reasoning Questions with Answers

Q A.
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
Answer: (a)

Q B.
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.
Answer: (a)

Q1.
Assertion (A): Unsupervised Learning is a type of learning without any guidance.
Reasoning (R): 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)/(b)/(c)/(d)
Answer: (a)

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)/(b)/(c)/(d)
Answer: (a)

Q3.
Assertion (A): Supervised Learning requires labeled data for training.
Reasoning (R): Labeled data helps the model learn the relationship between inputs and their correct outputs.
Options: (a)/(b)/(c)/(d)
Answer: (a)

Q4.
Assertion (A): Reinforcement Learning is based on the concept of agents receiving only penalties.
Reasoning (R): In reinforcement learning, agents receive both rewards and penalties depending on their actions.
Options: (a)/(b)/(c)/(d)
Answer: (d)

Q5.
Assertion (A): Artificial Intelligence is a broad field that aims to create machines capable of simulating human intelligence.
Reasoning (R): Domains of AI include learning, reasoning, problem-solving, perception, and natural language processing.
Options: (a)/(b)/(c)/(d)
Answer: (a)

Q6.
Assertion (A): In supervised learning, regression is used to predict categorical values.
Reasoning (R): Regression predicts continuous values such as temperature, price, or age.
Options: (a)/(b)/(c)/(d)
Answer: (c)

Q7.
Assertion (A): Classification is a supervised learning task.
Reasoning (R): In classification, the model learns from labeled data to predict discrete categories.
Options: (a)/(b)/(c)/(d)
Answer: (a)

Q8.
Assertion (A): Clustering is used to divide data into groups where data points in the same group are more similar to each other than to those in other groups.
Reasoning (R): Clustering is an example of unsupervised learning.
Options: (a)/(b)/(c)/(d)
Answer: (a)

Q9.
Assertion (A): Association rule learning is used to find hidden relationships in data.
Reasoning (R): Market Basket Analysis is a popular application of association rules.
Options: (a)/(b)/(c)/(d)
Answer: (a)

Q10.
Assertion (A): Artificial Neural Networks (ANNs) are inspired by the biological neural networks in the human brain.
Reasoning (R): ANNs consist of interconnected nodes (neurons) that process information in layers.
Options: (a)/(b)/(c)/(d)
Answer: (a)

Q11.
Assertion (A): CNNs are mainly used for sequence prediction tasks like stock market forecasting.
Reasoning (R): CNNs are more suitable for image recognition because they extract spatial features.
Options: (a)/(b)/(c)/(d)
Answer: (c)

Q12.
Assertion (A): In reinforcement learning, the environment provides feedback to the agent.
Reasoning (R): This feedback helps the agent improve its policy to maximize rewards.
Options: (a)/(b)/(c)/(d)
Answer: (a)

Q13.
Assertion (A): Regression and classification are types of unsupervised learning.
Reasoning (R): Both require labeled data, so they are actually supervised learning methods.
Options: (a)/(b)/(c)/(d)
Answer: (d)

Q14.
Assertion (A): AI in the domain of perception includes computer vision and speech recognition.
Reasoning (R): These technologies allow machines to interpret and understand sensory data.
Options: (a)/(b)/(c)/(d)
Answer: (a)

Q15.
Assertion (A): In ANN, activation functions are used to introduce non-linearity.
Reasoning (R): Without activation functions, the neural network would behave like a simple linear model.
Options: (a)/(b)/(c)/(d)
Answer: (a)

Q16.
Assertion (A): Overfitting is a situation where the model performs well on training data but poorly on unseen data.
Reasoning (R): Overfitting happens when the model captures noise along with actual patterns.
Options: (a)/(b)/(c)/(d)
Answer: (a)

Q17.
Assertion (A): In clustering, the number of groups (clusters) is always known in advance.
Reasoning (R): Algorithms like K-Means require specifying the number of clusters beforehand, but other methods like DBSCAN do not.
Options: (a)/(b)/(c)/(d)
Answer: (b)

Q18.
Assertion (A): Supervised learning is useful when historical data with correct answers is available.
Reasoning (R): It uses these past examples to make predictions about future or unseen data.
Options: (a)/(b)/(c)/(d)
Answer: (a)

Q19.
Assertion (A): Association rules are only applicable in e-commerce.
Reasoning (R): Association rules can be applied in healthcare, bioinformatics, and other fields as well.
Options: (a)/(b)/(c)/(d)
Answer: (d)

Q20.
Assertion (A): CNNs use pooling layers to reduce the dimensionality of feature maps.
Reasoning (R): Pooling layers help in reducing computation while preserving important spatial features.
Options: (a)/(b)/(c)/(d)
Answer: (a)