Practice Advanced Concepts of Modeling in AI Class 10 Extra Questions and Answers to strengthen your understanding of important AI modeling concepts. These extra questions are designed to help Class 10 students revise key topics, understand concepts clearly, and prepare effectively for school and board examinations.
Advanced Concepts of Modeling in AI Class 10 Extra Questions and Answers
Q. A company wants to detect manufacturing defects where examples of both normal and defective products are available. They should use :
a. Unsupervised learning
b. Reinforcement learning
c. Association rules
d. Supervised learning
Answer: d. Supervised learning
Q. In unsupervised learning, the machine’s primary task is to :
a. Predict specific outputs.
b. Identify patterns and relationships in unlabelled data.
c. Classify data into pre-defined categories.
d. Learn from rewards only.
Answer: b. Identify patterns and relationships in unlabelled data.
Q. A music streaming service groups songs by tempo and intensity to understand user preferences. This is an example of :
a. Supervised learning – Classification
b. Unsupervised learning – Clustering
c. Reinforcement learning
d. Supervised learning – Regression
Answer: b. Unsupervised learning – Clustering
Q. Which scenario best represents a regression problem ?
a. Identifying whether an email is spam.
b. Grouping customers by behaviour.
c. Predicting tomorrow’s temperature.
d. Recognizing faces in photos.
Answer: c. Predicting tomorrow’s temperature.
Q. Artificial Neural Networks are inspired by :
a. Computer circuits
b. Mathematical equations
c. Human brain and nervous system
d. Internet networks
Answer: c. Human brain and nervous system
Q. Which learning approach would be most suitable for training an AI model to park the car correctly ?
a. Supervised Learning
b. Unsupervised Learning
c. Transfer Learning
d. Reinforcement Learning
Answer: d. Reinforcement Learning
Q. An e-commerce platform analyzes customer purchase patterns to recommend “Customers who bought product X also bought product Y.” This uses :
a. Classification model
b. Regression model
c. Association model
d. Clustering model
Answer: c. Association model
Q. In supervised learning, what is the purpose of the testing dataset ?
a. To train the model.
b. To evaluate the model’s accuracy.
c. To create new features.
d. To label the data.
Answer: b. To evaluate the model’s accuracy.
Q. State True/False : Machine learning and Deep learning are part of Artificial Intelligence, but not everything that is Machine learning will be Deep learning.
Answer: Ture
Q. Explain the following with respect to Unsupervised Learning Models :
a. Clustering
b. Dimensionality Reduction
Answer:
- a. Clustering: Clustering is a method in unsupervised learning where data is grouped into clusters based on similarity. For example, grouping customers by shopping habits.
- b. Dimensionality Reduction: Dimensionality reduction reduces the number of features in data while keeping important information. It makes data easier to analyse and visualize. Example: compressing image data into fewer variables.
Q. How is reinforcement learning different from supervised and unsupervised learning?
Answer: Reinforcement learning is different because the machine learns by trial and error, receiving rewards or penalties for its actions. Supervised learning uses labelled data to train the model, while unsupervised learning works with unlabelled data to find hidden patterns. Reinforcement learning focuses on learning the best strategy through experience.
Q. a) What is the name of the learning model that works with unlabeled data to identify hidden patterns or structures in the data?
b) Name the two main categories of this learning model.
c) Explain each category briefly and provide one example for each.
Answer:
- a) The learning model that works with unlabelled data is Unsupervised Learning.
- b) The two main categories are Clustering and Association.
- c) Clustering groups similar data points together, like grouping songs by tempo. Association finds relationships between items, like “Customers who bought X also bought Y.”
Q. Differentiate between Kinesthetic Intelligence and Spatial Visual Intelligence.
Answer: Kinesthetic Intelligence is the ability to use body parts and limbs in a skilled manner, such as in dance or sports. Spatial Visual Intelligence is the ability to perceive the visual world and understand the relationship between objects, such as in drawing, designing, or navigation.
Q. Give any two characteristics of Artificial Neural Networks.
Answer:
- They are inspired by the human brain and nervous system.
- They can learn complex patterns and improve performance with more data.
Q. Provide one real-world application each of Supervised Learning and Unsupervised Learning
Answer:
- Supervised Learning: Email spam detection, where labelled data (spam vs not spam) is used.
- Unsupervised Learning: Market segmentation, where customers are grouped based on buying behaviour without labels.
Q. (a) Differentiate between Classification Model and Regression Model.
(b) Study the following real-world applications and identify whether each represents a Classification Model or a Regression Model :
- (i) Predicting a patient’s blood pressure reading will be 120/80 mmHg based on age, weight, and lifestyle factors.
- (ii) Forecasting that 2,847 units of a product will be sold next month based on past sales data and market trends.
- (iii) Determining if a patient has “diabetes” or “no diabetes” based on glucose levels and other medical parameters.
- (iv) Predicting that a company’s share price will be ` 1,250.75 tomorrow based on market indicators and company performance.
Answer: A Classification Model is used when the output is categorical, such as “yes/no” or “spam/not spam.” It divides data into predefined classes. A Regression Model is used when the output is continuous numerical values, such as predicting temperature or sales figures.
(b) Real‑world applications:
- (i) Predicting blood pressure reading → Regression Model
- (ii) Forecasting product sales → Regression Model
- (iii) Determining diabetes or no diabetes → Classification Model
- (iv) Predicting share price → Regression Model
Q. (a) Explain Reinforcement Learning with an example.
(b) A drawback of Rule Based Approach is that learning is static. Explain the given statement.
Answer:
- (a) Reinforcement Learning is a type of learning where the machine learns by trial and error, receiving rewards for correct actions and penalties for wrong ones. For example, training an AI to play chess — it gets a reward when it wins and a penalty when it loses, gradually learning the best moves.
- (b) In a rule‑based approach, the system follows fixed rules written by humans. Learning is static because the system cannot improve or adapt beyond those rules. It cannot learn new patterns automatically.
Q. Give two differences between Supervised and Unsupervised learning.
Answer:
- Supervised Learning uses labelled data (input with correct output), while Unsupervised Learning uses unlabelled data.
- Supervised Learning predicts specific outputs (classification or regression), while Unsupervised Learning finds hidden patterns or groups (clustering or association).
Q. Differentiate between Statistical Data and Computer Vision domains of AI with one example each.
Answer:
- Statistical Data Domain deals with collecting and analysing large datasets to find meaning. Example: Price comparison websites that compare product prices from different vendors.
- Computer Vision Domain deals with analysing and understanding visual information like images or videos. Example: Surveillance systems that detect suspicious activities in public spaces.
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