Advanced Concepts of Modeling in AI MCQ – Class 10 AI (417)
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This Advanced Concepts of Modeling in AI MCQ is designed to help you practice everything you need for your CBSE board exam. Each question is carefully prepared based on the latest syllabus, previous years’ question patterns, and important exam topics. By solving these MCQs, you’ll strengthen your concepts, revise the chapter quickly, and improve your chances of scoring high in the exam.
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MULTIPLE CHOICE QUESTIONS (MCQs) – – Advance Concept of Modeling
Q1. Artificial Intelligence (AI) refers to:
a) Programming only robots
b) A technique that enables computers to mimic human intelligence
c) Software used only for gaming
d) A programming language
Q2. Machine Learning enables machines to:
a) Work only with images
b) Improve at tasks with experience
c) Store unlimited data
d) Replace humans completely
Q3. Deep Learning trains software using:
a) Small amounts of data only
b) Rules written by developers only
c) Vast amounts of data
d) No data
Q4. Which is the correct hierarchy?
a) ML → AI → DL
b) DL → AI → ML
c) AI → ML → DL
d) DL → ML → AI
Q5. Artificial Intelligence is known as the ______ term.
a) Smallest
b) Umbrella
c) Weakest
d) Fastest
Q6. Deep Learning is a subset of:
a) AI only
b) Machine Learning
c) Robotics
d) Programming
Q7. Which of the following is an example of Machine Learning?
a) Object Classification
b) Word Processing
c) File Compression
d) Printing
Q8. Which ML application helps detect unusual patterns in data?
a) Regression
b) Anomaly Detection
c) Classification
d) Segmentation
Q9. In Deep Learning, the input image is analysed using:
a) Database
b) Artificial Neural Network (ANN)
c) Spreadsheet
d) Compiler
Q10. Digit Recognition is an example of:
a) Machine Translation
b) Deep Learning
c) Word Processing
d) Database Management
Q11. Data is:
a) Information in any form
b) Only numerical values
c) Only text
d) Only images
Q12. Columns in a dataset are called:
a) Records
b) Features
c) Labels
d) Rows
Q13. Data Labeling means:
a) Deleting data
b) Compressing data
c) Attaching meaning to data
d) Encrypting data
Q14. Data having tags attached is called:
a) Raw Data
b) Unlabeled Data
c) Labeled Data
d) Mixed Data
Q15. The raw form of data without any tags is called:
a) Structured Data
b) Labeled Data
c) Unlabeled Data
d) Filtered Data
Q16. The training dataset is mainly used to:
a) Delete errors
b) Teach the AI model
c) Store outputs
d) Reduce memory
Q17. The testing dataset is mainly used to:
a) Train the model
b) Increase storage
c) Test the accuracy of the model
d) Remove labels
Q18. AI Modelling refers to:
a) Designing websites
b) Developing algorithms that produce intelligent outputs
c) Drawing flowcharts
d) Creating databases
Q19. Which of the following is NOT an AI modelling approach?
a) Rule-Based Approach
b) Learning-Based Approach
c) Manual Typing Approach
d) None of these
Q20. In a Rule-Based Approach, the relationship between data is:
a) Learned automatically by the machine
b) Defined by the developer
c) Randomly generated
d) Ignored
Q21. Rule-based chatbots mainly work using:
a) Neural Networks
b) Decision Trees and predefined rules
c) Regression Models
d) Reinforcement Learning
Q22. A rule-based chatbot on a shopping website is commonly used for:
a) Predicting weather
b) Image recognition
c) Answering FAQs
d) Playing games
Q23. One limitation of a Rule-Based AI model is that it:
a) Learns continuously
b) Cannot improve using feedback after training
c) Requires no rules
d) Always gives perfect answers
Q24. A Learning-Based AI model:
a) Never changes once trained
b) Learns patterns from data
c) Uses only fixed rules
d) Works without data
Q25. In a Learning-Based Approach, the machine:
a) Creates its own algorithm from the data
b) Uses only manually written rules
c) Never adapts
d) Ignores new data
Q26. Which example best represents a Learning-Based AI model?
a) Calculator
b) Spam Email Filter
c) Keyboard
d) Stopwatch
Q27. During the training of a spam filter, emails are labeled as:
a) Read and Unread
b) Spam and Legitimate
c) Long and Short
d) Personal and Official
Q28. Which learning approach uses labeled data?
a) Reinforcement Learning
b) Supervised Learning
c) Unsupervised Learning
d) Rule-Based Learning
Q29. In supervised learning, labels are:
a) Unknown
b) Removed
c) Known to the trainer
d) Random
Q30. A model trained to classify dogs and cats is an example of:
a) Reinforcement Learning
b) Supervised Learning
c) Association
d) Clustering
Q31. Which learning model works on unlabeled data?
a) Supervised Learning
b) Reinforcement Learning
c) Unsupervised Learning
d) Deep Learning
Q32. Unsupervised Learning mainly helps to identify:
a) Labels
b) Hidden patterns and relationships
c) Programming errors
d) Database records
Q33. The example of grouping 1000 dog images based on colour or size represents:
a) Supervised Learning
b) Reinforcement Learning
c) Unsupervised Learning
d) Rule-Based Learning
Q34. OTT platforms recommending movies based on watch history is an example of:
a) Supervised Learning
b) Reinforcement Learning
c) Unsupervised Learning
d) Rule-Based AI
Q35. Detecting suspicious bank transactions without predefined fraud labels is an example of:
a) Supervised Learning
b) Reinforcement Learning
c) Unsupervised Learning
d) Classification
Q36. Reinforcement Learning mainly learns through:
a) Labeled datasets
b) Fixed rules
c) Trial and error using rewards
d) Manual programming
Q37. In Reinforcement Learning, the objective is to:
a) Minimize storage
b) Maximize rewards
c) Reduce labels
d) Increase rules
Q38. Classification models are used when the output is:
a) Continuous
b) Categorical
c) Random
d) Unlabelled
Q39. Which of the following is an example of a Classification model?
a) Predicting house price
b) Predicting tomorrow’s temperature
c) Classifying emails as spam or not spam
d) Predicting salary
Q40. In the weather example, predicting “Hot” or “Cold” is a:
a) Regression problem
b) Classification problem
c) Clustering problem
d) Association problem
Q41. Regression models work with:
a) Discrete values
b) Continuous values
c) Labels only
d) Images only
Q42. Which of the following is a continuous variable?
a) Grade
b) Colour
c) Temperature
d) Gender
Q43. Which model is suitable for predicting house prices?
a) Classification
b) Regression
c) Clustering
d) Association
Q44. In the house price prediction example, the label (dependent variable) is:
a) Number of bedrooms
b) Carpet size
c) Garage area
d) Price
Q45. Which of the following is an independent variable in house price prediction?
a) Price
b) House value
c) Carpet size
d) Selling amount
Q46. Predicting the selling price of a used car is an example of:
a) Regression
b) Classification
c) Clustering
d) Reinforcement Learning
Q47. Clustering belongs to:
a) Supervised Learning
b) Reinforcement Learning
c) Unsupervised Learning
d) Rule-Based Learning
Q48. Clustering groups data based on:
a) Predefined labels
b) Similarities
c) Programming rules
d) Random selection
Q49. Which statement correctly differentiates Classification and Clustering?
a) Both require labeled data.
b) Classification uses predefined classes, while clustering finds similarities.
c) Clustering predicts continuous values.
d) Classification works only on images.
Q50. Association Rule is mainly used to:
a) Predict continuous values
b) Find meaningful relationships between variables
c) Detect viruses
d) Build websites
Q51. In the supermarket example, customers buying bread are most likely predicted to buy:
a) Rice
b) Butter
c) Milk
d) Eggs
Q52. Artificial Neural Networks (ANN) are modelled on the:
a) Human brain and nervous system
b) Computer motherboard
c) Internet
d) Database
Q53. The biggest advantage of an Artificial Neural Network is that it:
a) Requires no data
b) Automatically extracts features
c) Uses only fixed rules
d) Works only for text
Q54. Which layer of a Neural Network receives the input data?
a) Hidden Layer
b) Output Layer
c) Input Layer
d) Bias Layer
Q55. Most of the processing in a Neural Network takes place in the:
a) Input Layer
b) Hidden Layer
c) Output Layer
d) Data Layer
Q56. The Hidden Layer performs computations using:
a) Files and folders
b) Weights and Biases
c) Keyboard and mouse
d) Labels and features only
Q57. According to the chapter, which of the following is a real-world application of Neural Networks?
a) Facial Recognition
b) Customer Support Chatbot
c) Vegetable Price Prediction
d) All of these
ASSERTION AND REASON BASED QUESTIONS – – Advance Concept of Modeling
Q58. Assertion (A): Machine Learning enables machines to improve their performance through experience.
Reason (R): Machine Learning learns from new data and considers previous mistakes.
a) Both Assertion (A) and Reason (R) are true, and R is the correct explanation of A.
b) Both Assertion (A) and Reason (R) are true, but R is NOT the correct explanation of A.
c) Assertion (A) is true, but Reason (R) is false.
d) Assertion (A) is false, but Reason (R) is true.
Q59. Assertion (A): Labeled data has no tags attached to it.
Reason (R): Labels provide meaning to the data.
a) Both Assertion (A) and Reason (R) are true, and R is the correct explanation of A.
b) Both Assertion (A) and Reason (R) are true, but R is NOT the correct explanation of A.
c) Assertion (A) is true, but Reason (R) is false.
d) Assertion (A) is false, but Reason (R) is true.
Q60. Assertion (A): The testing dataset is used to evaluate the accuracy of a model.
Reason (R): During testing, the model is evaluated using unlabeled data and the results are later verified with labels.
a) Both Assertion (A) and Reason (R) are true, and R is the correct explanation of A.
b) Both Assertion (A) and Reason (R) are true, but R is NOT the correct explanation of A.
c) Assertion (A) is true, but Reason (R) is false.
d) Assertion (A) is false, but Reason (R) is true.
Q61. Assertion (A): Supervised Learning uses labeled data for training.
Reason (R): Labels help the machine learn the relationship between features and target values.
a) Both Assertion (A) and Reason (R) are true, and R is the correct explanation of A.
b) Both Assertion (A) and Reason (R) are true, but R is NOT the correct explanation of A.
c) Assertion (A) is true, but Reason (R) is false.
d) Assertion (A) is false, but Reason (R) is true.
Q62. Assertion (A): Unsupervised Learning requires labeled datasets.
Reason (R): It discovers hidden patterns and similarities in unlabeled data.
a) Both Assertion (A) and Reason (R) are true, and R is the correct explanation of A.
b) Both Assertion (A) and Reason (R) are true, but R is NOT the correct explanation of A.
c) Assertion (A) is true, but Reason (R) is false.
d) Assertion (A) is false, but Reason (R) is true.
Q63. Assertion (A): Reinforcement Learning learns through trial and error.
Reason (R): The objective is to maximize rewards using feedback.
a) Both Assertion (A) and Reason (R) are true, and R is the correct explanation of A.
b) Both Assertion (A) and Reason (R) are true, but R is NOT the correct explanation of A.
c) Assertion (A) is true, but Reason (R) is false.
d) Assertion (A) is false, but Reason (R) is true.
Q64. Assertion (A): Regression models predict continuous values.
Reason (R): Continuous variables include temperature, income and house price.
a) Both Assertion (A) and Reason (R) are true, and R is the correct explanation of A.
b) Both Assertion (A) and Reason (R) are true, but R is NOT the correct explanation of A.
c) Assertion (A) is true, but Reason (R) is false.
d) Assertion (A) is false, but Reason (R) is true.
Q65. Assertion (A): Classification models predict categories.
Reason (R): Spam detection classifies emails into predefined classes.
a) Both Assertion (A) and Reason (R) are true, and R is the correct explanation of A.
b) Both Assertion (A) and Reason (R) are true, but R is NOT the correct explanation of A.
c) Assertion (A) is true, but Reason (R) is false.
d) Assertion (A) is false, but Reason (R) is true.
Q66. Assertion (A): Clustering is an example of supervised learning.
Reason (R): Clustering groups similar data points without predefined labels.
a) Both Assertion (A) and Reason (R) are true, and R is the correct explanation of A.
b) Both Assertion (A) and Reason (R) are true, but R is NOT the correct explanation of A.
c) Assertion (A) is true, but Reason (R) is false.
d) Assertion (A) is false, but Reason (R) is true.
Q67. Assertion (A): Artificial Neural Networks are inspired by the human brain.
Reason (R): Neural Networks automatically extract features without programmer intervention.
a) Both Assertion (A) and Reason (R) are true, and R is the correct explanation of A.
b) Both Assertion (A) and Reason (R) are true, but R is NOT the correct explanation of A.
c) Assertion (A) is true, but Reason (R) is false.
d) Assertion (A) is false, but Reason (R) is true.
COMPETENCY BASED QUESTIONS – Advance Concept of Modeling
Q68. Riya says that Deep Learning and Machine Learning are the same. Her friend says that Deep Learning is a subset of Machine Learning. Who is correct?
a) Riya only
b) Riya’s friend only
c) Both are correct
d) Neither is correct
Q69. A clothing website uses a chatbot that answers only predefined questions like order tracking and delivery status. Which AI approach is being used?
a) Learning-Based Approach
b) Rule-Based Approach
c) Deep Learning
d) Reinforcement Learning
Q70. An email application keeps improving its ability to identify spam emails after receiving feedback from users. Which AI approach is being used?
a) Rule-Based Approach
b) Learning-Based Approach
c) Expert System
d) Manual Programming
Q71. A teacher teaches students using many solved examples before conducting a test. Which AI concept does this situation represent?
a) Testing Dataset
b) Validation Dataset
c) Training Dataset
d) Label
Q72. After training an AI model, a developer checks whether it predicts correct outputs using unseen data. Which dataset is being used?
a) Training Dataset
b) Testing Dataset
c) Feature Dataset
d) Input Dataset
Q73. A dataset contains the following columns:
Colour Weight Fruit Name
The model predicts the fruit name using colour and weight. Which option correctly identifies the features and label?
a) Features: Fruit Name; Label: Colour, Weight
b) Features: Colour, Weight; Label: Fruit Name
c) Features: Weight; Label: Colour and Fruit Name
d) Features: Fruit Name and Colour; Label: Weight
Q74. A model is trained using thousands of images already labeled as “Cat” and “Dog”. Which type of learning is being used?
a) Reinforcement Learning
b) Unsupervised Learning
c) Supervised Learning
d) Deep Learning only
Q75. A company has 10,000 customer records, but none are categorized. The AI groups customers based on buying behaviour. Which learning model is being used?
a) Supervised Learning
b) Reinforcement Learning
c) Unsupervised Learning
d) Rule-Based Learning
Q76. A robot first identifies an apple as a cherry. It receives negative feedback and, after several attempts, correctly identifies the apple. Which learning technique is being used?
a) Supervised Learning
b) Reinforcement Learning
c) Unsupervised Learning
d) Classification
Q77. An OTT platform recommends movies similar to the ones a user has already watched. Which learning model is most suitable?
a) Supervised Learning
b) Unsupervised Learning
c) Reinforcement Learning
d) Rule-Based Learning
Q78. A bank wants to predict whether a customer is eligible for a loan. Which supervised learning model should be used?
a) Regression Model
b) Clustering Model
c) Classification Model
d) Association Model
Q79. A real estate company wants to predict the selling price of a house using the number of bedrooms, carpet area and garage size. Which model should be used?
a) Classification Model
b) Clustering Model
c) Regression Model
d) Association Rule
Q80. A music app groups songs with similar tempo and intensity before recommending them to users. Which learning technique is being used?
a) Classification
b) Regression
c) Clustering
d) Reinforcement Learning
Q81. A supermarket observes that customers buying bread often buy butter. Which learning technique is being used?
a) Clustering
b) Classification
c) Association Rule
d) Regression
Q82. A hospital wants to analyse thousands of medical images automatically. Which AI technique is most suitable?
a) Artificial Neural Network
b) Rule-Based System
c) Decision Tree
d) Expert System
Q83. A student says that all processing in an Artificial Neural Network happens in the Input Layer. Which option is correct?
a) The student is correct.
b) Processing mainly happens in the Hidden Layer.
c) Processing happens only in the Output Layer.
d) All layers perform equal processing.
Q84. An AI model performs calculations using weights and biases before passing the result to the next layer. Which layer performs this task?
a) Input Layer
b) Hidden Layer
c) Output Layer
d) Training Layer
Q85. While deciding whether to go to a park, a person considers a jacket, an umbrella, the current weather and the weather forecast. Some factors are considered more important than others. Which Neural Network concept does this represent?
a) Bias
b) Epoch
c) Weights
d) Dataset
Q86. A smartphone unlocks only after recognizing its owner’s face. Which AI application makes this possible?
a) Speech Recognition
b) Machine Translation
c) Facial Recognition
d) Recommendation System
Q87. A company wants an AI system that can improve automatically whenever new data becomes available. Which approach should the company choose?
a) Rule-Based Approach
b) Learning-Based Approach
c) Manual Programming
d) Fixed Algorithm Approach
