CHAPTER 1 – INTRODUCTION TO ARTIFICIAL INTELLIGENCE & MACHINE LEARNING
MCQ Question Paper
Class: __________
Time: 1 Hour
Full Marks: 30
Instructions:
All questions are compulsory.
Each question carries 1 mark.
Choose the correct option.
Section A – Multiple Choice Questions
1. Artificial Intelligence (AI) refers to:
a) Human intelligence
b) Intelligence demonstrated by machines
c) Natural intelligence
d) Emotional intelligence
2. Which of the following is an example of AI?
a) Calculator
b) Voice assistant like Alexa
c) Light bulb
d) Fan
3. The main goal of AI is to:
a) Replace humans completely
b) Make machines think and act like humans
c) Increase internet speed
d) Store data
4. Machine Learning is a subset of:
a) Big Data
b) Artificial Intelligence
c) Data Science
d) Cloud Computing
5. Big Data refers to:
a) Small structured data
b) Large and complex data sets
c) Printed books
d) Manual records
6. Data Science mainly deals with:
a) Cooking data
b) Analyzing and interpreting data
c) Manufacturing machines
d) Designing hardware
7. Which of the following technologies helps AI systems learn from data?
a) Machine Learning
b) Typing
c) Scanning
d) Printing
8. Which field combines statistics, programming, and domain knowledge?
a) Artificial Intelligence
b) Data Science
c) Big Data
d) Robotics
9. AI systems can perform tasks such as:
a) Reasoning
b) Learning
c) Problem-solving
d) All of the above
10. Which of the following is NOT related to AI?
a) Natural Language Processing
b) Computer Vision
c) Manual typewriter
d) Robotics
11. The relationship between AI and Machine Learning is:
a) AI is a subset of ML
b) ML is a subset of AI
c) Both are unrelated
d) Both are hardware devices
12. Big Data is important for Machine Learning because:
a) ML needs data to learn
b) ML works without data
c) ML deletes data
d) ML prints data
13. Data Science helps AI by:
a) Ignoring data
b) Cleaning and analyzing data
c) Destroying data
d) Compressing images only
14. Which of the following is a real-life application of AI?
a) Self-driving cars
b) Traditional clock
c) Chalkboard
d) Paper notebook
15. The term “Beyond the AI Hype” means:
a) Ignoring AI completely
b) Understanding realistic capabilities and limitations of AI
c) Promoting false information
d) Replacing all humans
16. AI systems work mainly on:
a) Algorithms
b) Emotions
c) Instincts
d) Magic
17. Which type of learning involves labeled data?
a) Supervised Learning
b) Unsupervised Learning
c) Reinforcement Learning
d) Manual Learning
18. Which of the following is an AI-powered recommendation system example?
a) Netflix suggestions
b) Wall painting
c) Blackboard
d) Printed newspaper
19. Data that grows rapidly in volume, velocity, and variety is called:
a) Small Data
b) Big Data
c) Hard Data
d) Old Data
20. AI can improve decision-making by:
a) Guessing randomly
b) Analyzing patterns in data
c) Avoiding data
d) Ignoring information
21. Which of the following is a limitation of AI?
a) Requires quality data
b) Works perfectly in all situations
c) Has human emotions
d) Never makes errors
22. The foundation of Machine Learning is:
a) Data and algorithms
b) Paper and pen
c) Manual typing
d) Electricity only
23. AI that can perform only a specific task is called:
a) General AI
b) Narrow AI
c) Super AI
d) Human AI
24. Which of the following industries uses AI?
a) Healthcare
b) Education
c) Banking
d) All of the above
25. Data Science uses which of the following tools?
a) Statistics
b) Programming
c) Visualization
d) All of the above
26. AI helps businesses by:
a) Increasing efficiency
b) Automating tasks
c) Improving customer experience
d) All of the above
27. Which statement is correct?
a) AI works without data
b) Machine Learning needs data
c) Big Data is unrelated to AI
d) Data Science ignores statistics
28. The process of machines improving through experience is called:
a) Coding
b) Machine Learning
c) Painting
d) Designing
29. AI systems make predictions using:
a) Random guessing
b) Data patterns
c) Manual calculation only
d) None of the above
30. The future of AI depends largely on:
a) Data availability
b) Advanced algorithms
c) Ethical usage
d) All of the above
Answer Key – Chapter 1: Introduction to Artificial Intelligence & Machine Learning
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