Unit–1: Introduction to AI Class 9th

Section A: AI Project Cycle – Overview (10 MCQs)

1. The AI Project Cycle is:
   a) A random sequence of AI steps
   b) A structured process for AI projects
   c) A hardware configuration process
   d) A method of human learning

2. Which of the following is NOT a stage in the AI Project Cycle?
   a) Data Acquisition
   b) Modelling
   c) Cooking Recipes
   d) Problem Scoping

3. What is the main goal of the AI Project Cycle?
   a) To write AI-based poetry
   b) To develop efficient AI solutions
   c) To replace all human jobs
   d) To create gaming applications only

4. In the AI Project Cycle, Modelling refers to:
   a) Building mathematical or AI models from data
   b) Drawing pictures of AI
   c) Testing hardware performance
   d) Writing a novel about AI

5. Ethics and Morals in AI projects focus on:
   a) Speed of processing
   b) Fair and unbiased AI solutions
   c) Creating humorous AI
   d) Reducing electricity cost only

6. The first stage in an AI Project Cycle is:
   a) Data Exploration
   b) Problem Scoping
   c) Modelling
   d) AI Deployment

7. Which stage involves real-world implementation of an AI solution?
   a) Data Exploration
   b) Deployment
   c) Evaluation
   d) Problem Scoping

8. In the AI Project Cycle, Evaluation Techniques are used to:
   a) Increase project cost
   b) Check AI model accuracy
   c) Write AI history books
   d) Create random predictions

9. The AI Project Cycle is usually:
   a) Linear and one-time
   b) Iterative and repeated as needed
   c) Only for robotics projects
   d) Completed without feedback

10. The AI Project Cycle helps in:
    a) Developing structured AI solutions
    b) Cooking AI meals
    c) Avoiding testing
    d) Making AI toys only

Section B: Problem Scoping and Setting Goals (12 MCQs)

11. Problem Scoping in AI means:
    a) Finding a good problem to solve
    b) Writing code immediately
    c) Buying AI hardware
    d) Making AI games

12. Which question is important in Problem Scoping?
    a) Who will benefit from the solution?
    b) How to play music?
    c) What is the AI’s favorite color?
    d) Which mobile phone to buy?

13. A clear goal in AI should be:
    a) Vague and general
    b) Specific and measurable
    c) Impossible to achieve
    d) Randomly decided

14. Stakeholders in AI problem scoping are:
    a) People affected by the solution
    b) Computer servers only
    c) Programmers only
    d) Robots only

15. SMART goals stand for:
    a) Simple, Measurable, Attractive, Reliable, Timely
    b) Specific, Measurable, Achievable, Relevant, Time-bound
    c) Small, Medium, Average, Reliable, True
    d) Speed, Memory, Accuracy, Response, Time

16. The scope of a problem defines:
    a) Its limits and boundaries
    b) The number of computers used
    c) The budget for marketing
    d) The number of AI models used

17. Setting unrealistic AI goals can lead to:
    a) Faster project success
    b) Project failure
    c) Reduced cost
    d) Automatic success

18. Identifying constraints in problem scoping means:
    a) Knowing the limitations and challenges
    b) Buying more computers
    c) Removing all rules
    d) Ignoring deadlines

19. Which of these is an example of a specific AI goal?
    a) “Improve the system”
    b) “Reduce delivery time by 20% within 3 months”
    c) “Make AI better”
    d) “Do something fast”

20. Who should be consulted in problem scoping?
    a) Stakeholders
    b) Only the programmer
    c) Only the AI model
    d) No one

21. Problem Scoping also involves:
    a) Understanding user needs
    b) Installing antivirus software
    c) Making website logos
    d) Choosing a music playlist

22. Goals in AI should be:
    a) Specific and measurable
    b) Random and uncertain
    c) Impossible to track
    d) Set without thinking

Section C: Data Acquisition (8 MCQs)

23. Data Acquisition is:
    a) Collecting relevant data for AI projects
    b) Buying AI books
    c) Drawing AI diagrams
    d) Ignoring data

24. Which of these is a data source?
    a) Sensors
    b) Databases
    c) User surveys
    d) All of the above

25. Data should be:
    a) Relevant and accurate
    b) Random and large
    c) Outdated and incomplete
    d) Fake but large

26. Data acquisition in AI can be:
    a) Manual or automatic
    b) Only manual
    c) Only automatic
    d) Always free

27. Primary data is:
    a) Collected first-hand
    b) Taken from books only
    c) Always outdated
    d) Never reliable

28. Secondary data is:
    a) Collected from existing sources
    b) Always fake
    c) Not used in AI
    d) Collected from hardware

29. Which tool can be used for online data collection?
    a) Google Forms
    b) MS Word
    c) Photoshop
    d) Paint

30. In AI projects, more data usually leads to:
    a) Better accuracy
    b) Slower training only
    c) Always wrong results
    d) No effect

Section D: Data Exploration (8 MCQs)

31. Data Exploration means:
    a) Understanding and analyzing data
    b) Throwing data away
    c) Buying data servers
    d) Ignoring data format

32. Which chart is used for visualizing data?
    a) Bar chart
    b) Pie chart
    c) Histogram
    d) All of these

33. Missing data in a dataset should be:
    a) Ignored completely
    b) Handled properly
    c) Increased
    d) Left as is

34. Outliers are:
    a) Data points far from the normal values
    b) Normal data points
    c) Fake data
    d) Missing values

35. Cleaning data means:
    a) Removing errors and inconsistencies
    b) Deleting all data
    c) Making data colorful
    d) Changing data randomly

36. Which tool is often used in data exploration?
    a) Excel
    b) Python libraries
    c) Tableau
    d) All of these

37. Data exploration helps in:
    a) Understanding patterns
    b) Making jokes about AI
    c) Ignoring dataset issues
    d) Slowing project progress

38. A dataset is balanced when:
    a) All classes have similar numbers of samples
    b) It is stored on a stable server
    c) It is encrypted
    d) It is very large

Section E: Modelling (8 MCQs)

39. Modelling in AI means:
    a) Building AI models using algorithms
    b) Painting AI pictures
    c) Buying AI software
    d) Avoiding coding

40. A training dataset is used to:
    a) Teach the AI model
    b) Test AI performance
    c) Store AI rules only
    d) Delete unwanted data

41. A test dataset is used to:
    a) Check AI performance on unseen data
    b) Train the model
    c) Increase accuracy during training
    d) Store errors only

42. Supervised learning uses:
    a) Labeled data
    b) Unlabeled data
    c) Random images
    d) Audio only

43. Unsupervised learning uses:
    a) Unlabeled data
    b) Labeled data
    c) Mixed data only
    d) No data

44. The purpose of model evaluation is to:
    a) Check accuracy and reliability
    b) Increase project cost
    c) Delete datasets
    d) Ignore performance

45. Overfitting occurs when:
    a) The model performs well on training data but poorly on new data
    b) The model performs equally well on all data
    c) The dataset is too small
    d) There is no training data

46. Cross-validation is:
    a) A technique to test model performance
    b) Making AI angry
    c) Randomizing data
    d) Ignoring test sets

Section F: AI Project Evaluation, Techniques, Deployment, Ethics & Morals (14 MCQs)

47. AI project evaluation checks:
    a) Model performance
    b) Hardware cost
    c) Internet speed
    d) File size

48. Which is NOT an evaluation metric?
    a) Accuracy
    b) Precision
    c) Recall
    d) Wallpaper color

49. Precision in AI measures:
    a) Correct positive predictions out of predicted positives
    b) Correct negatives out of total negatives
    c) Dataset size
    d) AI speed

50. Recall in AI measures:
    a) Correct positive predictions out of actual positives
    b) Time taken to train
    c) Dataset download speed
    d) Model size

51. F1 Score is:
    a) Harmonic mean of Precision and Recall
    b) A type of car race
    c) A data format
    d) Dataset size measure

52. Deployment means:
    a) Making the AI solution available to users
    b) Storing data in a locker
    c) Deleting test data
    d) Avoiding public use

53. Cloud deployment refers to:
    a) Hosting AI solutions online
    b) Running AI offline
    c) Printing AI code
    d) Storing AI on paper

54. AI ethics focus on:
    a) Fairness, transparency, and accountability
    b) Only increasing profit
    c) Deleting unused data
    d) Ignoring human rights

55. AI bias occurs when:
    a) Model gives unfair results
    b) AI works equally for everyone
    c) AI is too fast
    d) Data is complete

56. Which of these is an ethical concern?
    a) Privacy issues
    b) Transparency
    c) Unfair decision-making
    d) All of the above

57. Responsible AI means:
    a) AI that respects ethics and morals
    b) AI that works without humans
    c) AI that learns quickly
    d) AI with maximum profit

58. AI solutions should be:
    a) Explainable and transparent
    b) Hidden and secret
    c) Unchecked
    d) Unregulated

59. Which law is important for AI privacy?
    a) Data protection laws
    b) Cooking safety laws
    c) Traffic laws
    d) Sports rules

60. Morals in AI refer to:
    a) Human values guiding AI use
    b) Dataset size
    c) Speed of AI
    d) Accuracy of predictions