← Part 1 test: what AI is lesson New set →

AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 355 · 40 questions
TalentJR

AI tip (Name the idea, then answer): Learning from examples → AI; fixed rules → a normal program. Order: training data → model → prediction; good data is many, varied and correctly labelled. Generative AI makes new things and can hallucinate; one-sided data causes bias.

NameDateTime takenScore ___ / 40
  1. 1.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
  2. 2.🏠 AI or not AI: a spam filter? (a) uses AI (b) not AI
  3. 3.True or false: “wrong labels” is bad for training a model. (a) False (b) True
  4. 4.🎨 This prompt asks a generative AI for: a logo idea for the eco club. What kind of output is that? (a) music or sound (b) text (c) an image
  5. 5.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
  6. 6.✨ Generative or not: making up a story? (a) sorts or predicts (b) makes something new
  7. 7.🧐 Fact or myth: “AI can make up facts”? (a) a fact (b) a myth
  8. 8.True or false: translating a sentence into Hindi only needs a simple fixed rule. (a) True (b) False
  9. 9.🛠️ Which does this job need: recognising a bird from its song? (a) needs AI (learning) (b) a simple rule is enough
  10. 10.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it will turn yellow (d) it may get it wrong
  11. 11.🔍 Which is the odd one out? (a) many varied examples (b) copies of one photo (c) clear, sharp examples (d) examples of every type
  12. 12.⏩ In machine learning, which comes first: 500 labelled leaf photos or the trained program? (a) 500 labelled leaf photos (b) the trained program
  13. 13.Which of these is good for training a model? (a) many varied examples (b) wrong labels (c) only one kind of example (d) copies of one photo
  14. 14.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
  15. 15.⏩ In machine learning, which comes first: old emails marked spam or naming a new fruit photo? (a) old emails marked spam (b) naming a new fruit photo
  16. 16.🤖 What does AI stand for? (a) automatic internet (b) app installer (c) actual information (d) artificial intelligence
  17. 17.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  18. 18.✅ Is this a real check of an AI answer: find a trusted source? (a) a real check (b) not a real check
  19. 19.Which of these describes how AI works? (a) never learns from data (b) same steps every time (c) one set rule per case (d) gives a likely answer
  20. 20.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
  21. 21.📱 What is the AI doing here: a phone unlocking when it sees your face? (a) recognising (b) predicting (c) generating (d) recommending
  22. 22.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) a prediction (c) training data
  23. 23.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
  24. 24.Which of these is training data? (a) what training builds (b) the trained program (c) naming a new fruit photo (d) labelled fruit photos
  25. 25.🔍 Which is the odd one out? (a) AI has real feelings (b) AI can mix up numbers (c) AI can be wrong (d) AI learns from data
  26. 26.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  27. 27.True or false: “tomorrow’s rain guess” is the model. (a) False (b) True
  28. 28.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  29. 29.🔁 Training data, the model or a prediction: “what training builds”? (a) the model (b) a prediction (c) training data
  30. 30.True or false: “does exactly as coded” describes how AI works. (a) False (b) True
  31. 31.✍️ How does an AI chatbot write its replies? (a) uses magic (b) asks a person (c) copies one website (d) predicts likely words
  32. 32.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) a sticker on a laptop (d) the font size
  33. 33.🧪 Why test a model on NEW examples? (a) to make it slower (b) to see if it works (c) to delete its data (d) to change its name
  34. 34.🏠 AI or not AI: a fan regulator? (a) not AI (b) uses AI
  35. 35.⏩ In machine learning, which comes first: what training builds or naming a new leaf? (a) what training builds (b) naming a new leaf
  36. 36.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
  37. 37.🔍 Which is the odd one out? (a) AI can mix up numbers (b) AI can be wrong (c) AI can make up facts (d) AI is never wrong
  38. 38.🧮 Is a basic calculator an AI system? (a) yes, it is smart (b) no, it follows rules (c) only at night (d) yes, it can think
  39. 39.True or false: “long answers are true” is a fact about AI. (a) True (b) False
  40. 40.🤖 AI or a normal program: which one “same steps every time”? (a) AI (b) a normal program

Answer key

  1. bad for training
  2. uses AI
  3. True
  4. an image
  5. good for training
  6. makes something new
  7. a fact
  8. False
  9. needs AI (learning)
  10. it may get it wrong
  11. copies of one photo
  12. 500 labelled leaf photos
  13. many varied examples
  14. good for training
  15. old emails marked spam
  16. artificial intelligence
  17. reduces bias
  18. a real check
  19. gives a likely answer
  20. good for training
  21. recognising
  22. training data
  23. bad for training
  24. labelled fruit photos
  25. AI has real feelings
  26. False
  27. False
  28. True
  29. the model
  30. False
  31. predicts likely words
  32. the right answer tag
  33. to see if it works
  34. not AI
  35. what training builds
  36. False
  37. AI is never wrong
  38. no, it follows rules
  39. False
  40. a normal program

Free ai for students lessons, practice and worksheets at talentjr.in/ai-for-students