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AI for Students Worksheet: Final Test

AI for students lesson 24: Final test: AI for students · Set 218 · 40 questions
TalentJR

AI tip (Know it, prompt it, check it): Find the topic: what AI is, prompts, honest use, checking, privacy, age rules, fakes, credit, study, apps or projects. Use that lesson’s rule and read every option before you choose. For number questions, write the calculation first: words over = written − limit, accuracy = correct ÷ total × 100.

NameDateTime takenScore ___ / 40
  1. 1.True or false: “follows fixed rules” describes how a normal program works. (a) True (b) False
  2. 2.📲 Input, model or output: “your spoken words”? (a) the model (b) the input (c) the output
  3. 3.✨ Generative or not: writing a new poem? (a) sorts or predicts (b) makes something new
  4. 4.🔍 Which is the odd one out? (a) test on many groups (b) ask different people (c) add varied examples (d) test on one group only
  5. 5.Which of these helps reduce bias? (a) test on one group only (b) use one group only (c) test on many groups (d) skip testing
  6. 6.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) ignore complaints (d) fix wrong labels
  7. 7.🏷️ What is a “label” in machine learning? (a) the font size (b) a sticker on a laptop (c) the right answer tag (d) the price
  8. 8.🔍 Which is the odd one out? (a) “write my essay” (b) “solve my test paper” (c) “give me a hint” (d) “finish my homework”
  9. 9.🎂 Where can you find an app’s age rule? (a) nowhere (b) on its logo (c) its terms or rules page (d) in the weather report
  10. 10.🖼️ Warning sign or good sign: no date or author? (a) a warning sign (b) a good sign
  11. 11.True or false: “spots patterns in data” describes how a normal program works. (a) False (b) True
  12. 12.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  13. 13.Which of these describes how a normal program works? (a) one set rule per case (b) gives a likely answer (c) spots patterns in data (d) handles unseen examples
  14. 14.🔁 Learning a hard sum with AI: which should you do first — do one more on your own or ask for one hint? (a) do one more on your own (b) ask for one hint
  15. 15.🧩 Why add an example to a prompt? (a) to show the style (b) to make it longer (c) to confuse the AI (d) to hide the task
  16. 16.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) balance the data (d) ignore complaints
  17. 17.©️ What does “giving credit” mean? (a) paying with a card (b) hiding the source (c) adding a filter (d) saying who made it
  18. 18.🔮 A trained model sees a new photo and says “mango”. This is… (a) a password (b) a prediction (c) training data (d) a label error
  19. 19.Which of these is the input? (a) the patterns it learned (b) the label “cat” (c) a drawing you make (d) the predicted word
  20. 20.🤖 What does AI stand for? (a) automatic internet (b) app installer (c) actual information (d) artificial intelligence
  21. 21.🔍 Which is the odd one out? (a) a song on the radio (b) my own survey results (c) a quote from a book (d) a classmate’s drawing
  22. 22.Which of these does not make a prompt better? (a) use unclear slang (b) split into small asks (c) add your class level (d) give an example
  23. 23.❌ Ananya’s leaf identifier was tested on 45 new leaf photos and got 37 right. How many did it get wrong?
  24. 24.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
  25. 25.🖼️ Warning sign or good sign: fits other reports? (a) a warning sign (b) a good sign
  26. 26.True or false: “AI can be wrong” is a fact about AI. (a) True (b) False
  27. 27.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
  28. 28.True or false: “fixed replies only” describes a learning chatbot. (a) True (b) False
  29. 29.True or false: a weather app guessing tomorrow’s rain is AI predicting something. (a) True (b) False
  30. 30.True or false: “Suggest 4 names for our eco club, each under 3 words.” is a vague prompt. (a) True (b) False
  31. 31.🔍 Which is the odd one out? (a) voice typing (b) a map predicting traffic (c) a doorbell (d) a voice assistant
  32. 32.Which of these is okay to share with an AI chatbot? (a) your phone number (b) a bank card number (c) your name and school (d) your spelling list
  33. 33.🤖 AI or a normal program: which one “one set rule per case”? (a) AI (b) a normal program
  34. 34.📄 A chapter has 500 words. Diya asks an AI chatbot for a summary one tenth as long. How many words should the summary have?
  35. 35.✍️ Which prompt is clearer?
    A: “Give me some maths.”
    B: “Give me 10 questions on the 7 times table, with answers at the end.”
    (a) Prompt A (b) Prompt B
  36. 36.🔍 Which is the odd one out? (a) calm, factual words (b) a shocking headline (c) a named author (d) many trusted sources
  37. 37.✍️ Clear or vague: “Test me.”? (a) vague (b) clear
  38. 38.True or false: an online shop showing “you may also like” is AI recommending something. (a) False (b) True
  39. 39.True or false: “Use a table with 2 columns.” is the format part of a prompt. (a) False (b) True
  40. 40.📋 Planning an AI project: which comes first — work out the accuracy or correct bad labels? (a) work out the accuracy (b) correct bad labels

Answer key

  1. True
  2. the input
  3. makes something new
  4. test on one group only
  5. test on many groups
  6. fix wrong labels
  7. the right answer tag
  8. “give me a hint”
  9. its terms or rules page
  10. a warning sign
  11. False
  12. True
  13. one set rule per case
  14. ask for one hint
  15. to show the style
  16. ignore complaints
  17. saying who made it
  18. a prediction
  19. a drawing you make
  20. artificial intelligence
  21. my own survey results
  22. use unclear slang
  23. 8
  24. bad for training
  25. a good sign
  26. True
  27. bad for training
  28. False
  29. True
  30. False
  31. a doorbell
  32. your spelling list
  33. a normal program
  34. 50
  35. Prompt B
  36. a shocking headline
  37. vague
  38. True
  39. True
  40. correct bad labels

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