AI and model card reading skills

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-model-families-AI-and-model-card-reading-skills-r9a1-creators

1. What is the primary purpose of a model card?

   1. To guarantee the model will work for any use case
   2. To show the internal code structure of the model
   3. To provide a marketing pitch for the AI model
   4. To document what a model does, where it was tested, and its known limitations

2. Which task can AI assist with when reading a model card?

   1. Summarizing the card into key bullet points
   2. Confirming the model will pass your specific safety requirements
   3. Replacing the need for your own testing
   4. Guaranteeing the model has no biases

3. Why is the 'intended use' section of a model card critical to review?

   1. It lists every possible application the model can handle
   2. It legally binds the model provider to any damages
   3. It is required by law in all jurisdictions
   4. It tells you exactly what tasks the model was designed for and may not support others

4. What fundamental limitation do model cards have, regardless of how well they're written?

   1. They are always updated in real-time
   2. They prevent all model failures
   3. They cannot capture every possible behavior or edge case of a model
   4. They are required by law to be 100% accurate

5. You read a model card and it looks polished but contains no specific benchmark numbers. What should you conclude?

   1. The benchmarks must be proprietary
   2. You should request specific performance data before committing
   3. The model likely performs worse than competitors with published numbers
   4. The model has perfect performance

6. What does 'language coverage' refer to in model card evaluation?

   1. The languages in which the model's capabilities have been tested
   2. The model's ability to speak human languages
   3. The number of programming languages the model's code uses
   4. The languages the model can generate text in

7. What does 'refusal patterns' describe in a model card context?

   1. The types of requests the model is programmed to decline
   2. The model's ability to refuse inappropriate user inputs
   3. How quickly the model responds to requests
   4. The model's rate of failing to generate responses

8. Why should you conduct your own evaluation even after reviewing a model card?

   1. Model cards cannot be trusted at all
   2. The model provider forbids relying on their card
   3. Model cards are required to be wrong by law
   4. Your specific use case and data may behave differently than benchmark conditions

9. What distinguishes a claim from a benchmark in a model card?

   1. Claims are always false; benchmarks are always true
   2. Benchmarks are marketing language
   3. A claim is an assertion without verification; a benchmark is measured performance data
   4. They mean the same thing

10. Which of the following would be a typical limitation documented on a model card?

    1. The model only works on Tuesdays
    2. The model requires electricity to operate
    3. The model may produce incorrect facts and should not be used for medical diagnosis without oversight
    4. The model cannot process images

11. What does the license section of a model card indicate?

    1. What you are legally permitted to do with the model
    2. Who owns the model's weights
    3. The model's release date
    4. How much the model costs

12. What does it mean when a model card lacks a 'known limitations' section?

    1. Limitations sections are optional and irrelevant
    2. The model has no limitations
    3. The provider may not have conducted thorough evaluation
    4. The model is perfect for all use cases

13. What is the risk of using a model for a purpose not listed in its 'intended use'?

    1. You will automatically violate copyright law
    2. There is no risk; AI can handle anything
    3. The model may perform unreliably and the provider will not offer support
    4. The model will always fail

14. What should you do if a model card is mostly marketing language without technical details?

    1. Use the model immediately since marketing indicates quality
    2. Look for another model provider that publishes detailed documentation
    3. Ignore the lack of details
    4. Accept it at face value since it's official

15. Why might two models with similar benchmark scores perform differently in production?

    1. The models may handle different types of inputs or edge cases differently
    2. Benchmarks are always wrong
    3. Benchmark scores predict future performance exactly
    4. Only benchmark scores matter for production