AI Model Families: Pin Models, Watch Deprecations, and Plan Migrations

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-model-families-deprecation-and-pinning-r8a1-creators

1. What is the primary risk of using a model's 'latest' alias in a production AI application?

   1. Your production behavior can change overnight without warning
   2. The latest alias always points to the most capable model version
   3. The latest alias will eventually stop working entirely
   4. Using latest reduces the cost of API calls

2. A developer wants to ensure their AI-powered feature behaves consistently over time. Which practice directly addresses this goal?

   1. Subscribing to the provider's newsletter
   2. Using the most recent model available
   3. Checking model pricing weekly
   4. Pinning to a specific versioned model ID

3. What does the lesson identify as the only way to control behavioral changes that providers may introduce in updates they consider minor?

   1. Rely on the provider's changelog
   2. Pin versions and run regression evals on every announced update
   3. Use the cheapest available model tier
   4. Trust that semantic versioning prevents breaking changes

4. What is a regression eval in the context of AI model management?

   1. An evaluation comparing a model's performance against its previous version to detect behavioral drift
   2. A cost analysis of running different model sizes
   3. A security audit that checks for model vulnerabilities
   4. A test that measures how quickly a model responds to requests

5. Which of the following is explicitly listed as something AI cannot do in this lesson?

   1. Predict which model will be cheapest next month
   2. Run regression evals on your behalf
   3. Pin model versions automatically
   4. Stop providers from deprecating models

6. What information should you gather for each model ID you currently use before planning migrations?

   1. The model's training data source
   2. The model's energy consumption metrics
   3. The names of the researchers who built it
   4. The most recent deprecation timeline and successor model

7. Why is it important to plan a migration window before a model's End of Life (EOL) date?

   1. To avoid being forced into an emergency migration when the model becomes unavailable
   2. To test the model's maximum throughput
   3. To give the provider time to train a new model
   4. To reduce the cost of API calls during off-peak hours

8. The lesson recommends subscribing to what per provider?

   1. Social media accounts
   2. Technical support tickets
   3. Deprecation announcements
   4. Marketing emails

9. Which statement about model deprecation is correct based on the lesson content?

   1. Once deprecated, a model will always remain available at increased cost
   2. Providers always announce deprecations months in advance
   3. Deprecation only affects older, less capable models
   4. Deprecations can silently introduce behavioral changes in what providers call minor updates

10. When should regression evaluations ideally be run according to best practices in this lesson?

    1. Only when you notice problems in production
    2. When the provider lowers their prices
    3. On every announced version update
    4. Once per year during audit season

11. What is model pinning?

    1. Locking a model in a physical location
    2. A method to reduce model latency
    3. Restricting who can access your model
    4. The practice of fixing your application to a specific versioned model identifier

12. Why can pointing at a model's 'latest' alias cause production issues?

    1. Latest always means the most expensive version
    2. Latest models always require more computational resources
    3. The alias can redirect to a different model version without notice
    4. The alias will eventually be deleted by the provider

13. What is a migration window in the context of AI model management?

    1. The period when a model is being trained
    2. The duration of a model's beta testing phase
    3. The time it takes for a model to process a request
    4. A scheduled time period allocated for transitioning from one model version to another

14. The lesson mentions that a model the provider considers a minor update can change behavior on your tasks materially. What is the recommended defense against this?

    1. Pin versions and run regression tests
    2. Ignore updates that providers call minor
    3. Complain to the provider about behavioral changes
    4. Use a different provider for every minor update

15. What does the lesson recommend as part of a complete eval and migration plan?

    1. Migrating immediately without testing
    2. Calendar dates for evaluation and migration activities
    3. Waiting until the model is already deprecated
    4. Skipping regression testing to save time