AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-model-families-AI-hybrid-edge-cloud-pipelines-r13a3-creators

1. What is the core idea behind "AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App"?

   1. Edge for privacy and speed; cloud for muscle. The interesting designs blend them.
   2. Compare cost per 1M tokens at your typical input/output ratio
   3. Plan re-distillation as base models improve
   4. investment

2. Which term best describes a foundational idea in "AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App"?

   1. edge
   2. hybrid
   3. cloud
   4. fallback

3. A learner studying AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App would need to understand which concept?

   1. hybrid
   2. cloud
   3. edge
   4. fallback

4. Which of these is directly relevant to AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App?

   1. hybrid
   2. edge
   3. fallback
   4. cloud

5. Which of the following is a key point about AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App?

   1. Triage with a tiny local classifier; escalate hard cases to cloud
   2. Run privacy-sensitive parts locally, generic parts in cloud
   3. Cache common answers on-device
   4. Provide offline degradation gracefully

6. Which of these does NOT belong in a discussion of AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App?

   1. Run privacy-sensitive parts locally, generic parts in cloud
   2. Cache common answers on-device
   3. Compare cost per 1M tokens at your typical input/output ratio
   4. Triage with a tiny local classifier; escalate hard cases to cloud

7. Which statement is accurate regarding AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App?

   1. Hide the engineering complexity of two model stacks
   2. Skip eval discipline on both layers
   3. Eliminate cloud dependency for everything
   4. Match a single-model UX for response shape

8. Which of these does NOT belong in a discussion of AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App?

   1. Hide the engineering complexity of two model stacks
   2. Eliminate cloud dependency for everything
   3. Compare cost per 1M tokens at your typical input/output ratio
   4. Skip eval discipline on both layers

9. What is the key insight about "Try this prompt" in the context of AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App?

   1. Design a hybrid pipeline for [feature]. Specify which steps run on-device, which call cloud, and how to fall back when o…
   2. Compare cost per 1M tokens at your typical input/output ratio
   3. Plan re-distillation as base models improve
   4. investment

10. What is the key insight about "Watch out" in the context of AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App?

    1. Compare cost per 1M tokens at your typical input/output ratio
    2. Two model stacks = two eval sets, two deprecation timelines, two bugs. Justify the complexity before you build it.
    3. Plan re-distillation as base models improve
    4. investment

11. Which statement accurately describes an aspect of AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App?

    1. Compare cost per 1M tokens at your typical input/output ratio
    2. Plan re-distillation as base models improve
    3. Real production AI products often use a small on-device model for first-pass triage and a cloud frontier model for the hard 10%.
    4. investment

12. Which best describes the scope of "AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App"?

    1. It is unrelated to model-families workflows
    2. It applies only to the opposite beginner tier
    3. It was deprecated in 2024 and no longer relevant
    4. It focuses on Edge for privacy and speed; cloud for muscle. The interesting designs blend them.

13. Which section heading best belongs in a lesson about AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App?

    1. What AI does well here
    2. Compare cost per 1M tokens at your typical input/output ratio
    3. Plan re-distillation as base models improve
    4. investment

14. Which section heading best belongs in a lesson about AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App?

    1. Compare cost per 1M tokens at your typical input/output ratio
    2. What AI cannot do
    3. Plan re-distillation as base models improve
    4. investment

15. Which of the following is a concept covered in AI Hybrid Pipelines: Mixing On-Device and Cloud Models in One App?

    1. edge
    2. cloud
    3. hybrid
    4. fallback