1.4 Understand Model Selection in AI Model Hubs and Repositories for Appropriate Use‑cases (e.g., Reasoning, Multimodality) AI-TECHNICAL-PRACTITIONER Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A team is selecting a model for a real-time chatbot that must respond in under 200 milliseconds. They have narrowed the candidates to two models on Hugging Face: Model A has a higher benchmark score but is 10 times larger, and Model B has a slightly lower score but is much smaller and faster. Which model should they choose?

   • AModel B, because smaller models are always more accurate than larger ones.
   • BModel A, because the team can use model quantization to speed it up enough.
   • CModel A, because higher benchmark scores are the most important criterion.
   • DModel B, because the latency constraint is a hard requirement that Model A cannot meet.
   Reveal Solution Discussion Select an answer first
2. 2foundation · easy

   Which approach best justifies the selection of a model from a hub for a given use case?

   • AComparing candidate models on relevant benchmarks and evaluating them against the specific task requirements
   • BChoosing the model that appears first in the search results
   • CPicking the model with the highest number of citations
   • DSelecting the model with the most recent update date
   Reveal Solution Discussion Select an answer first
4. 3foundation · easy

   When selecting a model from a hub, which criterion is most directly related to the model's ability to perform well on a specific task?

   • AThe size of the model in terms of parameters
   • BPerformance metrics, such as accuracy or F1 score on relevant benchmarks
   • CThe number of downloads or community stars
   • DThe license type, which determines how the model can be used commercially
   Reveal Solution Discussion Select an answer first
5. 4foundation · easy

   Which model selection criterion is most important when a model will be deployed on a device with limited memory and processing power?

   • ACommunity support, such as the number of open issues
   • BThe model's training dataset size
   • CLicense type, such as permissive vs. restrictive
   • DModel size, such as the number of parameters or file size
   Reveal Solution Discussion Select an answer first
6. 5application · medium

   A developer is choosing between two models on PyTorch Hub for a text classification task. Model A has a higher F1 score on the hub's benchmark but is not compatible with the developer's version of PyTorch. Model B has a slightly lower F1 score but is compatible. Which model should they choose?

   • AModel A, because the developer can upgrade PyTorch to make it compatible.
   • BModel A, because a higher F1 score is the most important criterion.
   • CModel B, because it is always more accurate in practice.
   • DModel B, because compatibility with the existing environment is a hard requirement.
   Reveal Solution Discussion Select an answer first