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OpenAI Câu đố

Kiểm tra xem các mô hình OpenAI khác với các sản phẩm và API như thế nào, tại sao các biện pháp kiểm soát dữ liệu lại dành riêng cho từng dịch vụ và cách đánh giá một ứng dụng đã định cấu hình.

Đường dẫn hướng dẫn liên quanOpenai
Câu hỏi 1 của 8

What distinguishes an OpenAI model from an application such as a chatbot?

Xem trước mọi câu hỏi8 câu hỏi

Câu trả lời và giải thích xuất hiện khi bạn làm bài kiểm tra.

  1. What distinguishes an OpenAI model from an application such as a chatbot?
    • The model is one component; the application may add instructions, retrieval, tools, and data handling
    • A model always includes a user interface
    • An application cannot use a model
    • The company name identifies every feature
  2. When choosing a model for an API task, what should a developer check?
    • A historical product announcement only
    • The name of a consumer application
    • The specific model and endpoint documentation for needed inputs and tools
    • A benchmark from a different configuration
  3. Why should an application be evaluated separately from its base model?
    • The base model always determines every outcome
    • Retrieval, tools, instructions, and data handling can change behavior
    • Applications cannot affect latency
    • An application removes the need for testing
  4. How should developers interpret API training use, abuse monitoring, and application-state retention?
    • As one blanket promise that applies to every product
    • As settings that never change
    • As controls determined only by model size
    • As distinct data-control topics that need service-specific review
  5. Why record whether a model alias or snapshot was used?
    • Versioning choices can affect update behavior and reproducibility
    • Aliases always refer to a fixed snapshot
    • Snapshots remove the need for a prompt
    • The model name determines data retention
  6. What should evaluation include before adopting an OpenAI configuration?
    • Only a general benchmark score
    • A single successful demonstration
    • Representative tasks and failure cases in the intended workflow
    • The model’s company history
  7. What does a historical model name establish about current availability?
    • That it is currently available in every endpoint
    • Nothing by itself; check current catalog and deprecation information
    • That current limits match old documentation
    • That the model is best for all workloads
  8. How should a factual answer from a model be checked?
    • Trust the answer if the model is confident
    • Ask the same model to certify itself
    • Assume retrieval makes every citation accurate
    • Preserve source evidence and verify that it supports the claim