引导式测验 · 简单 等级

AI 评估基础知识测验

设定验收标准，将开发与保留的数据分开，与基线进行比较，选择任务适合性检查，并记录不确定性和限制。

相关引导路径人工智能评估基础知识
问题 1 的 8

在评估人工智能系统之前，团队应该定义什么？

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  1. 在评估人工智能系统之前，团队应该定义什么？
    • Acceptance criteria for inputs, expected outputs, tolerable errors, and escalation
    • A press release about model quality
    • A single average score
    • A deployment date only
  2. 开发示例与保留的评估集有何不同？
    • Use the held-out set for prompt tuning every day
    • Use development examples for iteration and reserve held-out examples to test choices
    • Mix both sets before every run
    • Keep no record of which examples were used
  3. 当一个团队反复调整其最终测试集时会发生什么？
    • The test set becomes larger
    • The score becomes a baseline
    • The test set becomes another development set
    • The model automatically generalizes better
  4. 哪个比较可以显示增加的模型复杂性是否能带来实际好处？
    • Compare only with last week’s best score
    • Compare with a simple baseline
    • Compare against a random deployment date
    • Compare with a different task’s benchmark
  5. 对于总结者，指南建议进行哪些评估检查？
    • Server uptime and account age
    • Pixel similarity and frame rate
    • Factual consistency and coverage
    • 训练纪元数
  6. How should a team verify an AI agent’s completed action?
    • Trust its success message
    • Count how many tools it called
    • Check the authoritative final state and unintended side effects
    • Ask the same model whether it succeeded
  7. Why might a rare failure deserve more attention than many harmless wording differences?
    • Consequences of the failure can outweigh frequent low-impact variations
    • Rare events are always measurement errors
    • Wording differences cannot be evaluated
    • The average score must be ignored
  8. What should an evaluation document about its limitations?
    • That every real-world case was tested
    • Where the evaluation does not represent actual use
    • Only the name of the model
    • That uncertainty has been eliminated