引导式测验 · 硬 等级

AI模型监控测验

测试漂移信号、延迟标签、亚组证据、原因诊断、警报所有权、恢复选择和后续检查。

相关引导路径AI模型监控
问题 1 的 8

A feature’s input distribution shifts after a product change. What does this establish?

预览每个问题8 问题

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  1. A feature’s input distribution shifts after a product change. What does this establish?
    • That the model must be retrained immediately
    • That the new data are invalid
    • A reason to investigate, not automatic proof that accuracy declined
    • That task quality improved
  2. Why track missing fields and invalid ranges before outcome labels arrive?
    • They measure accuracy directly
    • They replace delayed outcome evaluation
    • They prove the model is biased
    • They can expose pipeline problems before predictive quality can be measured
  3. An unlabeled drift metric rises. What is still needed to establish task performance?
    • Reliable outcome-based evaluation
    • A larger dashboard
    • A second drift metric
    • A new prompt template
  4. Why inspect label collection delays and selection?
    • Labels always arrive immediately
    • Delayed or selective labels can make performance evidence look more complete than it is
    • Only model outputs need versioning
    • A delayed label is automatically a bad outcome
  5. A feature definition changed and the model’s inputs shifted. What should happen before retraining?
    • Assume all drift requires retraining
    • Ignore the shift if latency is stable
    • Investigate the cause and determine whether the shift is a data or software defect
    • Change the alert threshold until it disappears
  6. What should a monitoring alert process specify?
    • Only a chart color
    • The model’s preferred action
    • A guarantee that drift will not occur
    • Thresholds, review ownership, and the recovery decision
  7. After retraining, how should the intervention be checked?
    • Evaluate on appropriate material and continue measuring afterward
    • 仅比较训练准确率
    • Assume retraining fixed the cause
    • Stop monitoring the old and new versions
  8. Why include subgroup results and sample sizes in monitoring reports?
    • Averages already provide complete subgroup information
    • They show which populations the reported performance actually covers
    • Sample sizes never affect interpretation
    • Subgroups can be inferred from the model score