Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Sida cad AI ee Kediska Dabagalka Model-Isha Furan

Faham waxa sida cad warbixinaha ay tijaabin karaan, sida xog-ururinta tixraacu u qaabeeyaan natiijooyinka leexashada iyo sababta warbixinnada aan la calaamadin u caddayn karin saxnaanta.

Wadooyinka haga ee la xidhiidhaSida muuqata AI Model Monitoring
Su'aal 1 ee 8

What does a reference-versus-current drift report compare?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. What does a reference-versus-current drift report compare?
    • Selected data distributions across two periods or datasets
    • Model accuracy without target labels
    • Only the training loss curve
    • The deployment and source-code branches
  2. Why should a report record its reference window and feature selection?
    • They determine the model's hidden layers
    • Those choices affect which changes are measured and how results are interpreted
    • They ensure every drift score is a p-value
    • They eliminate sample-size effects
  3. What can an unlabeled drift report establish?
    • That the model is unfair
    • That prediction accuracy has declined
    • A change in selected data distributions under configured methods
    • That the new data are incorrect
  4. Which expectation does a data contract test verify?
    • Whether labels arrive immediately
    • Whether every model output is correct
    • Whether drift is always harmful
    • Whether inputs satisfy specified schema or quality expectations
  5. Why can a strict drift threshold create noisy alerts?
    • The library ignores all features
    • Expected seasonality or sampling variation can trigger a change signal
    • Drift reports include ground truth automatically
    • Reference data are never needed
  6. How should delayed target labels be used in monitoring?
    • Use unlabeled inputs as a substitute for outcome truth
    • Treat missing recent labels as incorrect predictions
    • Join them to predictions with matching model version and outcome window
    • Drop all historical predictions
  7. Why version the report configuration and library version?
    • It changes the reference distribution
    • It guarantees the same input data forever
    • It removes the need for labels
    • Defaults and methods can change, affecting reproducibility
  8. How should a team respond to a failed data-quality test?
    • Investigate whether the input violates a meaningful contract or reflects an expected change
    • Automatically retrain on the data
    • Ignore the test for every future run
    • Declare the model inaccurate without labels