Maswali ya Kugundua Drift ya Mfano

Majibu na maelezo huonekana unapojibu maswali.

1. Kuna tofauti gani kati ya kusogea kwa data na kusogea kwa dhana?
   • Data drift hubadilisha usambazaji wa pembejeo; dhana drift hubadilisha uhusiano wa ingizo hadi pato
   • Ni majina mawili ya kitu kimoja
   • Uhamisho wa data huathiri tu picha; dhana drift huathiri maandishi pekee
   • Data drift hutokea katika mafunzo; dhana drift hutokea katika kupima
2. How should a team interpret a PSI value such as 0.30?
   • As a guarantee that model accuracy has fallen
   • As a signal to investigate under the chosen monitoring policy, not a universal proof of harmful drift
   • As an identical threshold for every feature and population
   • As proof that retraining must begin immediately
3. Kwa nini utambuzi wa utelezi unaotegemea pembejeo unaweza kutoa onyo la mapema?
   • Kwa sababu pembejeo huwa sio sahihi kwanza
   • Kwa sababu pembejeo ni faili ndogo
   • Kwa sababu lebo za ukweli wa msingi mara nyingi hufika baadaye sana kuliko utabiri
   • Kwa sababu mifano hupuuza lebo kabisa
4. What does a two-sample Kolmogorov-Smirnov test compare?
   • The average accuracy of two classifiers only
   • The class labels predicted by a single model
   • The parameter values of two neural networks
   • The empirical cumulative distribution functions of two samples
5. Ni hali gani ni mfano wazi wa dhana ya kuteleza?
   • Kile ambacho watumiaji huzingatia kuwa 'barua taka' hubadilika kwa wakati hata kwa barua pepe zinazofanana
   • Kamera hutoa picha angavu zaidi
   • Umri wa wateja katika mkusanyiko wa data hubaki sawa
   • Seti ya data imehifadhiwa katika muundo mpya wa faili
6. What is a safe first response when an input-drift monitor triggers?
   • Automatically deploy a newly retrained model without evaluation
   • Investigate the data and impact, then decide whether retraining or another action is justified
   • Delete the reference data
   • Assume every shift is harmful concept drift
7. What can TensorFlow Data Validation check in an ML pipeline?
   • The causal effect of a feature
   • Whether every model prediction is correct
   • Data schemas and statistics, including unexpected changes
   • The absolute accuracy of an unlabeled dataset
8. What does the Population Stability Index (PSI) measure in a drift-monitoring setup?
   • The model’s accuracy on labeled examples
   • A causal effect of an input feature
   • A universal pass/fail threshold for all models
   • A numerical summary of how binned feature distributions differ between reference and current data