Qaabka khadka tooska ah iyo khadka tooska ah ee u adeegaya Kediska Skew

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay tababbarka/u adeegidda skew?
   • Marka nooc lagu tababaro GPU laakiin loogu adeego CPU
   • Marka moodelku aad u weyn yahay oo aanu ku habboonayn xusuusta
   • Marka astaamaha lagu xisaabiyo offline inta lagu guda jiro tababarka ay ka duwan yihiin kuwa loogu adeego internetka ee wax soo saarka
   • Marka tababarku qaato waqti ka badan intii la filayay
2. When building a historical training row, which feature timestamps should a point-in-time join allow?
   • Values available at or before that row’s prediction time
   • Values recorded only after the outcome
   • The latest values regardless of historical time
   • Only values from the next calendar month
3. Midkee ka mid ah kuwan soo socda ayaa ah sababta caadiga ah ee sicir-bararka saxnaanta khadka ee ku burburta wax soo saarka?
   • Isticmaalka tusaalooyin tababar oo aad u yar
   • Tababarka mishiinka ka dheereeya adeega
   • Ku kaydinta sifooyinka kaydka xogta
   • Diidmada xogta, halkaas oo tababarku isticmaalo macluumaadka aan la heli karin wakhtiga saadaasha
4. A logged live feature distribution differs from training. What should the feature-serving team infer first?
   • The serving code is certainly defective
   • There is a mismatch to investigate; it may be drift or serving skew
   • The feature has no predictive value
   • The model must be running on different hardware
5. Maxay tahay sababta sifada khadka tooska ah ee la kaydiyay sida 'dalabka saacada ugu dambeeya' ay khatar u tahay?
   • Qiimaha la kaydiyay wuu jiri karaa oo kuma ekaan karo qiimihii dhawaan la xisaabiyay ee loo isticmaalay tababarka
   • Kaydinta had iyo jeer waxay hagaajisaa saxnaanta moodeelka
   • Kaydinta waxay ka dhigtaa sifada mid aan macquul ahayn in lagu xisaabiyo offline
   • Tilmaamaha la kaydiyay ma noqon karaan tiro ahaan
6. Why share one feature definition across batch training and online serving?
   • To guarantee every cached value is fresh
   • To eliminate changes in incoming populations
   • To make timestamp checks unnecessary
   • To reduce differences caused by separate transformation implementations
7. A transaction model uses a chargeback flag recorded days after purchase. Why is its offline feature join invalid?
   • The flag is categorical rather than numerical
   • The flag was unavailable when the purchase prediction was made
   • Chargeback flags cannot be stored historically
   • Only GPU models may use transaction features
8. Training recomputes a one-hour order count, but serving reads a ten-minute-old cached count. Which check targets this skew?
   • Increase the number of model parameters
   • Remove all timestamps from the feature store
   • Compare counts for matching entities and times while recording cache freshness
   • Switch both paths to a different programming language