Online uye Offline Feature Kushandira Skew Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Chii chinonzi kudzidzisa / kushumira skew?
   • Kana modhi ichidzidziswa paGPU asi yakashanda paCPU
   • Kana modhi yakakurisa kuti ikwane mundangariro
   • Kana maficha akaverengerwa kunze kwenyika panguva yekudzidziswa achisiyana kubva kune anopihwa online mukugadzira
   • Kana kudzidziswa kunotora nguva yakareba kupfuura yaitarisirwa
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. Ndechipi chezvinotevera chiri chikonzero chekare chekuwedzeredzwa kwechokwadi kunze kweindaneti kunodonha mukugadzirwa?
   • Kushandisa mienzaniso mishoma yekudzidzisa
   • Kudzidzira pamushini unokurumidza pane kushumira
   • Kuchengeta zvinhu mudhatabhesi
   • Kuburitswa kwedata, uko kudzidziswa kunoshandisa ruzivo rusingawanikwe panguva yekufanotaura
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. Sei chinhu chakavharirwa pamhepo senge 'maodha muawa yekupedzisira' iri njodzi ye skew?
   • Kukosha kwecached kunogona kuenda kwakare uye kusisaenderane neiyo ichangobva kuverengerwa kukosha inoshandiswa mukudzidziswa
   • Caching nguva dzose inovandudza kurongeka kwemuenzaniso
   • Caching inoita kuti chimiro chisakwanise kuverengera kunze kwenyika
   • Zvimiro zvakachengetwa hazvigoni kuva nhamba
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