Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Ẹya Stores adanwo

Practice feature-store history, online freshness, and entity joins, including how shared definitions reduce skew without guaranteeing identical inputs.

Jẹmọ itọsọna awọn ọnaAwọn ẹya ara ẹrọ Stores
Ibeere 1 ti 8

Why use shared feature definitions for training and online serving?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Why use shared feature definitions for training and online serving?
    • To guarantee every cached value is always fresh
    • To reduce skew caused by separate transformation implementations
    • To eliminate the need for timestamps
    • To choose a GPU model automatically
  2. Kini iyatọ laarin aisinipo ati awọn ile itaja ori ayelujara?
    • Aisinipo gba data itan fun ikẹkọ; ori ayelujara n ṣe iranṣẹ awọn iye tuntun ni iyara fun awọn asọtẹlẹ laaye
    • Aisinipo wa fun awọn aworan, ori ayelujara jẹ fun ọrọ
    • Wọn jẹ aami kanna pẹlu awọn orukọ oriṣiriṣi
    • Aisinipo nṣiṣẹ ni awọn ipari ose, awọn ṣiṣe ori ayelujara ni awọn ọjọ ọsẹ
  3. Kini idi ti awọn idapọ-akoko-akoko ṣe pataki nigba kikọ data ikẹkọ?
    • Wọn jẹ ki ibi ipamọ data ṣiṣẹ ni iyara
    • Wọn tumọ awọn ẹya si awọn ede miiran
    • Wọn ṣe idiwọ jijo data nipa lilo awọn iye ẹya bi wọn ti wa ni akoko iṣẹlẹ kọọkan
    • Wọn dinku iwọn faili awoṣe
  4. Which storage pattern commonly supports low-latency feature lookup by entity key?
    • An archival tape read for every request
    • A printed document catalog
    • A model-weight-only file
    • A key-value online store such as Redis or DynamoDB
  5. Kini 'ẹya-ara' ni aaye yii?
    • Oniyipada igbewọle ti o jade lati inu data, bii lilo apapọ lori awọn ọjọ 30
    • Kokoro kan ninu koodu awoṣe
    • Ilana awọ ti Dasibodu kan
    • A iru ti GPU
  6. Anfaani afikun wo ni katalogi ẹya ti o pin pese?
    • O npa data atijọ rẹ laifọwọyi ni gbogbo wakati
    • O rọpo iwulo fun ikẹkọ awoṣe eyikeyi
    • O jẹ ki awọn ẹya ti a ṣe fun awoṣe kan ṣe awari ati tun lo nipasẹ awọn awoṣe miiran
    • O encrypts awọn ile-ile imeeli
  7. A feature’s event time precedes a loan decision, but the value only arrived afterward. What must a historical training join consider?
    • Only whether its column name is short
    • A rule that every past event was already known
    • Only the current online value
    • Whether that value was actually available at prediction time
  8. An online feature has not been refreshed after new events arrived. Which property should a feature-store monitor inspect?
    • The font size of the registry page
    • Feature freshness relative to the prediction’s needs
    • The model’s number of attention heads only
    • Whether the feature name contains a digit