Guide-yakabatana mibvunzo · Pakati Nhanho

Feature Stores Quiz

Dzidzira nhoroondo-yechitoro chenhoroondo, kutsva kwepamhepo, uye mubatanidzwa kujoinha, kusanganisira kuti tsananguro dzakagovaniswa dzinoderedza sei skew pasina vimbiso yekupinda zvakafanana.

Nzira dzekutungamira dzinoenderanaFeature Stores
Mubvunzo 1 ye 8

Why use shared feature definitions for training and online serving?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  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. Ndeupi musiyano uripo pakati pezvitoro zvepamhepo neinternet?
    • Offline inobata nhoroondo data yekudzidziswa; online inoshandira zvazvino hunhu nekukasira kune hupenyu hwekufungidzira
    • Offline ndeyemifananidzo, online ndeyemavara
    • Iwo akafanana nemazita akasiyana
    • Offline inomhanya kupera kwevhiki, online inomhanya pakati pevhiki
  3. Sei kujoinha-mu-nguva kwakakosha pakuvaka data rekudzidzisa?
    • Vanoita kuti database iite nekukurumidza
    • Vanoturikira zvinhu mune mimwe mitauro
    • Ivo vanodzivirira kudonha kwedata nekushandisa hunhu hwemhando sezvazvaive panguva yechiitiko chega chega
    • Vanoderedza saizi yefaira remuenzaniso
  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. Chii chinonzi 'chimiro' muchirevo chechinyorwa chino?
    • Musiyano wekuisa unobva kudata, seavhareji yekushandisa kwemazuva makumi matatu
    • A bug mumodhi code
    • Iyo color scheme yedashboard
    • Mhando yeGPU
  6. Ndeipi imwe bhenefiti inopihwa neakagovaniswa chikamu katalogi?
    • Iyo inodzima data yekare otomatiki awa yega yega
    • Inotsiva kudiwa kwemhando ipi neipi yekudzidziswa
    • Inobvumira maficha akavakirwa kune imwe modhi kuwanikwa uye kushandiswazve nemamwe mamodheru
    • Iyo encrypts email yekambani
  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