Maswali ya Usambazaji wa Canary na Kivuli

Majibu na maelezo huonekana unapojibu maswali.

1. Nini hufafanua kupelekwa kwa canary?
   • Kuelekeza asilimia ndogo ya trafiki ya moja kwa moja hadi toleo jipya huku ukilifuatilia
   • Inatuma trafiki yote kwa toleo jipya mara moja
   • Kuendesha toleo jipya mwishoni mwa wiki pekee
   • Kusambaza bila ufuatiliaji wowote
2. Ni nini sifa kuu ya uwekaji kivuli?
   • Majibu yake yanatolewa kwa nusu ya watumiaji
   • Inapokea trafiki inayoakisiwa lakini majibu yake hutupwa, hayaonyeshwi kwa watumiaji
   • Inaendesha tu katika mazingira ya maendeleo
   • Inachukua nafasi ya mtindo wa uzalishaji mara moja
3. What does a shadow deployment protect users from, and what can it still cost?
   • It uses no extra resources and cannot access user data
   • Its predictions are returned to every user
   • Candidate responses are withheld, but mirrored inference still consumes compute and needs side-effect controls
   • It guarantees the candidate has no production impact of any kind
4. How does user-facing traffic differ between a canary and a shadow?
   • Both always send candidate output to every user
   • A shadow deployment never uses production inputs
   • A canary is only an offline test
   • A canary serves candidate responses to a defined live-traffic segment; a shadow typically does not
5. What should a team define before starting a canary or shadow rollout?
   • Traffic rules, monitoring thresholds, rollout duration, and a rollback owner
   • A promise that the candidate will outperform production
   • A fixed traffic percentage that must never change
   • A requirement to expose every user to the candidate
6. Je! Usambazaji wa canary na vivuli unakamilishanaje?
   • Wao ni kitu kimoja na majina tofauti
   • Kivuli huthibitisha tabia kwenye trafiki ya moja kwa moja bila hatari ya mtumiaji; canary inathibitisha athari halisi ya mtumiaji kwenye kipande kidogo
   • Canary hutupa majibu wakati kivuli kinawahudumia
   • Wala haihusishi trafiki ya uzalishaji
7. What should an operator do before mirroring production requests to a shadow model?
   • Allow the shadow model to execute writes twice
   • Assume copied requests contain no sensitive data
   • Isolate side effects and protect the handling of copied request data
   • Disable all monitoring of the candidate
8. What does a canary reveal that a shadow deployment does not directly measure?
   • Whether candidate inference consumes compute
   • How its predictions compare offline
   • Whether a model can load in the serving stack
   • How the candidate affects users who are actually served by it