Canary ati Shadow Deployments adanwo

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

1. Kini asọye imuṣiṣẹ Canary kan?
   • Gbigbe ipin kekere ti ijabọ laaye si ẹya tuntun lakoko ṣiṣe abojuto rẹ
   • Fifiranṣẹ gbogbo ijabọ si ẹya tuntun ni ẹẹkan
   • Nṣiṣẹ ẹya tuntun nikan ni awọn ipari ose
   • Gbigbe laisi ibojuwo eyikeyi
2. Kini abuda bọtini ti imuṣiṣẹ ojiji?
   • Awọn idahun rẹ jẹ iranṣẹ si idaji awọn olumulo
   • O gba ijabọ digi ṣugbọn awọn idahun rẹ jẹ asonu, ko han si awọn olumulo
   • O ṣiṣẹ nikan ni agbegbe idagbasoke
   • O rọpo awoṣe iṣelọpọ lẹsẹkẹsẹ
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. Bawo ni Canary ati awọn imuṣiṣẹ ojiji ojiji ṣe iranlowo fun ara wọn?
   • Wọn jẹ ohun kanna pẹlu awọn orukọ oriṣiriṣi
   • Ojiji fọwọsi ihuwasi lori ijabọ ifiwe laisi eewu olumulo; Canary ṣe idaniloju ipa olumulo gidi lori bibẹ kekere kan
   • Canary sọ awọn idahun silẹ lakoko ti ojiji ṣe iranṣẹ wọn
   • Bẹni ko kan ijabọ iṣelọpọ
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