Canary uye Shadow Deployments Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Chii chinotsanangura kutumirwa kwecanary?
   • Kuendesa diki muzana yehupenyu traffic kune iyo itsva vhezheni uchiiongorora
   • Kutumira traffic yese kushanduro itsva kamwechete
   • Kumhanyisa vhezheni itsva chete pakupera kwevhiki
   • Kutumira pasina chero chekutarisa
2. Chii chakakosha hunhu hwekuiswa kwemumvuri?
   • Mhinduro dzayo dzinopihwa kune hafu yevashandisi
   • Iyo inogamuchira miratidzo traffic asi mhinduro dzayo dzinoraswa, kwete kuratidzwa kune vashandisi
   • Inomhanya chete munharaunda yebudiriro
   • Inotsiva muenzaniso wekugadzira pakarepo
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. Ko canary neshadow deployments zvinopindirana sei?
   • Ndivo chinhu chimwe chete chine mazita akasiyana
   • Mvuri inosimbisa maitiro pane mhenyu traffic pasina njodzi yemushandisi; canary inosimbisa chaiyo mushandisi kukanganisa pane diki chidimbu
   • Canary inorasa mhinduro apo mumvuri uchivashandira
   • Izvo hazvisanganisire traffic yekugadzira
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