Canary and Shadow Deployments Quiz

Answers and explanations appear as you take the quiz.

1. What defines a canary deployment?
   • Routing a small percentage of live traffic to the new version while monitoring it
   • Sending all traffic to the new version at once
   • Running the new version only on weekends
   • Deploying without any monitoring
2. What is the key characteristic of a shadow deployment?
   • Its responses are served to half the users
   • It receives mirrored traffic but its responses are discarded, not shown to users
   • It only runs in the development environment
   • It replaces the production model immediately
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. How do canary and shadow deployments complement each other?
   • They are the same thing with different names
   • Shadow validates behavior on live traffic without user risk; canary validates real user impact on a small slice
   • Canary discards responses while shadow serves them
   • Neither involves production traffic
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