引导式测验 · 中等 等级
金丝雀和影子部署测验
比较金丝雀和影子版本、流量处理、监控、工作负载成本、隐私控制和安全部署准备。
问题 1 的 8
金丝雀部署的定义是什么？
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- 金丝雀部署的定义是什么？
- 在监控的同时将一小部分实时流量路由到新版本
- 立即将所有流量发送到新版本
- 仅在周末运行新版本
- 无需任何监控即可部署
- 影子部署的关键特征是什么？
- 它的响应服务于一半的用户
- 它接收镜像流量，但其响应被丢弃，不会向用户显示
- 仅在开发环境中运行
- 它立即取代了生产模型
- 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
- 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
- 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
- 金丝雀部署和影子部署如何相辅相成？
- 它们是相同的东西，但名称不同
- Shadow 验证实时流量的行为，无需用户承担风险；金丝雀验证一小部分的真实用户影响
- 金丝雀丢弃响应，而影子为它们提供服务
- 都不涉及生产流量
- 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
- 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