KCNA - Cloud Native Architecture - Section 4.3

Describe serverless and event-driven patterns in the cloud native ecosystem.

Functions as a service, scale-to-zero, event-driven autoscaling with KEDA, and Knative Serving and Eventing. Also when a serverless model is a poor fit.

serverlessKnativeKEDAscale to zeroevent-driven architectureCloudEvents

Practice question for this objective

Free sampleCloud Native Architecturemedium

A team wants a framework that runs HTTP request-driven services, automatically scales their replicas up and down with traffic including down to zero when idle, and manages revision-based traffic splitting between versions of a service. Which cloud native project is purpose-built for this serving model?

  • AKEDA, an add-on that reads external event sources and drives autoscaling of existing workloads based on those signals.
  • BPrometheus, a monitoring system that collects time-series metrics and evaluates alerting rules against them.
  • CKnative, which runs request-driven services, scales them with traffic including to zero, and routes traffic across service revisions. Correct
  • DCloudEvents, a specification that defines a common metadata format so events can be described consistently across producers.
Identify Knative as the serving framework that runs request-driven services with autoscaling to zero and revision-based traffic routing. Knative Serving is designed to run stateless, request-driven workloads: it autoscales replicas based on incoming request demand, can scale a service to zero when there is no traffic, and models each deployment as a revision so traffic can be split or shifted between versions.

Why A is wrong: Tempting because KEDA also enables scale to zero, but it is an event-driven autoscaler layered onto existing workloads; it does not itself provide an HTTP serving framework with revision-based traffic routing.

Why B is wrong: Tempting because its metrics can feed autoscaling decisions, but Prometheus is an observability and alerting system and does not run application services or route request traffic between revisions.

Why C is correct: Correct: Knative Serving provides exactly this model, deploying stateless request-driven services, autoscaling them on request concurrency down to zero, and managing revisions with configurable traffic splitting.

Why D is wrong: Tempting in an event-driven context, but CloudEvents is a data-format specification for event metadata, not a runtime that hosts services or performs autoscaling and traffic routing.

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