KCNA Cloud Native Architecture Practice Question
A team is building a serverless application using Knative. They want the application to scale to zero when idle. Which Knative resource type should they use?
⚠ Common exam trap
KCNA often tests the Serving vs Eventing boundary — candidates pick Eventing or Trigger because 'serverless' sounds event-driven, but scale-to-zero for request-driven apps is a Serving feature.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Knative Serving
Knative Serving is the component that manages request-driven workloads and provides scale-to-zero (and scale-from-zero) capabilities. It deploys containerized services with automatic scaling based on incoming requests, including scaling down to zero replicas when idle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Knative Serving
Why this is correct
Knative Serving provides request-driven autoscaling, including scale-to-zero, by routing traffic through the Activator and manipulating Kubernetes Deployments via the autoscaler. This directly satisfies the stem's idle scale-to-zero constraint, which Knative Eventing and other resource types cannot deliver.
- ✗
Knative Trigger
Why it's wrong here
A Trigger routes events to a Broker subscriber; it does not run or scale workloads. Triggers are tempting in event-driven designs, but scale-to-zero belongs to Knative Serving, whose Service and Revision resources manage pod lifecycle and idle scaling.
- ✗
Knative Build
Why it's wrong here
Knative Build was a deprecated precursor to Tekton, used for building container images from source, and never handled request-driven scaling. It is tempting as a Knative component, but scale-to-zero is provided by Knative Serving's Service and Revision resources.
- ✗
Knative Eventing
Why it's wrong here
Knative Eventing routes CloudEvents between producers and consumers; it does not manage request-driven workload scaling. Scale-to-zero is a Serving function, achieved by Knative Service revisions with the autoscaler. Eventing would be correct when building event-driven pipelines that fan messages out to sinks.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CNCF exam blueprint
This KCNA practice question is part of Courseiva's free CNCF certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the KCNA exam.