KCNA Cloud Native Architecture Practice Question
An organization wants to implement a serverless function that scales to zero when not in use. Which technology is specifically designed to achieve this on Kubernetes?
⚠ Common exam trap
The trap is picking Kubernetes HPA because it sounds like the native scaling solution — candidates forget that HPA cannot scale to zero and lacks request-driven activation, which is the defining serverless requirement.
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
Knative is a Kubernetes-based platform specifically designed to run serverless workloads, and its Serving component includes scale-to-zero as a core feature — pods are removed when there is no traffic and recreated on demand. It builds on Kubernetes primitives but adds the autoscaling and request-driven activation needed for true serverless behavior. This makes it the purpose-built answer for scale-to-zero functions on Kubernetes.
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
Why this is correct
Knative's Serving component manages request-driven workloads through its autoscaler, which scales pods down to zero when no traffic arrives and spins up on demand via the Activator. This directly satisfies the stem's scale-to-zero constraint, unlike standard Kubernetes Deployments, which maintain a minimum replica count.
- ✗
Prometheus
Why it's wrong here
Prometheus is a monitoring and alerting toolkit; it scrapes metrics and stores time series, and cannot run functions or scale workloads to zero. It is tempting because it is a core CNCF observability component often deployed alongside serverless platforms, but it provides no execution or autoscaling capability.
- ✗
Istio
Why it's wrong here
Istio is a service mesh providing traffic routing, mTLS and observability between pods, not a function runtime; it cannot scale workloads to zero. It is tempting because it deploys on Kubernetes and manages traffic, but the correct technology is Knative Serving, which builds on Istio while adding request-driven autoscaling to zero.
- ✗
Kubernetes Horizontal Pod Autoscaler (HPA)
Why it's wrong here
HPA adjusts replica counts between a configured minimum and maximum; it cannot scale to zero, and it manages pods rather than event-driven functions. It is tempting because HPA is the standard Kubernetes autoscaling mechanism, and it would be correct for scaling a long-running deployment based on CPU or custom metrics.
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 |
Go deeper
Related to this question
Learn chapter
Kubernetes API and Core Objects
Key term
Horizontal Pod Autoscaling
Horizontal Pod Autoscaling automatically adjusts the number of pod replicas in a Kubernetes cluster based on observed CPU, memory, or custom metrics.
Key term
ReplicaSet and Replication
A ReplicaSet ensures a specified number of identical pod instances are running at all times in Kubernetes, using replication to maintain availability and stability.
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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.