KCNA Cloud Native Architecture Practice Question
In a serverless architecture using Knative, what happens when a function finishes processing an event and there are no pending events?
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
✓
The function instance is automatically scaled down to zero replicas
Knative scales the function to zero replicas when idle, which is a key feature of serverless platforms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The function instance is automatically scaled down to zero replicas
Why this is correct
Knative's autoscaler, via the Pod Autoscaler and Activator, reduces the deployment to zero replicas when no events are pending, eliminating idle compute cost. The revision remains registered so the Activator can cold-start a replica when the next event arrives, matching the stem's zero-pending-events condition.
- ✗
The function instance is terminated and the container image is deleted
Why it's wrong here
Knative scales the pod to zero, but the container image persists in the registry for later cold starts; it is never deleted on scale-down. Image deletion describes registry lifecycle policies or garbage collection, not request-driven autoscaling behaviour.
- ✗
The function continues to run but stops listening for events
Why it's wrong here
Knative's queue-proxy and autoscaler scale the revision to zero replicas when no requests arrive, so nothing keeps running. A process that stays alive but stops listening describes a paused consumer, not Knative's request-driven scale-to-zero model.
- ✗
The function instance remains running for a configurable idle timeout
Why it's wrong here
Knative Serving scales idle revisions to zero by default, so instances do not linger for an idle timeout. A configurable idle period describes AWS Lambda's execution environment reuse, where a warm container survives briefly between invocations to reduce cold-start latency.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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