KCNA Cloud Native Architecture Practice Question
Which TWO of the following are common characteristics of serverless computing? (Choose two.)
⚠ Common exam trap
The trap is selecting options that sound 'efficient' (manual scaling, dedicated servers) because they are familiar from traditional infrastructure — candidates must remember that serverless is defined by automatic elasticity to zero and event-driven invocation, not by any form of pre-provisioned capacity.
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
✓
Auto-scaling to zero when idle
Option A (Auto-scaling to zero when idle) is correct because serverless platforms such as AWS Lambda, Azure Functions, and Google Cloud Functions provision no compute instances when there are no invocations, so you pay nothing during idle periods and capacity scales down to zero automatically. Option B (Event-driven execution) is correct because serverless functions are invoked in response to events — HTTP requests via API Gateway, object uploads to S3, messages in SQS, database changes, or scheduled timers — rather than running continuously. Option C is incorrect because manual scaling based on predicted load describes traditional provisioned servers or reserved capacity, not the automatic, demand-based scaling of serverless. Option D is incorrect because serverless abstracts away servers entirely; there are no always-on dedicated servers allocated to the customer. Option E is incorrect because serverless functions are designed to be short-lived and stateless, with state externalized to services like DynamoDB, S3, or Redis rather than held in long-running processes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Auto-scaling to zero when idle
Why this is correct
Auto-scaling to zero when idle is a defining trait of serverless platforms such as AWS Lambda and Azure Functions, where the provider provisions no instances during inactivity. This satisfies the stem's demand for common characteristics, since consumption-based billing and event-driven invocation both depend on capacity dropping to zero rather than idling at a baseline.
- ✓
Event-driven execution
Why this is correct
Serverless functions are invoked in response to events such as HTTP requests, queue messages or file uploads, rather than running continuously. This event-driven execution satisfies the pay-per-invocation model, since no compute is consumed while the function sits idle awaiting a trigger.
- ✗
Manual scaling based on predicted load
Why it's wrong here
Serverless platforms scale automatically and elastically in response to demand, so manual scaling contradicts the model. It is tempting because manual scaling is a legitimate cloud pattern, and would be correct for provisioned EC2 or container workloads where capacity is planned ahead of predicted load.
- ✗
Always-on dedicated servers
Why it's wrong here
Serverless computing abstracts servers entirely and bills per invocation, so always-on dedicated hardware contradicts it. It is tempting because dedicated servers offer predictable performance, and would be correct for steady, high-utilisation workloads where reserved instances reduce cost.
- ✗
Long-running stateful processes
Why it's wrong here
Serverless functions have execution time limits and are stateless by design, so long-running stateful processes break the model. It is tempting because stateful workloads are common, and would be correct for persistent services hosted on EC2 or containers with attached storage.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
Related to this question
Learn chapter
Cloud Native Application Design Principles
Key term
Serverless computing
Serverless computing is a cloud execution model where the cloud provider dynamically manages the allocation and provisioning of servers, allowing developers to write and deploy code without thinking about the underlying infrastructure.
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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.