SAP-C02 Design for New Solutions Practice Question
A company is designing a new application that will run on Amazon EKS. The application must be able to scale based on custom metrics such as number of messages in an SQS queue. Which Kubernetes component should be used to achieve this?
⚠ Common exam trap
Candidates often confuse the standard Horizontal Pod Autoscaler (HPA) with the ability to scale based on any custom metric, but the HPA alone cannot ingest external metrics like SQS queue depth without a custom metrics adapter such as KEDA.
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
✓
Kubernetes Event-Driven Autoscaler (KEDA)
Kubernetes Event-Driven Autoscaler (KEDA) is the correct component because it is specifically designed to scale Kubernetes workloads based on external event sources like Amazon SQS queue depth. KEDA acts as a custom metrics adapter that integrates with the Kubernetes Horizontal Pod Autoscaler (HPA), allowing the application to scale pods dynamically based on the number of messages in the SQS queue, which is a custom metric not natively supported by the standard HPA.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Kubernetes Event-Driven Autoscaler (KEDA)
Why this is correct
KEDA is designed for event-driven scaling.
- ✗
Kubernetes Horizontal Pod Autoscaler (HPA) with Prometheus
Why it's wrong here
HPA with Prometheus is unsuitable here because while HPA performs horizontal autoscaling, and Prometheus collects metrics, this combination does not natively provide the mechanism to consume *external* custom metrics like SQS queue length for scaling. Prometheus would typically collect metrics from within the cluster or scrape targets. This option is tempting because HPA is the standard Kubernetes autoscaler, and Prometheus is a common monitoring solution. It would be appropriate for scaling based on internal application metrics exposed and collected by Prometheus within the cluster.
- ✗
Kubernetes Cluster Autoscaler
Why it's wrong here
Cluster Autoscaler scales nodes, not pods.
- ✗
AWS Auto Scaling with target tracking
Why it's wrong here
AWS Auto Scaling is for EC2 instances, not Kubernetes pods.
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