Quick Answer
The answer is Kubernetes Event-Driven Autoscaler (KEDA). This is the correct component because it acts as a custom metrics adapter that integrates with the Kubernetes Horizontal Pod Autoscaler (HPA), enabling EKS autoscaling based on SQS queue depth—a custom metric that the standard HPA cannot natively read. KEDA listens to external event sources like Amazon SQS, scaling pods up or down in direct response to the number of messages in the queue, which is precisely the requirement for event-driven workloads. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this scenario tests your understanding of how to extend Kubernetes autoscaling beyond CPU and memory metrics; a common trap is selecting the standard HPA alone, which cannot ingest SQS metrics without an adapter. Remember the memory tip: KEDA is the “key” that unlocks event-driven scaling—think “KEDA = Queue Depth Adapter.”
This SAP-C02 practice question tests your understanding of design for new solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Kubernetes Event-Driven Autoscaler (KEDA)
KEDA is designed for event-driven scaling.
Kubernetes Horizontal Pod Autoscaler (HPA) with Prometheus
Why wrong: HPA can use custom metrics but KEDA is simpler for SQS.
Kubernetes Cluster Autoscaler
Why wrong: Cluster Autoscaler scales nodes, not pods.
AWS Auto Scaling with target tracking
Why wrong: AWS Auto Scaling is for EC2 instances, not Kubernetes pods.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Kubernetes Horizontal Pod Autoscaler (HPA) with Prometheus
Why it's wrong here
HPA can use custom metrics but KEDA is simpler for SQS.
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.
Common exam traps
The trap here is that 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.
Detailed technical explanation
KEDA works by deploying a custom metrics adapter that scrapes the SQS queue depth via the AWS SDK and exposes it as a custom metric in the Kubernetes metrics API. The HPA then uses this metric to scale the deployment's replica count, with KEDA supporting scaling to zero if no messages are present, which is a key differentiator from standard HPA that requires at least one replica. In a real-world scenario, this is critical for cost optimization in batch processing workloads where the queue may be idle for extended periods.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Design for New Solutions — This question tests Design for New Solutions — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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