The Horizontal Pod Autoscaler (HPA) is a core Kubernetes feature designed to automatically scale the number of Pod replicas in a workload based on observed metrics, making option A the correct answer. Its primary goal is to ensure that applications can handle varying levels of demand while maintaining performance and resource efficiency.
HPA works by continuously monitoring metrics such as CPU utilization, memory usage, or custom and external metrics provided through the Kubernetes metrics APIs. Based on target thresholds defined by the user, the HPA increases or decreases the number of replicas in a scalable resource like a Deployment, ReplicaSet, or StatefulSet. When demand increases, HPA adds more Pods to handle the load. When demand decreases, it scales down Pods to free resources and reduce costs.
Option B is incorrect because tracking performance metrics and reporting health status is handled by components such as the metrics-server, monitoring systems, and observability tools—not by the HPA itself. Option C is incorrect because rolling updates are managed by Deployment strategies, not by the HPA. Option D is incorrect because persistent volume management is handled by Kubernetes storage resources and CSI drivers, not by autoscalers.
HPA operates at the Pod replica level, which is why it is called “horizontal” scaling—scaling out or in by changing the number of Pods, rather than adjusting resource limits of individual Pods (which would be vertical scaling). This makes HPA particularly effective for stateless applications that can scale horizontally to meet demand.
In practice, HPA is commonly used in production Kubernetes environments to maintain application responsiveness under load while optimizing cluster resource usage. It integrates seamlessly with Kubernetes’ declarative model and self-healing mechanisms.
Therefore, the correct and verified answer is Option A, as the Horizontal Pod Autoscaler’s primary function is to automatically scale Pod replicas based on resource utilization and defined metrics.
