Correct Answer: Do not run HPA and VPA together on the same resource metrics (like CPU or memory), as their scaling actions will conflict
Running HPA and VPA together on the same metrics (like CPU and Memory) can lead to conflicts. For example, when CPU usage spikes, VPA may attempt to scale up the pod's CPU allocati...
Correct Answer
Do not run HPA and VPA together on the same resource metrics (like CPU or memory), as their scaling actions will conflict
Why This Answer Is Correct
Running HPA and VPA together on the same metrics (like CPU and Memory) can lead to conflicts. For example, when CPU usage spikes, VPA may attempt to scale up the pod's CPU allocation, while HPA attempts to create more replicas. You can only combine them if HPA is configured to scale based on custom/external metrics (not CPU/Memory) while VPA manages resource requests/limits.
This question evaluates your knowledge of Kubernetes Autoscaling — a core concept tested in cloud certification exams. Understanding Kubernetes Autoscaling is essential for designing scalable, secure, and cost-effective cloud architectures. Mastery of this topic will help you answer scenario-based questions confidently on exam day.
What does a HorizontalPodAutoscaler (HPA) do when CPU utilization exceeds the defined target?
You want to scale the number of replicas in your frontend deployment based on custom metrics retrieved from Prometheus, rather than standard CPU or Memory utilization. Which Kubernetes autoscaling component should you use?
During traffic peaks, your Horizontal Pod Autoscaler (HPA) scales out replicas rapidly. However, when traffic dips briefly, the HPA immediately scales down, causing resource thrashing. How can you configure HPA to delay scaling down and stabilize replica count?
You are running a stateful pod that performs long-running computations. You want to ensure that the Cluster Autoscaler (CA) does not terminate the node hosting this specific pod during a cluster scale-down event. Which annotation should you apply to the pod?