Exam Associate Cloud Engineer topic 1 question 249 discussion

Horizontal Pod Autoscaler (HPA): It automatically scales the number of pods in a deployment, replica set, or stateful set based on observed CPU utilization (or, with custom metrics support, on some other application-provided metrics). This helps maintain availability by ensuring that your application has the necessary number of pods to handle the workload. Vertical Pod Autoscaler (VPA): It automatically adjusts the CPU and memory reservations for your pods to help "right size" your applications. This is particularly useful when you're unsure of the resource requirements. VPA makes recommendations for the appropriate CPU and memory settings based on usage patterns, which can be very effective for cost optimization. This combination ensures that your workload is both horizontally scalable (to handle changes in demand) and vertically optimized (to use resources efficiently), following Google-recommended practices for Kubernetes workloads.

I believe B is the best choice: HPA ensures availability by scaling the number of pods based on metrics (like CPU utilization). The VPA analyzes resource utilization and provides recommendations for CPU and memory requests and limits. This is key for right-sizing your pods for optimal cost efficiency.

D is the correct answer

Horizontal Pod Autoscaler (HPA): HPA automatically adjusts the number of pods in a deployment based on observed CPU utilization (or other select metrics). This is crucial for maintaining the availability of your workload, especially if the workload experiences varying levels of traffic or load. HPA ensures that there are enough pods to handle the load, scaling out (adding more pods) when demand is high and scaling in (removing pods) when demand is low. Vertical Pod Autoscaler (VPA) Recommendations: VPA automatically adjusts the CPU and memory reservations for pods in a deployment. It can operate in a mode where it only provides recommendations (without automatically applying them), which is useful for understanding the resource needs of your workload. VPA recommendations can guide you in setting appropriate CPU and memory limits based on the observed usage of your workload.

Ans is B B. Configure the Horizontal Pod Autoscaler for availability, and configure the Vertical Pod Autoscaler recommendations for suggestions. This approach allows you to manage the number of pods based on the workload (HPA) and get optimal CPU and memory settings for each pod (VPA), which is in line with Google-recommended practices for managing Kubernetes workloads with uncertain resource requirements. This combination ensures that your workload can function consistently in varying situations by automatically adjusting both the quantity of pods and the resources each pod is allocated.

ChatGPT says option D, By configuring VPA for resource recommendations based on actual usage patterns and HPA for scaling pod instances based on demand, you can ensure that your workload is both cost-effective and capable of adapting to varying resource requirements, all while following Google-recommended practices for Kubernetes workloads.