You have several AI applications that use an Azure Kubernetes Service (AKS) cluster. The cluster supports a maximum of 32 nodes.
You discover that occasionally and unpredictably, the application requires more than 32 nodes.
You need to recommend a solution to handle the unpredictable application load.
Which scaling method should you recommend?

A. horizontal pod autoscaler

B. cluster autoscaler

C. manual scaling

D. Azure Container Instances








 

Suggested Answer: B

B: To keep up with application demands in Azure Kubernetes Service (AKS), you may need to adjust the number of nodes that run your workloads. The cluster autoscaler component can watch for pods in your cluster that can't be scheduled because of resource constraints. When issues are detected, the number of nodes is increased to meet the application demand. Nodes are also regularly checked for a lack of running pods, with the number of nodes then decreased as needed. This ability to automatically scale up or down the number of nodes in your AKS cluster lets you run an efficient, cost-effective cluster.
Reference:
https://docs.microsoft.com/en-us/azure/aks/cluster-autoscaler

This question is in AI-100 Designing and Implementing an Azure AI Solution Exam
For getting Microsoft Certified: Azure AI Engineer Associate Certificate





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