Kubernetes and Cloud Native Associate (KCNA) — Question 7

Let's assume that an organization needs to process large amounts of data in bursts, on a cloud-based Kubernetes cluster. For instance: each Monday morning, they need to run a batch of 1000 compute jobs of 1 hour each, and these jobs must be completed by Monday night. What's going to be the most cost-effective method?

Answer options

• A. Run a group of nodes with the exact required size to complete the batch on time, and use a combination of taints, tolerations, and nodeSelectors to reserve these nodes to the batch jobs.
• B. Leverage the Kubernetes Cluster Autoscaler to automatically start and stop nodes as they're needed.
• C. Commit to a specific level of spending to get discounted prices (with e.g. “reserved instances” or similar mechanisms).
• D. Use PriorityСlasses so that the weekly batch job gets priority over other workloads running on the cluster, and can be completed on time.

Correct answer: B

Explanation

The correct answer is B because using the Kubernetes Cluster Autoscaler allows for the dynamic adjustment of resources, which is the most cost-effective way to manage fluctuating workloads. Option A may lead to over-provisioning of resources, option C involves a commitment that may not be necessary, and option D, while helpful for prioritization, does not address the cost-effectiveness of resource allocation.