The suggested answer is B.
To process large bursts of data efficiently on a cloud-based Kubernetes cluster, leveraging the Kubernetes Cluster Autoscaler is the most cost-effective method. This approach allows the cluster to automatically start and stop nodes based on demand. During peak times, such as when the Monday morning batch jobs need to run, the cluster will scale up to provide the necessary resources. Once the jobs are completed, the cluster will scale down, ensuring that you only pay for the resources when they are needed, thus avoiding over-provisioning and reducing costs.