Databricks Certified Data Engineer - Professional
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When deploying Spark on Kubernetes with highly variable workloads, which strategy dynamically allocates resources to Spark executors to match real-time demand without over-provisioning?
A
Configure horizontal pod autoscaling for Spark executor pods based on CPU usage.
B
Employ Spark‘s dynamic resource allocation feature with external shuffle service enabled in Kubernetes.
C
Predefine resource quotas for namespaces and use Spark‘s static allocation mode.
D
Manually adjust the number of executors using the Spark UI based on workload.