Question 4

You configure a classic all-purpose compute resource on a Premium-plan Azure Databricks workspace for an interactive data-exploration team. Throughout the day the workload swings widely: some phases run heavy Spark transformations across large Delta tables, while other phases are mostly idle as analysts read results. You want Azure Databricks to dynamically add worker nodes during compute-intensive phases and remove them when they are no longer needed, so you achieve high utilization without provisioning a large fixed cluster for the peak. You configure the cluster as follows: ```json { "cluster_name": "exploration-autoscale", "spark_version": "15.4.x-scala2.12", "node_type_id": "Standard_DS4_v2", "autoscale": { "min_workers": 2, "max_workers": 12 }, "data_security_mode": "USER_ISOLATION" } ``` Which statement BEST describes the benefit of enabling autoscaling (Min 2 / Max 12) instead of setting a fixed number of workers for this workload?

• AAutoscaling guarantees the cluster always runs at exactly 12 workers so queries never queue.
• BAutoscaling lets Databricks dynamically reallocate workers between the min and max so the cluster runs faster than an under-provisioned fixed cluster and can reduce overall cost versus a statically sized cluster.
• CAutoscaling eliminates the need for auto-termination because an idle autoscaling cluster scales itself to zero workers and stops billing.
• DAutoscaling is required for Unity Catalog compatibility; a fixed-size cluster cannot access Unity Catalog data.
• EAutoscaling forces the use of spot instances for all worker nodes to lower cost.