Suggested Answer:A🗳️
Azure Databricks has two types of clusters: interactive and automated. You use interactive clusters to analyze data collaboratively with interactive notebooks. You use automated clusters to run fast and robust automated jobs. Example: Scheduled batch workloads (data engineers running ETL jobs) This scenario involves running batch job JARs and notebooks on a regular cadence through the Databricks platform. The suggested best practice is to launch a new cluster for each run of critical jobs. This helps avoid any issues (failures, missing SLA, and so on) due to an existing workload (noisy neighbor) on a shared cluster. Reference: https://docs.databricks.com/administration-guide/cloud-configurations/aws/cmbp.html#scenario-3-scheduled-batch-workloads-data-engineers-running-etl-jobs
There's no such thing as automated cluster. "Databricks makes a distinction between all-purpose clusters and job clusters". https://docs.databricks.com/clusters/index.html
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