Databricks Databricks-Certified-Data-Engineer-Associate Exam With Confidence Using Practice Dumps

For small-scale, ad hoc Python workloads that run frequently and should start quickly and stop without leaving idle infrastructure running, serverless compute is the best fit. Databricks documentation explains that serverless compute allows users to run workloads without provisioning compute resources manually , and Databricks automatically allocates and manages the required resources. This improves startup and scaling speed and minimizes idle time, which directly matches the requirement that compute should “wind down quickly” after the work completes. Databricks also recommends serverless compute broadly when the workload is supported because it is the simplest and most reliable option. An all-purpose cluster is better suited to interactive collaborative development and can remain running longer than needed. A job cluster is ephemeral and often a good batch choice, but it still requires more explicit configuration than serverless and is not as optimized for minimal operational overhead. A SQL warehouse is intended for SQL workloads, not general ad hoc Python script execution. Therefore, serverless compute is the most appropriate choice.

Databricks documents that Delta Sharing does not require data replication , but cloud providers can still charge data egress fees when data is shared across clouds or across geographic regions . Databricks specifically states that sharing within the same region incurs no egress cost , while cross-cloud or cross-region transfers can create additional charges from the underlying cloud provider. That means option A is correct. Options B and C describe same-cloud or same-region use cases that do not trigger the cross-region/cross-cloud egress pattern Databricks calls out. Option D is unrelated to the main documented billing driver for Delta Sharing costs. The important principle is that Delta Sharing itself is designed to avoid replication overhead, but the physical movement of data between cloud boundaries or regions can still result in vendor networking charges. Therefore, when sharing data with external partners in other regions or clouds, engineers should plan for possible egress costs and monitor them accordingly.

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