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

In Unity Catalog, ownership is the primary mechanism for delegating full administrative control of a securable object. The owner of a schema can grant and revoke privileges on that schema and on all objects contained within it (such as tables and views), without needing to be a workspace admin or metastore admin. Transferring ownership of main.marketing to the mkt-admins group therefore aligns precisely with the requirement to delegate day-to-day permission management at the schema scope. Granting MANAGE at the metastore level (option B) would be overly permissive, enabling global administration across all schemas and objects, which violates the principle of least privilege. Simply granting USE SCHEMA and MODIFY (option C) does not confer the ability to manage grants for other users. Making the group workspace admins (option D) unnecessarily elevates privileges beyond Unity Catalog’s data governance model. Unity Catalog documentation emphasizes using ownership transfer to delegate administrative responsibilities at the appropriate scope while maintaining centralized governance in Databricks.

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Serverless compute for workflows is designed to minimize operational overhead for Databricks Jobs while enforcing Unity Catalog governance. When a job is configured to run on serverless compute (in supported regions), Databricks automatically selects instance types , manages autoscaling , enables Photon where applicable, and handles runtime upgrades without user intervention. This directly satisfies the requirement to reduce operational toil. All-purpose clusters (option B) and traditional job clusters (option C) still require users to manage cluster sizing, instance families, and runtime versions, which contradicts the goal of automation. SQL warehouses (option A) are intended for SQL workloads and do not support running general Python notebook job tasks as workflow steps. Databricks documentation specifies that serverless workflows are compatible with Unity Catalog and are the recommended approach when users want fully managed compute for scheduled pipelines and tasks, provided the workspace is in a supported region. This makes serverless compute the correct and fully compliant solution.

In Production mode, the pipeline runs continuously and updates the output tables whenever new data is available in the input sources. The compute resources are allocated on demand and released when the pipeline is stopped. This mode is suitable for production workloads that require high availability and reliability. References: Configure pipeline settings for Delta Live Tables, Tutorial: Run your first Delta Live Tables pipeline, Building Reliable Data Pipelines Using DataBricks’ Delta Live Tables