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A data engineer is onboarding a new Bronze ingestion pipeline in Databricks with Unity Catalog. The team wants Databricks to handle storage layout, apply platform optimizations over time, and simplify lifecycle management so that when a table is dropped, its underlying data is also cleaned up according to Databricks-managed retention policies. Which table type should the data engineer create for these ingestion tables?
Correct Answer: A
Databricks recommends managed tables when you want the platform to control both the table metadata and the underlying storage lifecycle. For Unity Catalog managed tables, Databricks manages storage layout and applies platform-managed optimization features over time. Databricks also documents that when you drop a managed table, the underlying cloud data is deleted automatically after the managed retention period, which simplifies cleanup and reduces storage administration. That behavior matches the requirement exactly. External tables, by contrast, are used when you want to retain direct control over the storage path and lifecycle. Databricks explicitly states that dropping an external table removes only the metadata and does not delete the underlying files. Foreign tables are for federation scenarios against external systems, not for Bronze ingestion storage managed by Databricks. Temporary views also do not provide the desired governed lifecycle. Because the team wants Databricks-managed storage, managed retention, and simplified lifecycle handling, managed tables are the correct choice for these Bronze ingestion tables. =========