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

Option B reads the relational source through JDBC and writes the resulting Spark DataFrame directly as a registered Unity Catalog table using its three-level name: catalog, schema, and table. Once written with saveAsTable, the data is immediately discoverable and queryable through Databricks SQL, subject to Unity Catalog permissions. Because the read and write operations occur through supported Databricks workloads, Unity Catalog can capture lineage between the source operation, notebook or job, and destination table. Writing files only to DBFS or an external path does not by itself create the required governed table. Introducing a separate staging and loading job adds unnecessary processing and operational complexity. A Pandas extraction also reduces scalability and does not satisfy the direct managed-table requirement. Therefore, option B is correct.

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Option D matches the heterogeneous sources and governance requirements. A relational database, object storage, and Kafka require source-appropriate ingestion configurations rather than a single connector attempting to handle every source. Database ingestion can use incremental or change-data-capture processing, Amazon S3 files can use file-oriented ingestion, and Kafka requires a streaming connector. Publishing the outputs into Unity Catalog schemas provides centralized permissions, discovery, and governance boundaries. Supported Databricks ingestion workloads also record lineage between source and destination objects in Unity Catalog. Delta table history and time travel provide access to earlier table versions when the required transaction-log and data-file retention settings are maintained. Disabling version history or repeatedly performing complete reloads would weaken auditability and increase processing overhead. Therefore, the separate, incrementally processed, Unity Catalog-governed design in option D is correct.

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