Databricks-Certified-Data-Engineer-Associate Databricks Exam Lab Questions

he approach that can be used to identify the owner of new_table is to review the Owner field in the table’s page in Data Explorer. Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1. The table’s page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2. The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.

The other options are not correct or reliable for identifying the owner of new_table. Reviewing the Permissions tab in the table’s page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4. Reviewing the Owner field in the table’s page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5. There is a way to identify the owner of the table, as explained above, so option E is false.

1: Data Explorer | Databricks on AWS

2: Table details | Databricks on AWS

3: Set owner when creating a view in databricks sql - Databricks - 9978

4: Table access control | Databricks on AWS

5: External tables | Databricks on AWS

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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In the Medallion Architecture promoted by Databricks, the Bronze layer represents the raw ingestion layer where data is stored in its original form with minimal transformation. The primary goal is to ensure data fidelity and traceability , not to apply business logic or heavy transformations. Typical operations in the Bronze layer include schema inference (or enforcement), ingestion of raw data, and the addition of metadata columns such as ingestion timestamp, load date/time, and process identifiers. These metadata fields help track lineage, enable debugging, and support incremental processing downstream. Extensive data cleansing (A), aggregations (B), and business transformations (C) are intentionally deferred to the Silver and Gold layers , where data is refined and structured for analytics. By keeping Bronze data close to the source and append-only, organizations maintain a reliable audit trail and can reprocess data if needed. Therefore, adding operational metadata like load timestamps and process IDs is the correct characteristic of Bronze layer transformations.

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The < = > operator performs null-safe equality in Databricks SQL. It returns TRUE when both operands contain the same non-NULL value and also when both operands are NULL. It returns FALSE when only one operand is NULL. By contrast, the standard equality operator returns UNKNOWN when either side is NULL, so it cannot match two NULL values in a WHERE condition. Option C implements inequality logic and does not provide null-safe equality. Option D can incorrectly treat a genuine empty string as equivalent to NULL, changing the meaning of the data. Therefore, option A is the only predicate that preserves SQL values while implementing the required NULL comparison behavior. This question was also checked against the existing Word question bank.

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