Free Databricks Databricks-Certified-Data-Engineer-Associate Practice Exam with Questions & Answers | Set: 2

JSON data is a text-based format that represents data as a collection of name-value pairs. By default, when Auto Loader infers the schema of JSON data, it treats all columns as strings. This is because JSON data can have varying data types for the same column across different files or records, and Auto Loader does not attempt to reconcile these differences. For example, a column named “age” may have integer values in some files, but string values in others. To avoid data loss or errors, Auto Loader infers the column as a string type. However, Auto Loader also provides an option to infer more precise column types based on the sample data. This option is called cloudFiles.inferColumnTypes and it can be set to true or false. When set to true, Auto Loader tries to infer the exact data types of the columns, such as integers, floats, booleans, or nested structures. When set to false, Auto Loader infers all columns as strings. The default value of this option is false. References: Configure schema inference and evolution in Auto Loader, Schema inference with auto loader (non-DLT and DLT), Using and Abusing Auto Loader’s Inferred Schema, Explicit path to data or a defined schema required for Auto loader.

Databricks intentionally stops existing streaming reads and writes after an external Delta table is converted to a Unity Catalog managed table. This protects data consistency because the table’s managed storage location and access path have changed. The documented recovery action is to restart the stream with the same configuration. The existing checkpoint can then resume processing from the last committed offset, and supported path-based access is redirected to the converted managed table. REFRESH TABLE invalidates cached metadata but does not repair a stopped Structured Streaming query or modify its checkpoint. Deleting the checkpoint would discard progress information and could cause unnecessary reprocessing or duplicate output. Additional permissions are not the indicated solution when the failure is specifically caused by managed-table conversion. Therefore, option A is correct.

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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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Unity Catalog attribute-based access control policies provide centralized, tag-driven governance. Administrators define policies at a catalog or schema scope and use governed tags to identify the tables or columns to which the policies apply. When matching tags are assigned to current or future objects, the applicable policy is evaluated automatically, avoiding repetitive manual attachment of filters and masks to individual tables. This approach improves scalability and consistency when many objects require the same protection rules. It is important that the policy applies to objects matching its tag conditions, rather than indiscriminately to every object. The other choices incorrectly describe supported object types, multiple-filter behavior, or implementation languages. Databricks specifically positions attribute-based access control as the centralized approach for consistently applying row filtering and column masking across many governed tables. Therefore, option A is correct.

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Option A uses the supported ALTER TABLE ... SET MANAGED conversion for a Unity Catalog external Delta table. Databricks documents that this conversion retains the table name, permissions, dependent objects, settings, and table history while minimizing downtime for readers and writers. After conversion, Databricks manages the table’s storage lifecycle, satisfying the requirement for platform-controlled cleanup when the managed table is dropped. Dropping the original table with PURGE destroys the object instead of migrating it. SET LOCATION changes the storage path referenced by an external table but does not convert the table into a managed table. Creating a replacement table with CREATE TABLE AS SELECT introduces a new object and requires manual migration of permissions, dependencies, and workloads. Therefore, ALTER TABLE catalog.schema.sales SET MANAGED is the correct action.

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 Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the %sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. References:

Data Explorer

Access control for Delta tables

SHOW GRANT

[GRANT]

Lakehouse Federation is the Databricks feature built for querying external systems without moving all data into Databricks . Databricks documentation describes it as the platform for query federation , enabling users to run queries against multiple external data sources while keeping governance centralized through Unity Catalog . Databricks also documents support for external systems such as Amazon Redshift and other databases through connections and foreign catalogs, allowing read-only access to external data while managing permissions in Unity Catalog. This aligns directly with the requirement to minimize duplication and still maintain centralized governance. Databricks Connect is for local development against Databricks compute, not federated querying. MLflow is for machine learning lifecycle management. Delta Lake is a storage format and table layer, not a federation framework. Therefore, when the goal is unified governance across Databricks and external systems like MySQL, Redshift, and BigQuery without first ingesting the data, Lakehouse Federation is the documented answer. Databricks does note that for high-volume production ingestion, managed connectors may sometimes be preferred, but for direct querying without data movement, Lakehouse Federation is the correct feature.

In Unity Catalog, an external table is defined by referencing data that already exists in cloud storage, and Databricks documentation states that this is done by using a LOCATION clause that points to the storage path. Databricks also notes that the external table’s path should be contained within an existing external location that has been configured with the appropriate storage credential and access permissions. This makes option A the correct choice. Option B is incorrect because simply calling something “unmanaged” without specifying a location does not create a proper Unity Catalog external table. Option C is also incorrect because Databricks documentation explicitly requires a storage location for Unity Catalog external tables. Option D is wrong because managed tables are intended for Databricks-managed storage and lifecycle handling, not for referencing existing external files. So, for ADLS-based data that must remain in place while still being queryable through Databricks, the right pattern is to configure the external location and then create the table with a CREATE TABLE ... LOCATION statement.

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