Pass the Databricks Databricks Certification Databricks-Certified-Data-Engineer-Associate Questions and answers with CertsForce

Option B preserves the historical evidence needed to diagnose the regression. The Lakeflow Jobs UI provides run history, duration information, task details, links to the compute resource, and task metrics. Comparing several slow and normal runs shows whether the increase is consistent or intermittent. Cluster metrics such as CPU utilization, memory pressure, disk I/O, and network activity can then reveal resource spikes or bottlenecks that correlate with the longer runs. Automatically increasing cluster size before diagnosis could hide the cause and increase cost. Archiving old runs or resetting a baseline removes useful comparison data. Rerunning the job once does not make seven days of historical information irrelevant and cannot establish a trustworthy new baseline. Therefore, comparing run-history and cluster metrics, as stated in option B, is the appropriate troubleshooting action.

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Deploying a bundle creates or updates the resources defined in its configuration, but deployment does not automatically execute those resources. The databricks bundle run command starts a deployed job or pipeline by using its resource key from the bundle configuration. Because the job is keyed as etl_job, the correct command is databricks bundle run etl_job. The command bundle deploy run is not valid CLI syntax, and bundle execute is not the command used to run a bundle resource. databricks jobs submit creates and runs a one-time job from a supplied JSON definition rather than executing the job already managed by the bundle. Therefore, option D correctly triggers the deployed job while preserving the bundle-managed resource configuration.

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Reading a Unity Catalog table requires privileges at all relevant levels of its three-part namespace. The group needs USE CATALOG on corp_marketing, USE SCHEMA on campaigns, and SELECT on email_stats. These privileges allow users to resolve the catalog and schema and read the table without granting permission to create or modify objects. Option A omits USE CATALOG, so table access is incomplete. Option B unnecessarily grants CREATE SCHEMA, violating the requirement that the group must not create objects. Option D omits USE CATALOG and grants MODIFY, which would permit data changes. Unity Catalog follows a hierarchical permissions model, but possession of SELECT alone is insufficient without the required usage privileges on the parent containers. Therefore, the least-privilege sequence in option C is correct.

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In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is “sqlite” because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, “sqlite” would be appropriate here. References:

Create a table using JDBC

JDBC connection string

SQLite JDBC driver

Option D satisfies the requirements because a Lakeflow Connect managed SaaS connector provides source-specific authentication, incremental ingestion, schema evolution, and automated retries as managed capabilities. Managed connectors use serverless infrastructure and publish governed destination tables that can be processed downstream with Lakeflow Spark Declarative Pipelines. This avoids custom code for OAuth token handling, API pagination, CDC state management, retry behavior, and schema-change recovery. Option A still requires the engineering team to implement and maintain CDC and operational logic. Options B and C place most authentication, schema evolution, change tracking, and failure recovery responsibilities in custom notebooks or jobs, conflicting with the minimal-maintenance requirement. Therefore, the fully managed connector described in option D is the only solution that delivers the requested ingestion and operational behavior as an integrated platform capability.

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This question’s options do not include the most precise Unity Catalog answer, which would normally be the MANAGE privilege on the schema, because Databricks documents that MANAGE allows a principal to grant and revoke privileges on an object without automatically granting all data-access privileges on that object. However, since MANAGE is not one of the answer choices, the best available answer is D, Schema Owner . Databricks states that schema owners can grant and revoke permissions on the schema, and schema-level administration is a documented responsibility of the schema owner. Option A is incorrect because USE CATALOG and USE SCHEMA only allow object access/navigation and do not confer privilege-management rights. Option B is too broad because a catalog owner governs the entire catalog scope, not just one schema. Option C is too narrow because a table owner controls only one table, not various objects across a schema. So, among the choices provided, Schema Owner is the closest Databricks-aligned answer, though the cleanest real-world implementation would be MANAGE on the schema .

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