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

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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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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The STREAM function is used to process data from a streaming live table or view, which is a table or view that contains data that has been added only since the last pipeline update. Streaming live tables and views are stateful, meaning that they retain the state of the previous pipeline run and only process new data based on the current query. This is useful for incremental processing of streaming or batch data sources. The customers table in the query is a streaming live table, which means that it contains the latest data from the source. The STREAM function enables the query to read the data from the customers table incrementally and create another streaming live table named loyal_customers, which contains the customer IDs of the customers with high loyalty level. References: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.