Databricks-Certified-Data-Engineer-Associate Exam Dumps - Databricks Certified Data Engineer Associate Exam

The best practice is to use " Complete " as output mode instead of " append " when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

Databricks Asset Bundles are a Databricks CLI feature that lets teams define Databricks projects “as code” using YAML configuration files . In a bundle, metadata is expressed in YAML (for example, a required databricks.yml at the root), and the configuration can declare resources (such as Databricks Jobs/workflows and other supported assets), artifacts , and deployment targets . After defining these YAML manifests, teams use the Databricks CLI to validate, deploy, and run the bundle into a Databricks workspace, which matches the scenario described (YAML + CLI deployment). This approach standardizes how workflows and related resources are packaged and promoted across environments (dev/test/prod) while keeping definitions version-controlled and reproducible. GitHub is only a hosting/version-control platform (not the YAML deployment feature itself), Terraform is a separate IaC tool, and “DataOps” is a methodology rather than a specific Databricks feature. ( Databricks Documentation )

Bronze tables are the first layer of a medallion architecture, which is a data design pattern used to organize data in a lakehouse. Bronze tables contain raw data ingested from various sources, such as RDBMS data, JSON files, IoT data, etc. The table structures in this layer correspond to the source system table structures “as-is”, along with any additional metadata columns that capture the load date/time, process ID, etc. The only transformation applied to the raw data in this layer is to apply a schema, which defines the column names and data types of the table. The schema can be inferred from the data source or specified explicitly. Applying a schema to the raw data enables the use of SQL and other structured query languages to access and analyze the data. Therefore, option E is the correct answer. References: What is a Medallion Architecture?, Raw Data Ingestion into Delta Lake Bronze tables using Azure Synapse Mapping Data Flow, Apache Spark + Delta Lake concepts, Delta Lake Architecture & Azure Databricks Workspace.

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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databricks bundle validate should be run first because it checks whether the bundle configuration files are syntactically valid and reports configuration warnings. After validation succeeds, databricks bundle deploy creates or updates the bundle-defined resources and uploads the required artifacts to the selected workspace target. The final command, databricks bundle run, starts a deployed job or Lakeflow pipeline by using its resource key. bundle init creates a new project from a template and is not the validation step for an already developed bundle. bundle sync synchronizes files for development workflows but does not replace a complete deployment. The commands bundle execute, bundle check, bundle upload, and bundle start are not the appropriate command sequence. Therefore, option B is correct.