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

The --target prod option explicitly selects the prod target from the bundle configuration. Databricks then resolves ${var.dataset_catalog} using the target-level value catalog_prod, which takes precedence over the top-level default of catalog_dev. This allows the same dashboard resource definition to be promoted across environments without editing the resource itself. The --var option can override an individual variable, but it does not automatically select the production target. The --profile option selects an authentication profile from .databrickscfg; it does not select a bundle target or automatically apply the target’s variables. bundle execute is not the deployment command. Consequently, option D correctly combines production-target selection with the environment-specific variable override already defined in the YAML configuration.

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A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. One of the key features of a data lakehouse is that it supports ACID-compliant transactions, which means that it ensures data integrity, consistency, and isolation across concurrent read and write operations3. This feature results in improved data quality over a traditional data lake, which does not support transactions and may suffer from data corruption, duplication, or inconsistency due to concurrent or streaming data ingestion and processing . References: 1: What is a Data Lakehouse? - Databricks 2: What is a Data Lakehouse? Definition, features & benefits. - Qlik 3: ACID Transactions - Databricks : [Data Lake vs Data Warehouse: Key Differences] : [Data Lakehouse: The Future of Data Engineering]