Data Transformation and Modeling

Exam objectives are the official skill domains Databricks Certified Data Engineer Associate tests. Below are the official DATABRICKS docs our questions on this objective cite — review them, then practise.

Practise these 4 questions →

Official references for this objective

Databricks — Best Practices for Running dbt on Databricks: From SQL Models to Production Pipelines | Databricks

proven patterns for building a medallion-style architecture, orchestrating dbt jobs directly in Databricks, and applying governance and observability using Unity Catalog
Databricks — Building a Production: Scale Dimensional Data Mart With Lakeflow Spark Declarative Pipelines and AUTO CDC | Databricks

designing and operating dimensional data marts using Spark Declarative Pipelines, including patterns for CDC, schema evolution, and hybrid batch/streaming workloads
Databricks — Deep Dive: Why the Best Data Warehouse Is a Lakehouse | Databricks

costs with serverless SQL compute, improve trust with automated lineage, eliminate silos, and modernize legacy warehouse-centric architectures with a scalable, governed foundation for analytics and
Databricks — Semantic Modeling Best Practices for BI and AI | Databricks

UC Business Semantics provide governed definitions, AI/BI Dashboards add local semantics, and the Genie Knowledge Store adds domain context to improve how AI answers