Data Modeling

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

Practise these 6 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 — Beyond Medallion: Architecting Disney’s DATOS for Complex, Real-Time Data Streams With Databricks | Databricks

DATOS ingests diverse sources like Kafka, Kinesis, and data lakes while absorbing upstream schema and delivery variability to provide stable, reusable data products
Databricks — Building a Production: Scale Dimensional Data Mart With Lakeflow Spark Declarative Pipelines and AUTO CDC | Databricks

how to use AUTO CDC to handle data updates across batch and streaming pipelines, supporting continuously arriving sales transactions alongside rapidly changing product, store and
Databricks — Deep Dive: Why the Best Data Warehouse Is a Lakehouse | Databricks

modern lakehouses unify teams around a single source of truth, reduce costly data movement, simplify governance
Databricks — Modern Data Modeling at Scale: Advanced Patterns | Databricks

how to leverage primary and foreign keys, identity columns for surrogate keys, column-level data quality constraints and much more
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