Application Development — Databricks Certified Generative AI Engineer Associate

The official DATABRICKS documentation our Application Development practice questions are cited to. Review the primary sources, then practise.

practice 11 questions →

Official references for this objective

Databricks — Deploying and Monitoring Agents on Databricks | Databricks

learning how to deploy agents in batch mode using AI Functions and integrate them into data pipelines
Databricks — Applying Software Development Principles to a Data-Driven World: Automating SDP from Dev to Prod | Databricks

a trunk based Git strategy avoids the complexity of multi branch models
Databricks — Building Agentic Business Applications on Databricks with Lakebase, MCP and Marvis AI | Databricks

We will also demonstrate how MCP securely exposes trusted lakehouse data to AI copilots.
Databricks — Building Enterprise Applications with Databricks Apps | Databricks

followed by the deployment of a simple app using available templates
Databricks — Databricks Apps: A Magic Wand for Driving Data Quality Adoption and Observability | Databricks

We moved away from a config-file-driven model owned by a small group of platform engineers toward a user-centric approach that empowers teams to create, test
Databricks — Enabling Evolutionary Database Development with Database Branching | Databricks

Its copy-on-write technology creates a full-fidelity, governance-aware production clone in roughly one second, regardless of size, with zero initial storage.
Databricks — Let AI Work on Your Database: Safe AI-Driven Development with Database Branches | Databricks

create isolated code and data environments that enable AI coding agents to build in parallel, experiment safely, and iterate faster, without risking production data
Databricks — Lovable + Databricks: turn your data into apps your whole org can build on | Databricks

These apps connect through workspace-level OAuth and are governed by your existing access controls.
Databricks — Scaling Energy Deal Evaluation With Databricks Lakebase | Databricks

how it extended its Lakehouse with Databricks Lakebase to introduce relational structure and familiarity without sacrificing openness or scale