Tool Design, MCP, and Agent Context — Databricks Certified Context Engineering Associate

The official DATABRICKS documentation our Tool Design, MCP, and Agent Context practice questions are cited to. Review the primary sources, then practise.

practice 8 questions →

Official references for this objective

Databricks — Building Query Expert MCP: Using RAG to Build an Analytics Agent That Goes Beyond Text-to-SQL | Databricks

actionable insights require more than SQL syntax—they demand business context, domain expertise and nuances that live outside your schema docs
Databricks — From Bowls to Bots: CAVA's Ask Astro Multi-Agent Supervisor on Databricks | Databricks

Ask Astro routes each question to the right specialized agent—finance, operations, customer analytics, HR, Jira, or the web—then synthesizes a unified answer
Databricks — Building Agent-Ready Data: Best Practices for Intelligent Document Processing at Scale | Databricks

Accuracy, cost, and throughput stop being independent dials and become a three-way tradeoff you have to design around
Databricks — MCP Security Deep Dive: How Databricks Secures Tool Access for Enterprise Users | Databricks

Every interaction — from authentication to tool invocation — must be governed, permissioned, and fully auditable.
Databricks — “Make Me a Map”: Building a GIS Agent with Agent Bricks, MCP, and Lakebase | Databricks

Lakebase serves as the spatial backend (Postgres-compatible with PostGIS), while our MCP server exposes Felt's mapping capabilities to Agent Bricks