Practice Exams: Databricks Generative Al Engineer Associate

About this course

Each practice exam consists of 56 multiple-choice questions, carefully distributed across six key knowledge domains. The questions are designed to assess both your theoretical understanding and your ability to apply Databricks Generative AI concepts to realistic business scenarios.1. Design Applications

Design effective AI solutions by translating business requirements into appropriate prompts, AI pipelines, and tool-based workflows.

Identify and select the most suitable models, chain components, and Agent Bricks for different business scenarios.

Design sophisticated, multi-step AI systems that combine reasoning, tools, and structured workflows.2. Data Preparation

Prepare and transform high-quality data for Retrieval-Augmented Generation (RAG), including document extraction, filtering, and chunking strategies.

Manage, organize, and retrieve enterprise knowledge using Delta Lake and Unity Catalog.

Improve retrieval accuracy and relevance through advanced chunking techniques, re-ranking methods, and retrieval evaluation metrics.3. Application Development

Develop production-ready Generative AI applications using frameworks and technologies such as LangChain, MLflow, and Agent Framework.

Enhance application quality through prompt engineering, model optimization, embeddings, and appropriate guardrails.

Build, test, evaluate, and monitor intelligent agents, including complex multi-agent architectures.4. Assembling and Deploying Applications

Build, package, register, and deploy LLM and RAG applications using MLflow and Vector Search.

Configure model serving, storage, access controls, security, CI/CD pipelines, prompt management, and Model Context Protocol (MCP) integrations.

Read the full description on Udemy on Udemy (opens in a new tab)

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.