Complete preparation for Databricks Generative AI Engineer Associate certification + hands-on training
If you are interested in becoming a Certified Generative AI Engineer Associate from Databricks, you have come to the right place! This study guide will help you with preparing for this certification exam.
What you’ll learn
- Design end-to-end Generative AI applications on Databricks.
- Prepare, clean, and manage data for AI and RAG applications.
- Assemble and Deploy production-ready AI applications.
- Evaluate, test, and monitor Generative AI systems.
- Implement governance and security, and responsible AI practices.
Course Content
- Introduction to Databricks –> 6 lectures • 26min.
- Generative AI Fundamentals –> 4 lectures • 19min.
- Retrieval-Augmented Generation (RAG) –> 11 lectures • 1hr 16min.
- AI Agent Development –> 4 lectures • 32min.
- Gen AI Evaluation and Governance –> 2 lectures • 19min.
- Gen AI Deployment and Monitoring –> 4 lectures • 36min.
- No-Code AI Agents –> 1 lecture • 9min.
- Certification Overview –> 2 lectures • 8min.
Requirements
If you are interested in becoming a Certified Generative AI Engineer Associate from Databricks, you have come to the right place! This study guide will help you with preparing for this certification exam.
By the end of this course, you will have gained knowledge and skills in:
1. Design Applications
- Design prompts, AI pipelines, and tool workflows that meet business requirements.
- Select appropriate models, chain components, and Agent Bricks for specific use cases.
- Build multi-stage reasoning systems using tools and structured AI workflows.
2. Data Preparation
- Prepare high-quality RAG data through chunking, filtering, and document extraction.
- Store, organize, and retrieve knowledge using Delta Lake and Unity Catalog.
- Evaluate and optimize retrieval with advanced chunking, re-ranking, and retrieval metrics.
3. Application Development
- Build GenAI applications using LangChain, MLflow, and Agent Framework.
- Optimize prompts, models, embeddings, and guardrails for quality, safety, and performance.
- Develop, evaluate, and monitor agentic and multi-agent systems.
4. Assembling & Deploying Applications
- Build, register, and deploy RAG and LLM applications with MLflow and Vector Search.
- Configure serving, storage, security, CI/CD, prompt lifecycle, and MCP integrations.
- Develop user-facing interfaces and optimize deployment for performance, cost, and scalability.
5. Evaluation & Monitoring
- Evaluate LLMs and agents using metrics, MLflow, custom scorers, and ground truth.
- Monitor deployments with inference logging, Agent Monitoring, AI Gateway, and cost controls.
- Continuously improve performance using monitoring insights and SME feedback.
6. Governance
- Apply guardrails, masking, and security techniques to protect GenAI applications.
- Ensure compliance with legal, licensing, and data governance requirements.
- Mitigate risks from unsafe or problematic data sources.
With the knowledge you gain during this course, you will be ready to take the certification exam.
I am looking forward to meeting you!