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- AI
Databricks Certified Generative AI Engineer Associate
Databricks is a unified data and AI platform used to build, deploy, and govern generative AI applications at scale. Tools like Vector Search, Model Serving, MLflow, and Unity Catalog help engineers build and deploy RAG applications and LLM chains.\ \ \ This path prepares you for the Databricks Certified Generative AI Engineer Associate exam, covering application design, data preparation, development, deployment, governance, and evaluation.\ \ This learning path is actively in production. More content will be added to this page as it gets published and becomes available in the library. Planned content includes:\ \ - Databricks Certified Generative AI Engineer Associate: Designing Applications (video course)\ - Databricks Certified Generative AI Engineer Associate: Data Preparation (video course)\ - Databricks Certified Generative AI Engineer Associate: Application Development (video course)\ - Databricks Certified Generative AI Engineer Associate: Assembling and Deploying Apps (video course)\ - Databricks Certified Generative AI Engineer Associate: Governance (video course)\ - Databricks Certified Generative AI Engineer Associate: Evaluation and Monitoring (video course)\ - Databricks Certified Generative AI Engineer Associate: Exam Preparation and Strategy (video course)
Content in this path
Databricks Certified Generative AI Engineer Associate
Watch the following courses to get learning about designing, building, deploying, and monitoring generative AI applications on Databricks and to get ready for the exam!
Try this certification path for free
Build confidence to ace your certification exam with a variety of prep tools, including video courses, labs, and practice exams.
What You'll Learn
- \- How to design generative AI applications, including prompts, chain components, tools, and when to use Agent Bricks\
- How to prepare data for RAG applications, including chunking, document extraction, filtering, and writing to Delta Lake tables in Unity Catalog\
- How to develop LLM applications with LangChain, MLflow, and the Agent Framework, including model selection, prompt augmentation, and guardrails\
- How to assemble and deploy applications, including pyfunc chains, Vector Search indexes, Model Serving, Unity Catalog registration, MCP servers, and CI/CD\
- How to apply governance, including masking, guardrails against malicious inputs, and licensing and legal considerations for data sources\
- How to evaluate and monitor applications with MLflow scoring and tracing, inference tables, AI Gateway, custom scorers, and SME feedback\
- How to prepare for the exam with its format, objectives, and test-taking strategies
- No prerequisites are required for this exam, but related training is highly recommended. Databricks recommends at least six months of hands-on experience with the Databricks Platform across the areas in the exam outline, including designing, building, deploying, governing, and evaluating LLM-enabled applications.
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