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Training and exam prep - build and deploy production generative AI on the Databricks Data Intelligence Platform (RAG, Vector Search, Model Serving), with hands-on labs.
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The Databricks Certified Generative AI Engineer Associate is Databricks' generative AI engineering certification - described by Databricks as the industry's first comprehensive one. It validates that you can design, build, deploy and monitor production-grade LLM applications on the Databricks Data Intelligence Platform. The certification is earned by passing a single exam: 45 scored multiple-choice questions in 90 minutes (some unscored questions may also appear), taken online with remote proctoring through Databricks' certification platform, for about US$200. The exam covers six areas: designing applications (LLM selection, architecture); data preparation (embeddings, chunking, vector databases); application development (RAG pipelines, prompt engineering, chains and agents - the largest area, about 30%); assembling and deploying applications (Model Serving, scaling); governance (Unity Catalog, responsible AI); and evaluation and monitoring (MLflow evaluation and judges). Core tools include Databricks Vector Search, Mosaic AI Model Serving, MLflow and Unity Catalog.
Generative AI is one of the most in-demand areas in tech, and this certification proves you can engineer it for production - not just experiment with a chatbot. It validates that you can design, build, deploy and monitor real LLM applications on Databricks: RAG systems, Vector Search, agents, Model Serving and evaluation. Databricks calls it the industry's first comprehensive GenAI engineering certification, and it is one of the fastest-growing certifications of 2026 as organisations race to ship production generative AI.
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This certification has no formal prerequisites and is aimed at people who build or work near GenAI applications on Databricks. It is ideal for: - Data engineers and ML engineers moving into GenAI - AI engineers and LLM application developers - Solution architects designing GenAI systems - Anyone shipping RAG apps, agents or LLM features on Databricks Databricks recommends about 6 months of hands-on experience, though nothing is formally required. You should understand how RAG pipelines, Vector Search, MLflow, Model Serving and Unity Catalog connect.
How to earn it:
1. Prepare with a GenAI Engineer Associate exam-prep course that includes hands-on Databricks labs - that is what we provide.
2. Register for the exam through Databricks' official certification platform.
3. Take the exam online with remote proctoring (45 scored...
The Databricks Certified Generative AI Engineer Associate certification validates your ability to develop and implement generative AI solutions using the Databricks Data Intelligence Platform. The exam assesses your knowledge of large language models, retrieval-augmented generation, prompt engineering, data preparation, vector search, model evaluation, application development, and responsible AI practices. Understanding the exam structure and certification requirements can help you plan your preparation effectively and build confidence before the assessment. This section explains the exam pattern, question format, duration, certification requirements, and other important details you should know before taking the Databricks Certified Generative AI Engineer Associate exam.
Exam details verified on 16 August 2026. The Databricks Certified Generative AI Engineer Associate is earned by passing a single online- proctored exam of 45 scored multiple-choice questions in 90 minutes (some unscored questions may also appear), for about US$200 (plus applicable local taxes). Databricks does not publicly disclose a fixed passing score; a figure around 70% is commonly cited - confirm on Databricks' exam guide.
The GenAI Engineer Associate is ideal for: - Data engineers and ML engineers moving into GenAI - AI engineers and LLM application developers - Solution architects designing GenAI systems There are no formal prerequisites, but this is production GenAI engineering - you should be comfortable with how RAG, Vector Search, Model Serving and MLflow connect. If you want classic machine learning (models, tuning, deployment) rather than LLM applications, consider the Databricks Machine Learning Associate. Many people find the two complement each other.
