Databricks Generative AI Engineer Associate
Databricks Certified Generative AI Engineer Associate covers building and deploying generative-AI solutions on Databricks. Six sections: design applications (14%), data preparation (14%), application development (30%), assembling and deploying applications (22%), governance (8%) and evaluation and monitoring (12%). 45 scored questions in 90 minutes.
Offered in: English, Japanese, Portuguese, Korean.
Owner: Databricks · Read: · Source: Databricks Certified Generative AI Engineer Associate
Exam fee at last check: $200, per the vendor. The official objectives are at www.databricks.com/learn/certification/genai-engineer-associate. Verified 2026-10-25.
Practice is free. The full analysis adds the right answer, the explanation and the source for every question, readiness by domain and a study plan, for one payment.
- Design Applications14%
- Data Preparation14%
- Application Development30%
- Assembling and Deploying Applications22%
- Governance8%
- Evaluation and Monitoring12%
Source: Vendor exam objectives. Checked 2026-10-25. Bar length is relative to the heaviest domain; the figure beside each name is the published weight.
What's on the exam
Design Applications 14%
Turning a business need into a prompt, a chain, a set of tools and a choice of model task.
- Design a prompt that elicits a specifically formatted response
- Select model tasks and chain components for a desired input and output
- Define and order tools that gather knowledge or take actions for multi-stage reasoning
- Translate business goals into the inputs and outputs of the AI pipeline
- Agent Bricks: Knowledge Assistant, Multiagent Supervisor, Information Extraction
Primary sources: Databricks Generative AI Engineer Associate exam guide
Data Preparation 14%
Preparing source documents for retrieval: chunking, filtering, extraction and writing to Delta tables.
- Chunking strategy for a document structure and model constraints
- Filtering content that degrades a RAG application
- Choosing a Python package to extract document content
- Writing chunked text into Delta Lake tables in Unity Catalog
- Retrieval evaluation, advanced chunking and the role of re-ranking
Primary sources: Databricks Generative AI Engineer Associate exam guide, Databricks Vector Search docs
Application Development 30%
Building the application: tools, prompts, guardrails, model and embedding choice, and agent frameworks.
- Selecting LangChain or similar tools for a generative AI application
- Augmenting and adjusting prompts; qualitative assessment of quality and safety
- Implementing LLM guardrails to prevent negative outcomes
- Selecting an LLM, an embedding model context length and a model from a hub using model cards and metrics
- MLflow and Agent Framework for agentic systems; multi-agent systems with Genie Spaces
Primary sources: Databricks Generative AI Engineer Associate exam guide, Mosaic AI Model Serving docs
Assembling and Deploying Applications 22%
Packaging, registering, securing and serving the application, including Vector Search.
- Coding a chain as a pyfunc model with pre- and post-processing
- Registering the model to Unity Catalog with MLflow
- Creating and querying a Vector Search index; configuring it for embedding count, update frequency, latency and cost
- Serving an LLM application with Foundation Model APIs; batch inference with ai_query()
- CI/CD practice, prompt version control and MCP server integration
Primary sources: Databricks Generative AI Engineer Associate exam guide, Databricks Vector Search docs
Governance 8%
Masking, guardrails against malicious input, and legal and licensing limits on data sources.
- Masking techniques as guardrails
- Guardrail techniques against malicious user inputs
- Legal and licensing requirements for data sources
- Mitigating problematic text in a data source
Primary sources: Databricks Generative AI Engineer Associate exam guide, Mosaic AI Gateway docs
Evaluation and Monitoring 12%
Choosing metrics, evaluating agents, and tracking a live endpoint and its cost.
- Selecting an LLM by quantitative evaluation metrics
- Evaluating agents with MLflow scoring and tracing; custom Scorers; judges that need ground truth
- Inference tables, inference logging and Agent Monitoring for a live endpoint
- AI Gateway: inference tables, usage tables and rate limiting
- Controlling LLM cost; incorporating subject-matter-expert feedback
Primary sources: Databricks Generative AI Engineer Associate exam guide, MLflow LLM tracking
Exam-day strategy
Identify whether the question is about design, building, deployment, or governance. Each section emphasizes different Databricks products.
Most questions have one Databricks-native managed-service answer; eliminate options that require custom code where a managed service exists.
Vector Search, Mosaic AI Model Serving, Mosaic AI Gateway, Mosaic AI Agent Framework, and Mosaic AI Model Training are the five product names most frequently tested. Internalize what each does.
Watch for distractors that confuse training (Mosaic AI Model Training) with serving (Mosaic AI Model Serving) or with evaluation (Mosaic AI Agent Evaluation).
Pace at 2 minutes per question. 90 minutes for 45 questions is generous.
How to prepare for Databricks Generative AI Engineer Associate
- EVIDENCE-BACKEDPractice in the same format as the exam. How well practice matches the final test shapes how much it helps. (Yang et al., 2021)
- EVIDENCE-BACKEDStart practice questions in week one, even on topics you have not studied. Failed attempts before seeing the answer still helped learning. (Kornell et al., 2009 and others)
- EVIDENCE-BACKEDSpace your reviews over weeks instead of cramming the week before. (Cepeda et al., 2006 and others)
- PLAUSIBLEDivide the minutes by the questions before you start, and keep a margin for a second pass if the exam allows one. (Sound arithmetic; no study compared time budgets on a licensing exam.)
Why this works
EVIDENCE-BACKED At least one peer-reviewed study or official source found the effect or states the fact. A study can be small.
PLAUSIBLE Reasonable and partly studied, but not tested on exams, or the results are mixed.
Practice that matches the final test format works better than practice that does not.
Failed attempts before seeing the answer still improved later learning of the material asked about; the benefit for material not asked about was virtually nil.
Spacing beat massing across hundreds of experiments and beat cramming with flashcards; combined with retrieval it raised scores on a real high-stakes exam.
Sound arithmetic; no study compared time budgets on a licensing exam.
Sources
- Yang, C., Luo, L., Vadillo, M. A., Yu, R., & Shanks, D. R. (2021). Testing (quizzing) boosts classroom learning: A systematic and meta-analytic review. Psychological Bulletin, 147(4), 399-435. doi.org/10.1037/bul0000309
- Kornell, N., Hays, M. J., & Bjork, R. A. (2009). Unsuccessful retrieval attempts enhance subsequent learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(4), 989-998. doi.org/10.1037/a0015729
- Richland, L. E., Kornell, N., & Kao, L. S. (2009). The pretesting effect: Do unsuccessful retrieval attempts enhance learning? Journal of Experimental Psychology: Applied, 15(3), 243-257. doi.org/10.1037/a0016496
- St. Hilaire, K. J., Chan, J. C. K., & Ahn, D. (2024). Guessing as a learning intervention: A meta-analytic review of the prequestion effect. Psychonomic Bulletin & Review, 31(2), 411-441. doi.org/10.3758/s13423-023-02353-8
- Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354-380. doi.org/10.1037/0033-2909.132.3.354
- Kornell, N. (2009). Optimising learning using flashcards: Spacing is more effective than cramming. Applied Cognitive Psychology, 23(9), 1297-1317. doi.org/10.1002/acp.1537
- Kang, S. H. K. (2016). Spaced repetition promotes efficient and effective learning: Policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences, 3(1), 12-19. doi.org/10.1177/2372732215624708
- Janes, J. L., Dunlosky, J., Rawson, K. A., & Jasnow, A. (2020). Successive relearning improves performance on a high-stakes exam in a difficult biopsychology course. Applied Cognitive Psychology, 34(5), 1118-1132. doi.org/10.1002/acp.3699
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