Databricks Certified Generative AI Engineer Associate assessment
Proctored objective assessment using multiple-choice, multiple-response, or scenario-based items as specified by the provider.
- Type
- Written
- Delivery
- Online
- Duration
- 90 min
Exam sections
Generative AI Solution Design
This section treats generative ai solution design as an applied responsibility, including the surrounding inputs, controls, trade-offs, and evidence of success. Candidates should understand its relationship to generative AI, RAG, vector search and be able to explain how an outcome would be checked in practice.
Question notes
When Databricks Certified Generative AI Engineer Associate reaches Generative AI Solution Design, assessment items can test recognition of a sound approach, diagnosis of an incorrect one, or completion of a practical step. Treat official weighting separately from any unofficial study emphasis.
Preparation tips
Study from outcomes backward: define what a successful generative ai solution design result looks like, list the steps or controls that produce it, and practice spotting evidence that the process has drifted. Use Databricks Certified Generative AI Engineer Associate and the Generative AI Solution Design heading as the boundary for deciding how deeply to pursue adjacent material.
Data Preparation for AI
Questions or tasks in Data Preparation for AI explore more than terminology: candidates need to recognize appropriate methods, dependencies, and failure conditions. Candidates should understand its relationship to generative AI, RAG, vector search and be able to explain how an outcome would be checked in practice.
Question notes
The blueprint's treatment of Data Preparation for AI indicates that this domain may be assessed independently or as part of a scenario crossing other blueprint areas. Pay attention to the wording that changes scope, responsibility, risk, or the best next action.
Preparation tips
Use official terminology as an index, then attach each term to an action, example, counterexample, and verification method. Revisit weak explanations until they no longer depend on memorized wording. Use Databricks Certified Generative AI Engineer Associate and the Data Preparation for AI heading as the boundary for deciding how deeply to pursue adjacent material.
Retrieval-Augmented Generation
The Retrieval-Augmented Generation domain focuses on the concepts, actions, and judgment needed to use this part of the discipline effectively. Candidates should understand its relationship to generative AI, RAG, vector search and be able to explain how an outcome would be checked in practice.
Question notes
Within the Retrieval-Augmented Generation objectives, assessment items can test recognition of a sound approach, diagnosis of an incorrect one, or completion of a practical step. Treat official weighting separately from any unofficial study emphasis.
Preparation tips
Study from outcomes backward: define what a successful retrieval-augmented generation result looks like, list the steps or controls that produce it, and practice spotting evidence that the process has drifted. A final self-check should explain why Retrieval-Augmented Generation matters to the candidate profile for this credential.
Models, Serving, and Agents
Models, Serving, and Agents covers the decisions practitioners make before, during, and after implementing or evaluating this capability. Candidates should understand its relationship to generative AI, RAG, vector search and be able to explain how an outcome would be checked in practice.
Question notes
Assessment of Models, Serving, and Agents means assessment items can test recognition of a sound approach, diagnosis of an incorrect one, or completion of a practical step. Treat official weighting separately from any unofficial study emphasis.
Preparation tips
Alternate focused review with mixed-domain practice. The mixed sessions are important because Models, Serving, and Agents is likely to interact with other responsibilities rather than remain an isolated fact set. Finish by relating Models, Serving, and Agents to the credential's emphasis on model serving.
Evaluation and Monitoring
This section treats evaluation and monitoring as an applied responsibility, including the surrounding inputs, controls, trade-offs, and evidence of success. Candidates should understand its relationship to generative AI, RAG, vector search and be able to explain how an outcome would be checked in practice.
Question notes
A candidate working through Evaluation and Monitoring should remember that expect Evaluation and Monitoring to appear through choices, scenarios, or tasks that require application rather than simple recall. No section-specific question count or timing is assumed unless the provider publishes one.
Preparation tips
Build a small practice set for evaluation and monitoring: one normal workflow, one deliberately broken case, and one comparison between competing approaches. Record what evidence confirms the correct outcome. Finish by relating Evaluation and Monitoring to the credential's emphasis on agents.
Governance and Security
The scope of Governance and Security includes both understanding the subject and choosing an effective response when conditions or objectives change. Candidates should understand its relationship to generative AI, RAG, vector search and be able to explain how an outcome would be checked in practice.
Question notes
A candidate working through Governance and Security should remember that expect Governance and Security to appear through choices, scenarios, or tasks that require application rather than simple recall. No section-specific question count or timing is assumed unless the provider publishes one.
Preparation tips
Turn every major objective in Governance and Security into a decision question. Explain the preferred option, the risk in the strongest alternative, and the observation or artifact that would verify success. That exercise should make the role of Governance and Security within Databricks Certified Generative AI Engineer Associate concrete.