Databricks Certified Generative AI Engineer Associate
Practice exam for the Databricks Certified Generative AI Engineer Associate certification. Covers LLM application design, data preparation for RAG, prompt engineering, LangChain, application deployment on Databricks Model Serving, AI governance with Unity Catalog, and LLM evaluation and monitoring.
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Sample Questions — Databricks Certified Generative AI Engineer Associate
5 free sample questions from this practice exam. Correct answers are highlighted.
1. A company wants to build a customer support chatbot that answers questions using its internal product documentation. The documentation is updated weekly and contains thousands of pages. Which approach is best suited for this use case?
2. An AI engineer needs to select an LLM for a production chatbot. The primary requirements are low response latency (under 500ms) and minimal cost per request. Model quality is acceptable as long as the model passes a basic accuracy benchmark. Which criterion should guide the final selection?
3. A developer is building a simple FAQ bot that answers questions about a company's return policy. The return policy is three paragraphs long and rarely changes. Which design approach is most appropriate?
4. Which of the following is a primary benefit of using Retrieval-Augmented Generation (RAG) compared to relying solely on a pre-trained LLM for answering factual questions?
5. A company is building an application where users have multi-turn conversations with an AI assistant. The assistant must remember what was said earlier in the same conversation to provide coherent responses. Which design characteristic is essential?
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Study Guides & Articles
How to Pass Databricks Certified GenAI Engineer Associate in 2026: Study Guide
Complete study guide for the Databricks Certified Generative AI Engineer Associate exam. Covers RAG architecture, LangChain, prompt engineering, MLflow AI Gateway, and LLM evaluation.
Databricks GenAI Engineer Deep Dive: RAG Architecture, LangChain & MLflow AI Gateway
Master the hardest Databricks GenAI Engineer topics: RAG chunking strategies, vector stores, LangChain chains vs agents, MLflow AI Gateway routing, and LLM evaluation metrics.
Databricks GenAI Engineer Exam Traps: RAG, Prompting & Evaluation Gotchas
Avoid the most common Databricks GenAI Engineer exam mistakes. Master RAG pipeline traps, LangChain agent vs chain confusion, MLflow AI Gateway, and LLM evaluation metrics.
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