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Databricks Certified Generative AI Engineer Associate - 340 Questions

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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.

⭐ Premium Updated Mar 2026

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Sample Questions — Databricks Certified Generative AI Engineer Associate - 340 Questions

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?

A Use Retrieval-Augmented Generation (RAG) to index the documentation and retrieve relevant chunks at query time ✓ Correct
B Fine-tune a foundation model on the entire product documentation corpus each week
C Include all documentation directly in the system prompt using prompt engineering
D Train a new language model from scratch on the proprietary documentation

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?

A Select the model with the highest score on the MMLU benchmark regardless of cost
B Select the smallest model that passes the required accuracy benchmark and has the lowest cost per token ✓ Correct
C Select the model with the largest context window available
D Deploy any model on-premise to control infrastructure costs

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?

A Build a RAG pipeline with a vector database to retrieve return policy chunks
B Fine-tune a foundation model on the return policy text
C Include the return policy directly in the system prompt using prompt engineering ✓ Correct
D Design a multi-agent system where one agent classifies questions and another retrieves answers

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?

A RAG reduces hallucinations by grounding responses in retrieved documents from a knowledge base ✓ Correct
B RAG reduces the size of the underlying LLM, making inference faster and cheaper
C RAG eliminates the need for a system prompt by injecting context automatically
D RAG reduces operational complexity by removing the need for a vector store

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?

A Stateless single-turn design where each request is sent without prior context
B Stateful design that maintains and passes conversation history with each LLM request ✓ Correct
C Fine-tuning the LLM to memorize conversation patterns from training data
D Using a model with a larger context window to automatically retain conversation history

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