RAG & Vector Search — Databricks GenAI Associate Practice Question
A representative Databricks Certified Generative AI Engineer Associate (Databricks GenAI Associate) exam question on RAG & Vector Search. Work through it below, then read why each option is right or wrong.
This question is part of the free Databricks Certified Generative AI Engineer Associate practice test.
Short answer
The correct answer is A. Retrieval-augmented generation that queries a Mosaic AI Vector Search index of policy documents at inference time.
Retrieval-augmented generation (RAG) separates knowledge from the model: policy documents are chunked, embedded, and indexed in Mosaic AI Vector Search, so weekly changes only require re-indexing, not retraining. Retrieved passages also provide citable grounding. Continued pre-training every week is exactly the slow, costly retraining cycle the company rejected, and it cannot guarantee citations. Static few-shot prompts freeze knowledge at prompt-authoring time, so weekly policy changes would make answers stale, and prompt size limits how much policy text fits. A SQL-writing agent suits structured analytics, but return policies are unstructured prose, and text-to-SQL adds latency and failure modes without solving grounding on documents.
The Question
A retail company wants a customer-support assistant on Databricks that answers questions about its internal return policies, which change weekly. Fine-tuning a model on each policy update is considered too slow and costly. Latency must stay low and answers must cite current policy text. Which application pattern best fits these requirements?
Why A is correct
Retrieval-augmented generation (RAG) separates knowledge from the model: policy documents are chunked, embedded, and indexed in Mosaic AI Vector Search, so weekly changes only require re-indexing, not retraining. Retrieved passages also provide citable grounding. Continued pre-training every week is exactly the slow, costly retraining cycle the company rejected, and it cannot guarantee citations. Static few-shot prompts freeze knowledge at prompt-authoring time, so weekly policy changes would make answers stale, and prompt size limits how much policy text fits. A SQL-writing agent suits structured analytics, but return policies are unstructured prose, and text-to-SQL adds latency and failure modes without solving grounding on documents.
Why the other options are wrong
Option B does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.
Option C does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.
Option D does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.
Key idea: RAG & Vector Search
Retrieval-augmented generation (RAG) separates knowledge from the model: policy documents are chunked, embedded, and indexed in Mosaic AI Vector Search, so weekly changes only require re-indexing, not retraining. Retrieved passages also provide citable grounding. Continued pre-training every week is exactly the slow, costly retraining cycle the company rejected, and it cannot guarantee citations. Static few-shot prompts freeze knowledge at prompt-authoring time, so weekly policy changes would make answers stale, and prompt size limits how much policy text fits. A SQL-writing agent suits structured analytics, but return policies are unstructured prose, and text-to-SQL adds latency and failure modes without solving grounding on documents. On the Databricks GenAI Associate exam, questions in the "Design Applications" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.
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