Databricks-GenAI-Assoc Design Applications Practice Question
An engineer is designing a Databricks RAG application that must support multi-turn conversations where follow-up questions refer to earlier turns. They want the retrieval step to remain accurate as the conversation progresses. Which TWO design elements should they include? (Choose two.)
⚠ Common exam trap
The trap here is assuming that adding more conversation text to the retriever query improves multi-turn accuracy, when it actually dilutes the embedding and harms relevance.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Maintain a conversation history buffer and condense prior turns into a standalone query before invoking Vector Search.
Conversational RAG needs two coordinated elements: a rewritten, standalone query for retrieval, and conversation history for generation. Rewriting resolves references so Vector Search returns on-topic chunks, while including prior turns helps the model produce coherent follow-ups. Simply enlarging the retrieval window or removing filters does not address the ambiguity that multi-turn questions introduce.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Maintain a conversation history buffer and condense prior turns into a standalone query before invoking Vector Search.
Why this is correct
Follow-up questions often contain pronouns or omitted subjects that are ambiguous in isolation. Condensing the history into a self-contained query gives the retriever enough context to find relevant chunks, improving recall for multi-turn interactions. This is a standard pattern in conversational RAG design on Databricks.
- ✗
Increase the number of retrieved chunks for every turn to compensate for conversational ambiguity.
Why it's wrong here
Retrieving more chunks does not resolve ambiguity caused by unresolved references; it adds noise and token cost. A larger context can even distract the model from the most relevant passage. The better fix is query rewriting so the retriever receives a clear, standalone question.
- ✓
Include the previous assistant response as additional context in the prompt sent to the generation model.
Why this is correct
Including prior assistant responses helps the generation model maintain coherence and avoid repeating or contradicting earlier answers. While retrieval uses a condensed query, generation benefits from seeing the recent exchange. This separation of concerns keeps retrieval focused and generation contextually aware.
- ✗
Store the conversation history in the prompt and pass it unchanged to the retriever as the search query.
Why it's wrong here
Passing the entire history as the search query dilutes the embedding with earlier topics and can retrieve irrelevant chunks. The retriever is optimized for a focused query, not a transcript. History should inform a rewritten query, not be used verbatim for similarity search.
- ✗
Disable metadata filtering during multi-turn conversations so more historical documents are eligible.
Why it's wrong here
Disabling metadata filters weakens governance and can surface content that should be excluded, without improving reference resolution. Multi-turn accuracy depends on query clarity, not on broadening the candidate set. Filters should remain in place to preserve policy and relevance.
Visual reference
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-GenAI-Assoc exam.