Databricks-GenAI-Assoc Design Applications Practice Question
A GenAI engineer is building a Databricks RAG application where the retrieval step returns the top-5 chunks for each user question. The engineer wants to add a second LLM call that evaluates whether each retrieved chunk contains enough information to answer the question, and then filters out chunks that fail this evaluation before passing the remaining chunks to the final answer-generation prompt. Which design pattern is the engineer implementing?
⚠ Common exam trap
The trap here is assuming that any technique which improves retrieval quality, such as hybrid search or embedding fine-tuning, also performs per-chunk relevance filtering with an LLM.
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
✓
Retrieval-augmented generation with relevance grading
The defining behavior in the scenario is a second LLM call that grades each retrieved chunk for answerability and drops the ones that fail. That is relevance grading layered on top of retrieval-augmented generation, which raises precision by keeping only context that can actually support the answer. The other techniques alter retrieval ranking, prompt reasoning, or embeddings but do not insert an evaluative filtering step between retrieval and generation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Chain-of-thought prompting
Why it's wrong here
Chain-of-thought prompting asks the model to expose intermediate reasoning steps so that a complex task is solved more reliably. In this scenario, the engineer is not trying to elicit a reasoning trace from the answering model; they are inserting an independent LLM call whose only job is to judge and filter retrieved chunks. Chain-of-thought would not remove irrelevant chunks from the context window, so it does not solve the precision problem described.
- ✓
Retrieval-augmented generation with relevance grading
Why this is correct
The engineer is adding an LLM-based relevance grader between retrieval and generation. Each retrieved chunk is scored for whether it can actually answer the question, and only chunks that pass the grade are forwarded. This pattern improves answer precision by removing semantically similar but non-answering passages before the final prompt is assembled, which is exactly what the scenario describes.
- ✗
Fine-tuning the embedding model on domain data
Why it's wrong here
Fine-tuning an embedding model changes how vectors are produced and can improve recall for domain vocabulary. However, it does not add a separate evaluation step that inspects each retrieved chunk and discards those that fail a relevance check. The scenario explicitly describes a second LLM call that evaluates chunks, which is a runtime filtering pattern rather than a training-time embedding adjustment.
- ✗
Hybrid search combining vector similarity with BM25 keyword matching
Why it's wrong here
Hybrid search merges dense vector results with sparse keyword results to improve recall for exact terms. It still returns a ranked list of chunks and does not ask an LLM to judge whether each chunk contains enough information to answer the question. The scenario's second LLM call that filters chunks is the defining element, and hybrid search alone would not perform that filtering.
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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.