1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A legal firm needs an AI assistant that can answer questions based on a large corpus of internal regulations that change quarterly. The firm also requires high accuracy and the ability to cite sources. Which approach should the firm choose?
⚠ Common exam trap
Oracle often tests the misconception that fine-tuning is the best way to incorporate domain-specific knowledge, but the trap here is that fine-tuning cannot handle frequently changing data and does not provide source citations, whereas RAG with vector search is purpose-built for dynamic, citation-required use cases.
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
✓
Build a RAG application with vector search and citation generation
Retrieval-Augmented Generation (RAG) with vector search allows the legal firm to index its quarterly-changing regulations into a vector database, retrieve the most relevant chunks for each query, and generate answers with source citations. This approach ensures high accuracy by grounding the LLM's output in the current, authoritative documents without requiring retraining, and citation generation provides the necessary source traceability for legal compliance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Build a RAG application with vector search and citation generation
Why this is correct
RAG retrieves relevant documents and can cite sources, and updating the knowledge base is straightforward.
- ✗
Use a pre-trained model without customization
Why it's wrong here
Pre-trained models lack domain-specific knowledge and cannot cite sources.
- ✗
Implement a rule-based search engine
Why it's wrong here
Rule-based systems are inflexible and cannot handle natural language queries effectively.
- ✗
Fine-tune a pre-trained model on the current regulations
Why it's wrong here
Fine-tuning requires retraining each quarter and cannot guarantee source citations.
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