1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A financial services company is deploying a RAG system for regulatory compliance queries. The system uses OCI Data Science to run a custom embedding model fine-tuned on regulatory documents. The index in OpenSearch uses cosine similarity and HNSW algorithm. Users report that queries containing synonyms to regulatory terms (e.g., "AML" vs "Anti-Money Laundering") often fail to retrieve relevant documents. Which combination of improvements would be MOST effective? (Assume budget and latency constraints)
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
✓
Implement a hybrid search combining keyword and vector search
Hybrid search (combining keyword (BM25) and vector search) catches exact synonym matches from text. Query expansion helps but may not be as reliable. Fine-tuning on synonyms is possible but time-consuming. Increasing HNSW m slightly improves recall but does not address synonym gap.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the `m` parameter in HNSW to improve recall
Why it's wrong here
Improves nearest neighbor search but does not solve synonym mismatch.
- ✗
Fine-tune the embedding model further on a dataset of synonyms
Why it's wrong here
Effective but requires data and training, may violate latency constraints.
- ✓
Implement a hybrid search combining keyword and vector search
Why this is correct
Hybrid search (BM25 + vector) directly captures exact term matches, bridging the synonym gap effectively.
- ✗
Use query expansion with a thesaurus before embedding
Why it's wrong here
Query expansion can help but increases complexity and may introduce noise.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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