1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A developer notices that the RAG system returns irrelevant chunks when the user query contains typos or abbreviations. Which technique would BEST improve retrieval robustness for such queries?
⚠ Common exam trap
Oracle often tests the misconception that retrieval robustness can be improved by tuning chunk size or retrieval count, when the real bottleneck is the quality of the query embedding itself.
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 query rewriting or expansion using a language model before embedding.
Query rewriting or expansion using a language model (LLM) directly addresses typos and abbreviations by generating a corrected or enriched query before embedding. This improves the semantic alignment between the user's intent and the vector search, ensuring that even noisy input retrieves relevant chunks. Techniques like spelling correction or synonym expansion at query time are far more effective than post-retrieval fixes or parameter tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the chunk size to focus on smaller units.
Why it's wrong here
Smaller chunks may lose context and still not match the malformed query.
- ✗
Increase the number of retrieved chunks to cover more variations.
Why it's wrong here
More chunks may include irrelevant ones without addressing the query.
- ✗
Use a spell-checker on the retrieved chunks.
Why it's wrong here
Spell-checking chunks does not address the query typo.
- ✓
Implement query rewriting or expansion using a language model before embedding.
Why this is correct
Rewriting corrects typos and expands abbreviations, improving embedding quality.
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