1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A RAG system returns irrelevant chunks even though the embedding model and vector index are correctly configured. After reviewing, the chunks are too large and contain extraneous information. Which combination of adjustments should be made to improve relevance?
⚠ Common exam trap
Oracle often tests the misconception that only one parameter (like chunk size or topK) needs adjustment, when in reality a combination of chunk size, overlap, and topK tuning is required to address both chunk granularity and retrieval count.
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
✓
Reduce chunk size, increase overlap, and adjust topK.
Reducing chunk size removes extraneous information, increasing overlap ensures context continuity across smaller chunks, and adjusting topK limits the number of retrieved chunks to the most relevant ones. This combination directly addresses the problem of large chunks containing irrelevant data while maintaining retrieval precision.
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 chunk overlap only.
Why it's wrong here
Overlap alone may not fix irrelevance caused by large chunks.
- ✗
Decrease chunk size and increase chunk overlap.
Why it's wrong here
This is part of the solution, but topK may also need tuning.
- ✗
Use semantic chunking and adjust topK.
Why it's wrong here
Semantic chunking helps, but other parameters may still need adjustment.
- ✓
Reduce chunk size, increase overlap, and adjust topK.
Why this is correct
All three adjustments can help refine the retrieved context.
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