1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A developer is building a RAG application using Oracle Cloud Infrastructure (OCI) Document Understanding and OCI Generative AI. After chunking documents and generating embeddings, the developer observes that the retrieval step often returns chunks that are semantically unrelated to the query. Which action is MOST likely to improve retrieval relevance?
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
✓
Adjust the chunk size and chunk overlap to better capture coherent passages.
Adjusting chunk size and overlap helps create coherent chunks that align with query intent, improving retrieval relevance. Option A is wrong because the embedding model type (dense vs. sparse) affects retrieval method but does not directly fix chunk coherence issues. Option C is wrong because increasing chunk size may introduce noise and irrelevant context. Option D is wrong because reducing the number of retrieved chunks (k) only limits results, not improves relevance of individual chunks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch from a dense embedding model to a sparse embedding model.
Why it's wrong here
Switching from a dense to a sparse embedding model changes retrieval algorithm but does not address chunk coherence; the issue is likely chunk boundaries, not model type.
- ✓
Adjust the chunk size and chunk overlap to better capture coherent passages.
Why this is correct
Adjusting chunk size and overlap directly improves chunk coherence, making retrieved passages more semantically related to the query.
- ✗
Increase the chunk size to capture more context.
Why it's wrong here
Increasing chunk size may add more context but also introduces noise; without appropriate overlap, relevance may degrade.
- ✗
Reduce the number of retrieved chunks (k) in the vector search.
Why it's wrong here
Reducing k retrieves fewer chunks, but those chunks may still be irrelevant; it does not solve the core chunking problem.
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Same concept, more angles
1 more way this is tested on 1Z0-1127-25
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. When building a RAG application for document retrieval, which chunking strategy is recommended to maximize retrieval accuracy?
easy- A.Use fixed-size token chunks with no overlap
- ✓ B.Use overlapping chunks with a sliding window
- C.Use random splitting points
- D.Use entire documents as single chunks
Why B: Overlapping chunks with a sliding window ensure that context is preserved across chunk boundaries, which is critical for retrieval accuracy in RAG applications. When a query spans the boundary between two fixed-size chunks, the overlap captures the relevant context in both chunks, reducing the risk of missing key information. This strategy directly addresses the limitation of fixed-size token chunks that may split semantically related content.
JA
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