1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A startup is building a customer support chatbot using RAG with OCI Generative AI. They have a large corpus of FAQ documents stored as PDFs in OCI Object Storage. The developer uses OCI Language to embed the text and stores vectors in OCI OpenSearch. During testing, the chatbot often fails to answer questions because relevant FAQ entries are not retrieved. The team suspects the chunking size is too large, causing loss of specific details. After reducing chunk size, retrieval improves slightly but still misses many answers. What should the team do NEXT?
⚠ Common exam trap
OCI often tests the misconception that simply reducing chunk size or increasing k is sufficient to fix retrieval failures, when in fact the real issue is the lack of context continuity across chunks—a sliding window with overlap is the standard solution in production RAG systems.
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
✓
Use a sliding window chunking strategy with overlap
A sliding window chunking strategy with overlap ensures that context is preserved across chunk boundaries, preventing the loss of specific details that can occur when a relevant sentence or phrase is split between two chunks. This directly addresses the symptom where reducing chunk size alone still misses answers, as overlapping chunks increase the likelihood that the exact text needed for retrieval appears in at least one chunk.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a sliding window chunking strategy with overlap
Why this is correct
Overlap preserves context across chunk boundaries, improving recall.
- ✗
Increase the number of retrieved chunks (k)
Why it's wrong here
This may introduce more irrelevant chunks and increase noise.
- ✗
Switch to a different embedding model
Why it's wrong here
Model change is costly and may not address chunk boundary issues.
- ✗
Manually rephrase the queries
Why it's wrong here
Not scalable and may not fix the underlying retrieval problem.
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Written by Johnson Ajibi, MSc IT Security
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