1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A developer is building a RAG application using OCI Generative AI. They notice that the generated responses often contain outdated information even though the knowledge base is updated daily. What is the most likely cause?
⚠ Common exam trap
Oracle often tests the misconception that embedding model fine-tuning or chunk size adjustments are the primary cause of outdated responses, when in fact the root cause is the failure to rebuild or update the vector index after data changes.
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
✓
The vector database index is not rebuilt after data updates.
In a RAG pipeline, the vector database index is a static snapshot of the embedded knowledge base. When the knowledge base is updated daily, the index must be rebuilt or incrementally updated to reflect the new data. Without rebuilding, the retrieval step will still search the old index, returning outdated chunks and causing the LLM to generate stale responses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The embedding model is not fine-tuned on the latest data.
Why it's wrong here
Fine-tuning is not necessary for knowledge freshness; the model encodes semantics.
- ✓
The vector database index is not rebuilt after data updates.
Why this is correct
If the index is not refreshed, new data is not searchable, leading to outdated results.
- ✗
The retrieval top-k is set too high.
Why it's wrong here
A high top-k retrieves more chunks but does not affect the freshness of data.
- ✗
The chunk size is too small, causing loss of context.
Why it's wrong here
Chunk size affects context but not the freshness of retrieved chunks.
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