1Z0-1127-25 LLM Fundamentals Practice Question
A developer notices that an LLM-based question-answering system sometimes provides answers that are correct but from an outdated version of the knowledge base. The system uses RAG with a vector database updated daily. What is the MOST likely root cause?
⚠ Common exam trap
Oracle OCI GenAI exams often test the distinction between retrieval-side issues (index staleness) and model-side issues (knowledge cutoff), so candidates mistakenly pick D because they confuse the LLM's training cutoff with the freshness of the vector database index.
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 embedding model was not re-run on the updated documents, so the index contains old embeddings
The core issue is that the vector database index still contains old embeddings. Even though the knowledge base documents are updated daily, if the embedding model is not re-run on those updated documents, the vector representations in the index remain stale. When the RAG system retrieves, it fetches these outdated embeddings, leading to correct but outdated answers. This is a classic index synchronization problem in RAG pipelines.
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 retrieval top-k parameter is set too high
Why it's wrong here
Higher top-k may include less relevant chunks, but the core issue is the index containing outdated embeddings.
- ✗
The chunking strategy splits documents into too-small pieces
Why it's wrong here
Chunk size affects retrieval quality but not freshness; outdated information is a different issue.
- ✓
The embedding model was not re-run on the updated documents, so the index contains old embeddings
Why this is correct
If the vector database is updated but embeddings are not recomputed, the index still matches old chunks, causing retrieval of outdated information.
- ✗
The LLM's training data has a knowledge cutoff date
Why it's wrong here
The knowledge cutoff of the base model is separate from the RAG pipeline; the issue is with retrieved content, not the model's pre-training.
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