1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
Which TWO of the following are best practices for building a RAG pipeline in OCI?
⚠ Common exam trap
A common misconception in OCI RAG pipelines is that exact vector search is always superior for accuracy. In practice, approximate nearest neighbor (ANN) search in OCI Search with OpenSearch or OCI Generative AI's vector database achieves equivalent recall while being much faster.
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 overlapping chunks
Overlapping chunks ensure that context is not lost at chunk boundaries, which is critical for retrieval accuracy in RAG pipelines. By including overlapping text segments, the embedding model can capture semantic continuity, reducing the risk of missing relevant information when a query spans chunk edges.
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 overlapping chunks
Why this is correct
Overlapping chunks preserve context across boundaries, improving retrieval.
- ✗
Always use exact vector search for accuracy
Why it's wrong here
Exact search is slower and often unnecessary; ANN is a practical choice.
- ✓
Use a pre-trained embedding model from OCI Generative AI
Why this is correct
Pre-trained models are optimized and ready for most use cases.
- ✗
Avoid storing metadata alongside vectors
Why it's wrong here
Metadata enables filtering and improves result relevance.
- ✗
Use a single large chunk for each document
Why it's wrong here
Single large chunks may exceed token limits and reduce retrieval precision.
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