1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
Which TWO actions are best practices when deploying a RAG application using OCI OpenSearch and OCI Generative AI?
⚠ Common exam trap
Oracle often tests the misconception that real-time embedding (Option A) is efficient for RAG, when in fact pre-computed embeddings are standard, and that very small chunks (Option C) improve granularity, whereas they actually harm context coherence and retrieval quality.
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
✓
Implement a reranker to improve the relevance of retrieved documents.
Implementing a reranker improves retrieval precision by re-scoring the top-k documents from the initial vector search using a cross-encoder model, which captures deeper semantic relevance than cosine similarity alone. In OCI OpenSearch, this is typically done via a post-processing step with OCI Generative AI or a dedicated reranking model, ensuring only the most contextually relevant chunks are passed to the LLM for generation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Embed every document chunk in real-time during query processing.
Why it's wrong here
Prefer offline embedding.
- ✓
Implement a reranker to improve the relevance of retrieved documents.
Why this is correct
Improves precision.
- ✗
Use very small chunk sizes (e.g., 50 tokens) to maximize granularity.
Why it's wrong here
Too small loses context.
- ✓
Monitor query latency and adjust the number of retrieved documents accordingly.
Why this is correct
Balances performance and quality.
- ✗
Set the LLM temperature to 1.5 to encourage diverse outputs.
Why it's wrong here
Too high temperature causes hallucination.
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