1Z0-1127-25 LangChain and AI Application Development Practice Question
In a LangChain RAG pipeline using Oracle AI Vector Search, the developer wants to retrieve chunks that are both relevant and diverse to cover multiple aspects of a query. Which retrieval method should they configure on the retriever?
⚠ Common exam trap
The 1Z0-1127 exam often tests the misconception that simply increasing k or using a threshold will naturally yield diverse results, but candidates fail to recognize that without an explicit diversity mechanism like MMR, similarity-based retrievers inherently favor redundancy over coverage.
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
✓
Maximal Marginal Relevance (MMR)
Maximal Marginal Relevance (MMR) is the correct retrieval method because it explicitly balances relevance to the query with diversity among the retrieved chunks. In a LangChain RAG pipeline using Oracle AI Vector Search, MMR re-ranks the initial similarity results to minimize redundancy, ensuring the final set covers multiple aspects of the query rather than returning near-duplicate chunks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Threshold-based search
Why it's wrong here
Threshold search returns all chunks above a similarity threshold, which does not enforce diversity.
- ✓
Maximal Marginal Relevance (MMR)
Why this is correct
MMR balances relevance and diversity by iteratively selecting documents that are dissimilar to already chosen ones.
- ✗
Random sampling of the top-k results
Why it's wrong here
Random sampling does not guarantee diversity; it may still select similar chunks.
- ✗
Similarity search with a high k value
Why it's wrong here
High k returns many results but they may all be very similar, lacking diversity.
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