Building LLM Applications with RAG and Vector Search →mediumMultiple ChoiceObjective-mapped

1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

A company is building a RAG application using OCI Generative AI and OCI Search with OpenSearch. Users report that the responses from the LLM are not relevant to the queries, even though the document chunks seem appropriate. What is the most likely cause?

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

✓

Reranking is not enabled in the OpenSearch query.

Enabling reranking improves the relevance of retrieved documents by reordering them based on semantic match with the query. Without reranking, the initial vector search results may not be optimally ordered.

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 suited for the domain.
Why it's wrong here
While possible, the most common fix is reranking.
✓
Reranking is not enabled in the OpenSearch query.
Why this is correct
Reranking reorders search results for better relevance, significantly impacting quality.
✗
The top K value is set too high.
Why it's wrong here
Top K affects number of chunks, not necessarily relevance ordering.
✗
The chunk size is too small, causing loss of context.
Why it's wrong here
Chunk size affects detail, but relevance issue is more likely due to ordering.

Go deeper

1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Why each option matters

Option-by-option breakdown

Related to this question