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

A RAG application is hallucinating because the LLM receives irrelevant context from the retrieval step, even when topK is set to 3. Which strategy would best reduce hallucination by improving the relevance of retrieved documents?

⚠ Common exam trap

Oracle often tests the misconception that simply adjusting retrieval parameters (like chunk size or topK) can fix relevance issues, when the real solution is a dedicated reranking step that re-evaluates relevance with a more powerful model.

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

Add a reranking step after retrieval to select the most relevant chunks

Adding a reranking step after retrieval directly addresses the core issue: even with a low topK, the initial retrieval may return chunks that are semantically similar but not precisely relevant to the query. Reranking uses a cross-encoder model to score each retrieved chunk against the query, reordering them so that only the most contextually relevant chunks are passed to the LLM. This reduces the chance of the LLM receiving irrelevant context, thereby minimizing hallucination.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Reduce the chunk size to one sentence per chunk

    Why it's wrong here

    May lose necessary context.

  • Add a reranking step after retrieval to select the most relevant chunks

    Why this is correct

    Reranking improves the relevance of the final context set.

  • Implement a query rewriting mechanism

    Why it's wrong here

    Query rewriting may help but does not directly fix irrelevant retrieval.

  • Increase topK to 10 to provide more context

    Why it's wrong here

    More context often includes more irrelevant information, potentially increasing hallucination.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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