- A
Reduce the chunk size to one sentence per chunk
Why wrong: May lose necessary context.
- B
Add a reranking step after retrieval to select the most relevant chunks
Reranking improves the relevance of the final context set.
- C
Implement a query rewriting mechanism
Why wrong: Query rewriting may help but does not directly fix irrelevant retrieval.
- D
Increase topK to 10 to provide more context
Why wrong: More context often includes more irrelevant information, potentially increasing hallucination.
Question 797 of 991
1Z0-1127 Practice Question: Building LLM Applications with RAG and Vector Search
This 1Z0-1127 practice question tests your understanding of building llm applications with rag and vector search. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Reranking typically employs a cross-encoder like BERT or a specialized model such as Cohere Rerank, which computes a relevance score for each (query, chunk) pair in a single forward pass, unlike the bi-encoder used in initial retrieval that relies on approximate nearest neighbor search. This two-stage retrieval (bi-encoder for candidate retrieval, cross-encoder for reranking) is a standard pattern in production RAG systems to balance speed and accuracy. In real-world scenarios, even with topK=3, the initial retrieval might include a chunk that is topically related but answers a different aspect of the query, and reranking effectively filters that out.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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Building LLM Applications with RAG and Vector Search — study guide chapter
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
Building LLM Applications with RAG and Vector Search — This question tests Building LLM Applications with RAG and Vector Search — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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