- A
Use a larger LLM for generation, hoping it ignores irrelevant chunks.
Why wrong: LLMs do not inherently filter irrelevant context; they may be misled by it.
- B
Reduce the top-k retrieval count.
Why wrong: Reducing top-k may remove both irrelevant and relevant chunks, lowering recall.
- C
Implement a reranking step using a cross-encoder model.
Reranking with a cross-encoder (e.g., Cohere rerank) reorders chunks by relevance to the query, filtering out irrelevant ones.
- D
Increase the chunk size to provide more context.
Why wrong: Larger chunks may introduce more irrelevant information.
Question 458 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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
During a RAG implementation, the response quality degrades because the LLM receives too many irrelevant document chunks. Which technique can best filter out irrelevant chunks before sending them to the LLM?
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 reranking step using a cross-encoder model.
A reranking step using a cross-encoder model (Option C) directly addresses the problem of irrelevant chunks by scoring each retrieved chunk against the user query with a deep, pairwise similarity computation. Unlike a bi-encoder used in initial retrieval, a cross-encoder evaluates query-chunk pairs jointly, allowing it to reorder or filter out chunks that are semantically irrelevant, thus improving the quality of context fed to the LLM.
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.
- ✗
Use a larger LLM for generation, hoping it ignores irrelevant chunks.
Why it's wrong here
LLMs do not inherently filter irrelevant context; they may be misled by it.
- ✗
Reduce the top-k retrieval count.
Why it's wrong here
Reducing top-k may remove both irrelevant and relevant chunks, lowering recall.
- ✓
Implement a reranking step using a cross-encoder model.
Why this is correct
Reranking with a cross-encoder (e.g., Cohere rerank) reorders chunks by relevance to the query, filtering out irrelevant ones.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the chunk size to provide more context.
Why it's wrong here
Larger chunks may introduce more irrelevant information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle OCI GenAI exams often test the misconception that simply adjusting retrieval parameters (like top-k or chunk size) can fix relevance issues, when in fact a dedicated reranking model is required to re-evaluate semantic relevance at a deeper level.
Detailed technical explanation
How to think about this question
Cross-encoders, such as those based on BERT or RoBERTa, process the query and each chunk as a single input sequence, outputting a relevance score via a classification head. This pairwise computation is more accurate than bi-encoder cosine similarity but is O(n) per chunk, making it suitable as a reranker on a small candidate set (e.g., top-20 to top-100) rather than for full corpus retrieval. In production RAG pipelines, a common pattern is to use a lightweight bi-encoder for initial retrieval (e.g., using FAISS or HNSW) and then apply a cross-encoder reranker to refine the top-k results before LLM generation.
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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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: Implement a reranking step using a cross-encoder model. — A reranking step using a cross-encoder model (Option C) directly addresses the problem of irrelevant chunks by scoring each retrieved chunk against the user query with a deep, pairwise similarity computation. Unlike a bi-encoder used in initial retrieval, a cross-encoder evaluates query-chunk pairs jointly, allowing it to reorder or filter out chunks that are semantically irrelevant, thus improving the quality of context fed to the LLM.
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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