- A
Apply instruction tuning on the generation model.
Why wrong: Instruction tuning is resource-intensive and not targeted at the immediate issue.
- B
Implement a re-ranking step using a cross-encoder model.
Re-ranking scores each chunk for relevance to the query, filtering out noise.
- C
Reduce the number of retrieved chunks from 5 to 3.
Why wrong: Reducing chunks may help but does not specifically filter out irrelevant ones.
- D
Increase the similarity threshold for retrieval from 0.7 to 0.85.
Why wrong: A higher threshold may exclude relevant chunks, reducing recall.
Question 601 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.
A team is optimizing a RAG pipeline for OCI Generative AI. They observe that the model's responses are verbose and often include irrelevant details from the retrieved chunks, reducing user satisfaction. They have already tuned the prompt template. What is the most effective next step?
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 re-ranking step using a cross-encoder model.
Option B is correct because implementing a re-ranking step with a cross-encoder model directly addresses the problem of verbose and irrelevant responses. Cross-encoders evaluate the query-document pair jointly, producing a fine-grained relevance score that filters out noisy or off-topic chunks before they reach the generation model. This improves the quality of the context provided to the LLM, reducing verbosity and irrelevance without requiring retraining or altering the retrieval threshold.
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.
- ✗
Apply instruction tuning on the generation model.
Why it's wrong here
Instruction tuning is resource-intensive and not targeted at the immediate issue.
- ✓
Implement a re-ranking step using a cross-encoder model.
Why this is correct
Re-ranking scores each chunk for relevance to the query, filtering out noise.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the number of retrieved chunks from 5 to 3.
Why it's wrong here
Reducing chunks may help but does not specifically filter out irrelevant ones.
- ✗
Increase the similarity threshold for retrieval from 0.7 to 0.85.
Why it's wrong here
A higher threshold may exclude relevant chunks, reducing recall.
Common exam traps
Common exam trap: answer the scenario, not the keyword
OCI GenAI exams often test the misconception that adjusting retrieval parameters (threshold or count) is sufficient to fix relevance issues, when in fact a dedicated re-ranking step is needed to refine the quality of the context passed to the generation model.
Detailed technical explanation
How to think about this question
Cross-encoder models, such as BERT-based rankers, compute a single relevance score by processing the query and each chunk together through a full transformer layer, unlike bi-encoders which produce independent embeddings. This joint encoding captures nuanced semantic interactions, such as negation or entity disambiguation, that cosine similarity on embeddings may miss. In a RAG pipeline, re-ranking with a cross-encoder typically reduces the top-k chunks from an initial retrieval (e.g., 20) to a smaller set (e.g., 5) for the LLM, balancing recall and precision.
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 re-ranking step using a cross-encoder model. — Option B is correct because implementing a re-ranking step with a cross-encoder model directly addresses the problem of verbose and irrelevant responses. Cross-encoders evaluate the query-document pair jointly, producing a fine-grained relevance score that filters out noisy or off-topic chunks before they reach the generation model. This improves the quality of the context provided to the LLM, reducing verbosity and irrelevance without requiring retraining or altering the retrieval threshold.
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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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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