- A
Use a more detailed system prompt instructing the model to not make up information.
Why wrong: Prompts are not a reliable enforcement mechanism.
- B
Increase the temperature parameter of the LLM to reduce creativity.
Why wrong: Higher temperature increases creativity, not reduces it.
- C
Implement a post-generation verification step that checks if the answer is grounded in the retrieved chunks.
Directly verifies faithfulness.
- D
Increase the number of retrieved documents to provide more context.
Why wrong: May increase hallucinations due to conflicting information.
Question 239 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 financial firm deploys a RAG application using OCI OpenSearch. They observe that the LLM sometimes generates incorrect answers that are not supported by the retrieved documents. Which technique directly addresses this issue?
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 post-generation verification step that checks if the answer is grounded in the retrieved chunks.
Option C is correct because it directly addresses the problem of hallucination by verifying that the LLM's output is factually supported by the retrieved documents. In a RAG pipeline, the LLM may still generate unsupported content even with good retrieval; a post-generation grounding check explicitly validates each claim against the source chunks, ensuring answer fidelity.
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 more detailed system prompt instructing the model to not make up information.
Why it's wrong here
Prompts are not a reliable enforcement mechanism.
- ✗
Increase the temperature parameter of the LLM to reduce creativity.
Why it's wrong here
Higher temperature increases creativity, not reduces it.
- ✓
Implement a post-generation verification step that checks if the answer is grounded in the retrieved chunks.
Why this is correct
Directly verifies faithfulness.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of retrieved documents to provide more context.
Why it's wrong here
May increase hallucinations due to conflicting information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that prompt engineering or parameter tuning alone can solve hallucination in RAG, when in fact a dedicated verification step is required to enforce factual grounding.
Detailed technical explanation
How to think about this question
Post-generation grounding verification typically uses techniques like entailment classification (e.g., using a BERT-based NLI model) to check if each sentence in the answer is entailed by the retrieved chunks. In production, this can be implemented as a separate validation step that rejects or flags answers with low grounding scores, often combined with a fallback mechanism to re-query or abstain. This approach is critical in regulated industries like finance, where unsupported claims can lead to compliance violations.
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
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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 post-generation verification step that checks if the answer is grounded in the retrieved chunks. — Option C is correct because it directly addresses the problem of hallucination by verifying that the LLM's output is factually supported by the retrieved documents. In a RAG pipeline, the LLM may still generate unsupported content even with good retrieval; a post-generation grounding check explicitly validates each claim against the source chunks, ensuring answer fidelity.
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: Jun 30, 2026
This 1Z0-1127 practice question is part of Courseiva's free Oracle certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 1Z0-1127 exam.
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