- A
Increase the temperature parameter to make outputs more focused.
Why wrong: Higher temperature increases randomness, likely producing more hallucinations.
- B
Provide clear instructions in the system prompt to answer only based on the provided context.
Explicit grounding instructions guide the model to stick to retrieved documents, reducing unsupported claims.
- C
Use a smaller LLM to reduce model capacity.
Why wrong: Smaller models are more prone to hallucination, not less.
- D
Retrieve more chunks (increase top-k) to provide more context.
Why wrong: More chunks can include irrelevant or contradictory information, potentially increasing hallucinations.
Question 95 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.
An enterprise is using OCI Generative AI with a RAG architecture. They observe that the LLM sometimes produces hallucinated answers that are not supported by the retrieved documents. Which strategy is most effective in reducing these hallucinations?
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
✓
Provide clear instructions in the system prompt to answer only based on the provided context.
Option B is correct because explicitly instructing the LLM to answer only based on the provided context directly addresses the root cause of hallucinations in a RAG pipeline: the model's tendency to rely on its parametric knowledge rather than the retrieved documents. This system prompt acts as a behavioral constraint, forcing the model to ground its responses in the supplied context, which is the most effective and widely recommended mitigation strategy.
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.
- ✗
Increase the temperature parameter to make outputs more focused.
Why it's wrong here
Higher temperature increases randomness, likely producing more hallucinations.
- ✓
Provide clear instructions in the system prompt to answer only based on the provided context.
Why this is correct
Explicit grounding instructions guide the model to stick to retrieved documents, reducing unsupported claims.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a smaller LLM to reduce model capacity.
Why it's wrong here
Smaller models are more prone to hallucination, not less.
- ✗
Retrieve more chunks (increase top-k) to provide more context.
Why it's wrong here
More chunks can include irrelevant or contradictory information, potentially increasing hallucinations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that increasing context quantity (top-k) or adjusting model parameters like temperature will solve hallucinations, when in fact the most reliable solution is explicit behavioral instruction through system prompts.
Detailed technical explanation
How to think about this question
Under the hood, RAG hallucinations occur when the LLM's internal parametric knowledge overrides the retrieved context during generation. The system prompt acts as a 'context grounding' directive that biases the attention mechanism toward the provided documents, effectively suppressing the model's reliance on its training data. In production systems, this is often combined with prompt engineering techniques like 'chain-of-thought' or 'structured output' constraints to further enforce adherence to retrieved evidence.
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: Provide clear instructions in the system prompt to answer only based on the provided context. — Option B is correct because explicitly instructing the LLM to answer only based on the provided context directly addresses the root cause of hallucinations in a RAG pipeline: the model's tendency to rely on its parametric knowledge rather than the retrieved documents. This system prompt acts as a behavioral constraint, forcing the model to ground its responses in the supplied context, which is the most effective and widely recommended mitigation strategy.
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
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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