- A
Use a larger retrieval chunk size
Why wrong: More context may help, but doesn't directly enforce factual grounding.
- B
Increase the temperature
Why wrong: Higher temperature increases randomness, worsening hallucinations.
- C
Use prompt engineering to instruct the model to stick to the provided context
Explicitly instructing the model to base answers only on the given context reduces hallucination.
- D
Decrease the maximum token length
Why wrong: Shorter outputs may still hallucinate.
Question 519 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 developer uses OCI Generative AI with a custom OCI OpenSearch vector store. The text generation model sometimes hallucinates facts not in the retrieved documents. What is the most effective mitigation?
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
✓
Use prompt engineering to instruct the model to stick to the provided context
Prompt engineering that instructs the model to strictly adhere to the provided context is the most effective mitigation because it directly addresses the root cause of hallucination: the model's tendency to generate information beyond the retrieved documents. By explicitly constraining the model's behavior through the system or user prompt, you reduce the likelihood of fabricated facts without altering the retrieval or generation parameters.
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 retrieval chunk size
Why it's wrong here
More context may help, but doesn't directly enforce factual grounding.
- ✗
Increase the temperature
Why it's wrong here
Higher temperature increases randomness, worsening hallucinations.
- ✓
Use prompt engineering to instruct the model to stick to the provided context
Why this is correct
Explicitly instructing the model to base answers only on the given context reduces hallucination.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Decrease the maximum token length
Why it's wrong here
Shorter outputs may still hallucinate.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that adjusting retrieval parameters (like chunk size) or generation parameters (like temperature or token length) can fix hallucination, when in fact the most direct and reliable solution is to constrain the model's behavior through prompt engineering.
Trap categories for this question
Command / output trap
Shorter outputs may still hallucinate.
Detailed technical explanation
How to think about this question
Under the hood, OCI Generative AI models use a transformer architecture where the attention mechanism weighs the provided context. Prompt engineering works by injecting a system-level instruction (e.g., 'Only answer using the provided documents; if unsure, say you don't know') that biases the model's probability distribution toward tokens grounded in the context. In a RAG pipeline with OCI OpenSearch, this is often implemented as a prepended system message in the prompt template, which the model treats as a hard constraint during generation, effectively reducing the probability of hallucinated tokens.
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: Use prompt engineering to instruct the model to stick to the provided context — Prompt engineering that instructs the model to strictly adhere to the provided context is the most effective mitigation because it directly addresses the root cause of hallucination: the model's tendency to generate information beyond the retrieved documents. By explicitly constraining the model's behavior through the system or user prompt, you reduce the likelihood of fabricated facts without altering the retrieval or generation parameters.
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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