- A
Fine-tune on general financial data.
Why wrong: Incorrect: Fine-tuning may still produce hallucinations.
- B
Use RAG with a verified corpus of regulations and reports.
Correct: Grounding in trusted data reduces hallucinations.
- C
Increase the temperature to get more creative responses.
Why wrong: Incorrect: Higher temperature increases hallucination risk.
- D
Use a larger model to improve accuracy.
Why wrong: Incorrect: Larger models can still hallucinate.
Question 854 of 991
1Z0-1127 Fundamentals of Large Language Models Practice Question
This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. 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 financial institution uses an LLM for generating investment advice. They are concerned about hallucinations. Which method is most effective?
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 RAG with a verified corpus of regulations and reports.
Option B is correct because Retrieval-Augmented Generation (RAG) grounds the LLM's output in a verified, external knowledge base (e.g., regulations and reports). By retrieving relevant documents at inference time, RAG reduces the model's reliance on its parametric memory, directly mitigating hallucinations in high-stakes domains like financial advice.
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.
- ✗
Fine-tune on general financial data.
Why it's wrong here
Incorrect: Fine-tuning may still produce hallucinations.
- ✓
Use RAG with a verified corpus of regulations and reports.
Why this is correct
Correct: Grounding in trusted data reduces hallucinations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the temperature to get more creative responses.
Why it's wrong here
Incorrect: Higher temperature increases hallucination risk.
- ✗
Use a larger model to improve accuracy.
Why it's wrong here
Incorrect: Larger models can still hallucinate.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that simply fine-tuning or scaling a model can fix hallucinations, when in fact grounding via retrieval (RAG) is the most effective technique for factual accuracy in domain-specific applications.
Detailed technical explanation
How to think about this question
RAG works by embedding a query and retrieving the top-k chunks from a vector database (e.g., using cosine similarity on embeddings from a model like text-embedding-ada-002), then concatenating those chunks with the original prompt before generation. This forces the LLM to condition its output on factual context, effectively acting as a dynamic, verifiable memory. In practice, a financial institution might use a vector store populated with SEC filings and Basel III documents, ensuring that any generated advice cites specific regulatory clauses.
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?
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use RAG with a verified corpus of regulations and reports. — Option B is correct because Retrieval-Augmented Generation (RAG) grounds the LLM's output in a verified, external knowledge base (e.g., regulations and reports). By retrieving relevant documents at inference time, RAG reduces the model's reliance on its parametric memory, directly mitigating hallucinations in high-stakes domains like financial advice.
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: 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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