The correct answer is Retrieval-Augmented Generation (RAG) with a verified corpus of regulations and reports. This method is most effective because RAG grounds the LLM’s output in a trusted, external knowledge base, retrieving relevant documents at inference time rather than relying solely on the model’s parametric memory, which is the primary source of hallucinations. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this concept tests your understanding of how to ensure factual accuracy in high-stakes domains like finance, where unverified model outputs are unacceptable. A common trap is choosing fine-tuning alone, which adjusts weights but does not prevent the model from inventing facts when it lacks specific data. Memory tip: think “RAG retrieves, fine-tuning forgets”—RAG always pulls from a verified source, making it the gold standard for mitigating LLM hallucinations in regulated industries.
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 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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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.
About these practice questions
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company fine-tunes an LLM on internal support tickets. After deployment, the model hallucinates company-specific product names. What is the most effective mitigation?
hard
A.Switch to a smaller model to reduce hallucination risk
B.Use prompt engineering to remind the model to be accurate
✓ C.Implement RAG with a verified product database
D.Fine-tune further with more ticket data
Why C: RAG (Retrieval-Augmented Generation) grounds the LLM's output in a verified product database, providing factual context that prevents hallucination of company-specific product names. Unlike fine-tuning, which only adjusts model weights and can still produce plausible but incorrect names, RAG retrieves exact records at inference time, ensuring accuracy for proprietary terminology.
Last reviewed: Jun 30, 2026
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