- A
Reduce the 'topP' parameter to 0.1 to force the model to sample only from the highest probability tokens, thereby reducing randomness.
Why wrong: Reducing topP makes outputs more deterministic but does not add factual knowledge, so hallucinations may persist.
- B
Implement a Retrieval Augmented Generation (RAG) pipeline that retrieves relevant product documents from the internal database and prepends them to the prompt before inference.
RAG injects accurate, domain-specific context, improving factual accuracy without fine-tuning, and can be implemented with efficient retrieval for low latency.
- C
Use few-shot prompting with three manually curated examples of correct product specifications included in every prompt.
Why wrong: Few-shot prompting can help but is limited by context window, may increase latency due to longer prompts, and does not scale to many niche products.
- D
Increase the 'temperature' parameter to make the model more creative, hoping it will generate more accurate responses when unsure.
Why wrong: Increasing temperature increases randomness and likely worsens hallucinations.
Quick Answer
The correct choice is implementing a Retrieval Augmented Generation (RAG) pipeline that retrieves relevant product documents from the internal database and prepends them to the prompt before inference. This approach directly addresses the core challenge of reducing hallucination with RAG retrieval augmented generation by grounding the LLM’s output in authoritative, up-to-date context from the company’s internal database, eliminating the need for costly fine-tuning while providing factual accuracy for niche products. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this scenario tests your understanding of how OCI’s on-demand serving mode can integrate with vector search or knowledge bases to supply real-time context without sacrificing the required sub-2-second latency. A common trap is confusing creativity parameters like temperature or topP with factual grounding—these only control randomness, not knowledge. Remember the mnemonic: “RAG fills the gap, temperature just the snap.”
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 multinational corporation is deploying a generative AI chatbot for customer support using Oracle Cloud Infrastructure's Generative AI service. The chatbot is powered by a large language model (LLM) accessed via the on-demand serving mode. During initial testing, the chatbot provides accurate answers for well-known products but frequently hallucinates or gives incorrect specifications for niche products. The company maintains a comprehensive internal database of product specifications, updated daily. The support team prefers not to fine-tune the LLM due to cost and maintenance overhead. Additionally, the chatbot must respond within 2 seconds to maintain a good customer experience. The team considers several approaches: A. Increasing the 'temperature' parameter to make the model more creative, hoping it will generate more accurate responses when unsure. B. Using few-shot prompting with three manually curated examples of correct product specifications included in every prompt. C. Implementing a Retrieval Augmented Generation (RAG) pipeline that retrieves relevant product documents from the internal database and prepends them to the prompt before inference. D. Reducing the 'topP' parameter to 0.1 to force the model to sample only from the highest probability tokens, thereby reducing randomness. Which approach best meets the requirements of improving factual accuracy while maintaining low latency?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 Retrieval Augmented Generation (RAG) pipeline that retrieves relevant product documents from the internal database and prepends them to the prompt before inference.
Option A is correct because Retrieval Augmented Generation (RAG) provides relevant, up-to-date context from the internal database, improving factual accuracy without fine-tuning, and can be optimized for low latency. Option B (few-shot) is limited by context window size and increases token usage, potentially increasing latency. Option C (increasing temperature) is counterproductive as it increases randomness. Option D (reducing topP) does not add factual knowledge and may reduce output quality.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the 'topP' parameter to 0.1 to force the model to sample only from the highest probability tokens, thereby reducing randomness.
Why it's wrong here
Reducing topP makes outputs more deterministic but does not add factual knowledge, so hallucinations may persist.
- ✓
Implement a Retrieval Augmented Generation (RAG) pipeline that retrieves relevant product documents from the internal database and prepends them to the prompt before inference.
Why this is correct
RAG injects accurate, domain-specific context, improving factual accuracy without fine-tuning, and can be implemented with efficient retrieval for low latency.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Use few-shot prompting with three manually curated examples of correct product specifications included in every prompt.
Why it's wrong here
Few-shot prompting can help but is limited by context window, may increase latency due to longer prompts, and does not scale to many niche products.
- ✗
Increase the 'temperature' parameter to make the model more creative, hoping it will generate more accurate responses when unsure.
Why it's wrong here
Increasing temperature increases randomness and likely worsens hallucinations.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Trap categories for this question
Command / output trap
Reducing topP makes outputs more deterministic but does not add factual knowledge, so hallucinations may persist.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related 1Z0-1127 NAT questions on configuration and troubleshooting.
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Fundamentals of Large Language Models — study guide chapter
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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 — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Implement a Retrieval Augmented Generation (RAG) pipeline that retrieves relevant product documents from the internal database and prepends them to the prompt before inference. — Option A is correct because Retrieval Augmented Generation (RAG) provides relevant, up-to-date context from the internal database, improving factual accuracy without fine-tuning, and can be optimized for low latency. Option B (few-shot) is limited by context window size and increases token usage, potentially increasing latency. Option C (increasing temperature) is counterproductive as it increases randomness. Option D (reducing topP) does not add factual knowledge and may reduce output quality.
What should I do if I get this 1Z0-1127 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related 1Z0-1127 NAT questions on configuration and troubleshooting.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Same concept, more angles
1 more ways this is tested on 1Z0-1127
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 uses OCI Generative AI to power a chatbot for customer support. They notice that the model's responses sometimes contain factual inaccuracies. Which strategy would best reduce hallucination?
medium- ✓ A.Implementing Retrieval-Augmented Generation (RAG).
- B.Increasing the temperature parameter.
- C.Reducing the max token limit.
- D.Fine-tuning the model on a larger general corpus.
Why A: Retrieval-Augmented Generation (RAG) grounds the model's responses in retrieved factual information, directly reducing hallucination. Increasing temperature increases randomness, fine-tuning on a larger corpus may not fix factual accuracy, and reducing max tokens does not affect correctness.
Last reviewed: Jun 23, 2026
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