- 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.
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?
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 B is correct because Retrieval Augmented Generation (RAG) directly addresses the hallucination problem by providing the LLM with up-to-date, factual product specifications from the internal database as context in the prompt. This approach improves factual accuracy without fine-tuning, and because retrieval can be optimized (e.g., using vector search with approximate nearest neighbor algorithms), it can meet the 2-second latency requirement. RAG leverages the LLM's existing knowledge while grounding responses in authoritative external data.
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.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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: answer the scenario, not the keyword
The trap here is that candidates may confuse parameter tuning (temperature, topP) with a method to improve factual accuracy in OCI Generative AI, when in fact these parameters control randomness, not knowledge grounding; the real solution is to leverage Oracle's RAG pattern using OCI Data Science or OpenSearch to provide external factual context.
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
RAG pipelines typically use an embedding model to convert documents into vector representations, store them in a vector database (e.g., OCI OpenSearch), and retrieve the top-k most relevant chunks via cosine similarity search at inference time. The retrieved chunks are prepended to the user query in the prompt, allowing the LLM to generate answers grounded in those documents. To maintain sub-2-second latency, the retrieval step must be efficient—often using approximate nearest neighbor (ANN) indexing and caching frequent queries—and the LLM inference itself must be optimized (e.g., using OCI Generative AI's on-demand serving with batching or model quantization).
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 network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
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: 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 B is correct because Retrieval Augmented Generation (RAG) directly addresses the hallucination problem by providing the LLM with up-to-date, factual product specifications from the internal database as context in the prompt. This approach improves factual accuracy without fine-tuning, and because retrieval can be optimized (e.g., using vector search with approximate nearest neighbor algorithms), it can meet the 2-second latency requirement. RAG leverages the LLM's existing knowledge while grounding responses in authoritative external data.
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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