The answer is retrieval-augmented generation (RAG), which directly reduces hallucinations by grounding the model in relevant documents. RAG combats hallucination by fetching external, verifiable context—such as the original customer support ticket—at inference time, forcing the model to align its summary with retrieved evidence rather than relying solely on its parametric memory. This preserves summary quality because the model can still generate fluent, coherent text while being constrained to factual source material. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this concept tests your understanding of how to mitigate model confabulation in enterprise summarization tasks; a common trap is choosing prompt engineering alone, which lacks the grounding mechanism of RAG. Remember the mnemonic “RAG Retrieves, Grounds, and Guards” to recall that RAG retrieves evidence, grounds output in facts, and guards against hallucination.
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 data scientist is using a large language model to summarize customer support tickets. The model occasionally generates summaries that include hallucinated details not present in the original ticket. Which technique would best reduce hallucinations while maintaining summary quality?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement retrieval-augmented generation (RAG) to ground the model in relevant documents.
Retrieval-Augmented Generation (RAG) reduces hallucinations by grounding the model's output in external, verifiable documents retrieved from a knowledge base. Instead of relying solely on the model's parametric memory, RAG fetches relevant context (e.g., the original ticket) at inference time, ensuring the summary is factually aligned with the source. This maintains summary quality because the model can still generate fluent text while being constrained to the retrieved evidence.
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.
✓
Implement retrieval-augmented generation (RAG) to ground the model in relevant documents.
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a longer system prompt instructing the model to be factual.
Why it's wrong here
Prompt engineering alone is often insufficient to eliminate hallucinations.
✗
Fine-tune the model on a large corpus of general text to improve its knowledge.
Why it's wrong here
Fine-tuning on general data does not specifically target hallucination reduction.
✗
Increase the temperature parameter to 0.9 to encourage more deterministic outputs.
Why it's wrong here
Increasing temperature increases randomness, which can worsen hallucinations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that simply instructing the model to be factual (Option B) or fine-tuning (Option C) can eliminate hallucinations, when in reality grounding via retrieval (RAG) is the only technique that directly supplies external evidence to constrain generation.
Detailed technical explanation
How to think about this question
RAG works by embedding the input query (e.g., a ticket summary request) and performing a similarity search against a vector database of indexed documents (e.g., ticket histories). The top-k retrieved chunks are concatenated with the original prompt before being fed to the LLM, effectively constraining the generation to the retrieved context. A subtle behavior is that if the retrieval step fails to return relevant documents (e.g., due to poor embedding quality or low k), the model may still hallucinate, so tuning the retrieval pipeline (e.g., chunk size, overlap, embedding model) is critical for reliability.
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: Implement retrieval-augmented generation (RAG) to ground the model in relevant documents. — Retrieval-Augmented Generation (RAG) reduces hallucinations by grounding the model's output in external, verifiable documents retrieved from a knowledge base. Instead of relying solely on the model's parametric memory, RAG fetches relevant context (e.g., the original ticket) at inference time, ensuring the summary is factually aligned with the source. This maintains summary quality because the model can still generate fluent text while being constrained to the retrieved evidence.
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.
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?
Read the scenario before looking for a memorised answer.
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Question Discussion
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