- A
Use only a system prompt instructing the model to be accurate and safe.
Why wrong: System prompts alone are often insufficient to guarantee safety and accuracy.
- B
Use retrieval-augmented generation (RAG) for factual accuracy and a content safety filter for safe outputs.
RAG improves accuracy; safety filter ensures safety.
- C
Use a high temperature for creativity and a safety classifier for blocking toxic outputs.
Why wrong: High temperature can reduce accuracy.
- D
Fine-tune the model on all historical chat logs and use a high temperature.
Why wrong: Historical chat logs may contain biases and safety issues.
Question 436 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 company is deploying a large language model in a customer-facing chatbot. The model's responses must be both accurate and safe. Which combination of techniques should be employed?
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 retrieval-augmented generation (RAG) for factual accuracy and a content safety filter for safe outputs.
Option B is correct because RAG grounds the model's responses in a verified external knowledge base, reducing hallucinations and improving factual accuracy, while a content safety filter (e.g., a classifier or guardrail) actively blocks toxic or unsafe outputs before they reach the user. This combination addresses both accuracy and safety independently, unlike a single system prompt which is easily bypassed.
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.
- ✗
Use only a system prompt instructing the model to be accurate and safe.
Why it's wrong here
System prompts alone are often insufficient to guarantee safety and accuracy.
- ✓
Use retrieval-augmented generation (RAG) for factual accuracy and a content safety filter for safe outputs.
Why this is correct
RAG improves accuracy; safety filter ensures safety.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a high temperature for creativity and a safety classifier for blocking toxic outputs.
Why it's wrong here
High temperature can reduce accuracy.
- ✗
Fine-tune the model on all historical chat logs and use a high temperature.
Why it's wrong here
Historical chat logs may contain biases and safety issues.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that a single technique (like a system prompt or fine-tuning) can simultaneously guarantee both accuracy and safety, when in practice they require separate, complementary mechanisms.
Detailed technical explanation
How to think about this question
RAG works by embedding user queries and retrieving relevant document chunks from a vector database (e.g., using cosine similarity on embeddings from models like text-embedding-ada-002), then injecting those chunks into the prompt context to constrain the LLM's generation. Content safety filters often use a separate classifier (e.g., a BERT-based toxicity model or Azure AI Content Safety) that scores outputs on dimensions like hate, self-harm, or sexual content, and blocks or redacts responses exceeding a threshold. In production, these two techniques are often chained: RAG ensures the model has correct facts, and the filter acts as a final gate to catch any residual unsafe generation.
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
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.
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use retrieval-augmented generation (RAG) for factual accuracy and a content safety filter for safe outputs. — Option B is correct because RAG grounds the model's responses in a verified external knowledge base, reducing hallucinations and improving factual accuracy, while a content safety filter (e.g., a classifier or guardrail) actively blocks toxic or unsafe outputs before they reach the user. This combination addresses both accuracy and safety independently, unlike a single system prompt which is easily bypassed.
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: 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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