1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A healthcare company is using OCI GenAI to generate patient summaries from clinical notes. The model output sometimes includes hallucinated medical facts, such as incorrect dosages or diagnoses, which could be dangerous. The team needs to improve factual accuracy while maintaining data privacy. They have a large collection of internal medical knowledge bases (clinical guidelines, drug databases) that are stored in OCI Object Storage. The current implementation uses a zero-shot prompt with the base Cohere Command model. The data science team has limited GPU resources and wants to avoid building a complex pipeline. Which course of action best addresses the hallucination problem?
⚠ Common exam trap
Oracle often tests the misconception that prompt engineering alone can reliably eliminate hallucinations, but the trap here is that without external knowledge injection (RAG), the model cannot overcome its inherent tendency to fabricate facts, especially in high-stakes domains like healthcare.
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 RAG pipeline that retrieves relevant documents from the internal knowledge bases and includes them in the prompt.
A Retrieval-Augmented Generation (RAG) pipeline directly addresses hallucination by grounding the model's output in verified, internal medical knowledge bases stored in OCI Object Storage. This approach retrieves relevant clinical guidelines or drug database entries and includes them in the prompt, providing factual context without requiring fine-tuning or complex GPU-intensive pipelines. It also preserves data privacy by keeping sensitive medical data within OCI and avoids exposing it to external model training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the temperature parameter to 0.9 to encourage more deterministic outputs.
Why it's wrong here
Higher temperature increases randomness, worsening hallucination.
- ✗
Use prompt engineering to add 'Only provide facts that are absolutely certain.'
Why it's wrong here
Prompt instructions are often ignored by LLMs and do not guarantee factual accuracy.
- ✓
Implement a RAG pipeline that retrieves relevant documents from the internal knowledge bases and includes them in the prompt.
Why this is correct
RAG grounds generation in retrieved facts, significantly reducing hallucinations.
- ✗
Fine-tune the Cohere model on a publicly available medical dataset like PubMed.
Why it's wrong here
Public datasets may not align with internal data, and fine-tuning does not prevent hallucinations of specific facts.
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