1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A company is deploying a large language model for a customer service chatbot. The model needs to understand industry-specific jargon and maintain low latency. Which approach best balances these requirements?
⚠ Common exam trap
Oracle often tests the misconception that larger models always perform better or that RAG alone solves domain adaptation, ignoring the latency and efficiency trade-offs that make fine-tuning a smaller model the optimal choice for production systems with strict response time requirements.
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
✓
Fine-tune a small open-source LLM on domain-specific data
Fine-tuning a small open-source LLM on domain-specific data is the best approach because it adapts the model to understand industry-specific jargon while keeping the model small enough to maintain low latency. Unlike larger models, a fine-tuned small model can run efficiently on local hardware, reducing inference time and avoiding the overhead of external API calls or large model sizes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Employ retrieval-augmented generation (RAG) with a general model
Why it's wrong here
RAG helps with facts but does not deeply embed jargon into model behavior.
- ✗
Rely solely on prompt engineering with a general model
Why it's wrong here
Prompt engineering may not suffice for consistent understanding of specialized terms.
- ✗
Use a large general-purpose LLM with zero-shot prompting
Why it's wrong here
Large models have higher latency and may still miss niche jargon.
- ✓
Fine-tune a small open-source LLM on domain-specific data
Why this is correct
Fine-tuning adapts the model to jargon and a smaller model keeps latency low.
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