1Z0-1127-25 Using OCI Generative AI Service Practice Question
A developer is using OCI Generative AI Service to generate product descriptions. The outputs are often too generic and lack brand-specific tone. The developer has a small set of 20 high-quality example descriptions. What is the most efficient approach to improve output quality?
⚠ Common exam trap
Oracle often tests the misconception that fine-tuning is always the best approach for customization, but candidates overlook the fact that with very small datasets (like 20 examples), few-shot prompting is more practical and efficient than fine-tuning.
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 few-shot prompting by including the 20 examples in the prompt.
Few-shot prompting is the most efficient approach when you have a small set of high-quality examples (20 in this case). It allows the model to infer the desired tone and style directly from the provided examples without requiring any training or fine-tuning, which would be inefficient and potentially ineffective with such a small dataset. In OCI Generative AI Service, few-shot prompting leverages the model's in-context learning capability to adapt its output to the brand-specific tone.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune a base model on the 20 examples.
Why it's wrong here
Fine-tuning requires much larger datasets; 20 examples are insufficient.
- ✓
Use few-shot prompting by including the 20 examples in the prompt.
Why this is correct
Few-shot prompting leverages examples without retraining, ideal for small datasets.
- ✗
Use a more detailed system prompt describing the brand tone.
Why it's wrong here
A system prompt alone may not be sufficient; examples are more effective.
- ✗
Use chain-of-thought prompting to guide the model step by step.
Why it's wrong here
Chain-of-thought is for multi-step reasoning, not for tone or style.
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