1Z0-1127-25 Using OCI Generative AI Service Practice Question
A company wants to use OCI Generative AI service to automatically generate product descriptions for an e-commerce catalog. They have 10,000 products. What is the best approach to ensure high-quality, consistent descriptions?
⚠ Common exam trap
The exam often tests the misconception that few-shot prompting (Option C) is sufficient for large-scale, consistent generation, when in reality it suffers from context window limits and lack of domain-specific adaptation, making fine-tuning the only viable option for production workloads with thousands of items.
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 base model on a dataset of existing product descriptions.
Fine-tuning a base model on a dataset of existing product descriptions is the best approach because it adapts the model to the specific domain, style, and vocabulary of the e-commerce catalog. This ensures high-quality, consistent outputs across 10,000 products by learning the patterns and terminology from the company's own data, rather than relying on generic or template-based methods.
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 a pre-trained summarization model.
Why it's wrong here
Summarization models are not designed for generating descriptions from scratch.
- ✗
Use a template-based generation with keyword insertion.
Why it's wrong here
Template-based generation is rigid and may produce unnatural text.
- ✗
Use the built-in chat model with few-shot examples in the prompt.
Why it's wrong here
Few-shot prompting may work for a few examples but is not scalable for 10,000 products and may yield inconsistent results.
- ✓
Fine-tune a base model on a dataset of existing product descriptions.
Why this is correct
Fine-tuning adapts the model to the specific domain and produces consistent outputs across many products.
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