AI-102 Implement generative AI solutions Practice Question
You are building an Azure OpenAI solution that must generate product descriptions from a small set of 40 curated examples that demonstrate your brand voice. You want the model to imitate the style without changing the model weights or incurring the cost of a fine-tuning job. What should you do?
⚠ Common exam trap
The trap here is assuming that any style customization requires fine-tuning, when a small example set is better handled with in-context few-shot prompting.
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
✓
Include several of the curated examples directly in the system and user messages of each chat completion request.
Few-shot prompting embeds a handful of curated examples in the prompt so the model mimics their tone, structure, and vocabulary on each call. Because the requirement is a small example set and no weight modification or fine-tuning cost, in-context examples are the correct mechanism. Sampling parameters and retrieval indexes do not substitute for style conditioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the temperature parameter to 2 and the top_p parameter to 0.1 on every request.
Why it's wrong here
Temperature and top_p control randomness and token sampling, not style acquisition. Raising temperature to 2 increases unpredictability and can produce incoherent text, while a very low top_p narrows choices. Neither parameter lets the model learn or imitate the curated brand voice from examples.
- ✗
Upload the examples to an Azure AI Search index and enable semantic ranker on the index.
Why it's wrong here
Azure AI Search with semantic ranker is a retrieval mechanism for grounding answers in documents; it does not teach the model a writing style. Retrieving similar examples could superficially help, but semantic ranker optimizes relevance ranking, not stylistic imitation. It also adds infrastructure without addressing the core few-shot need.
- ✓
Include several of the curated examples directly in the system and user messages of each chat completion request.
Why this is correct
Placing curated examples in the system and user messages is few-shot prompting, which conditions the model on your brand voice at inference time without modifying weights. It is the appropriate approach when you have a small set of examples and want to avoid the cost and latency of a fine-tuning job. The examples guide tone and structure for each response.
- ✗
Create a fine-tuning job in Azure OpenAI Studio using the 40 examples and deploy the resulting custom model.
Why it's wrong here
Fine-tuning can teach style, but it requires a larger, well-formatted JSONL dataset and incurs training and hosting costs. The scenario explicitly asks to avoid fine-tuning cost and weight changes, so this approach contradicts the stated constraint. Forty examples are also generally too few for a reliable fine-tune.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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