AI-900 Practice Question: Describe features of generative AI workloads on Azure
A developer uses Azure OpenAI to generate product descriptions. They provide five examples of product descriptions that follow a specific format (name, features, price, call to action). They then ask the model to write a new description for a given product, expecting the same format. Which technique is the developer using?
⚠ Common exam trap
It's easy for candidates to confuse few-shot learning with fine-tuning, assuming that providing examples in the prompt constitutes training the model, when in fact fine-tuning involves a separate training phase that modifies model parameters.
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
✓
Few-shot learning
The developer is using few-shot learning, which involves providing a small number of examples (in this case, five product descriptions) to guide the model's output format and style without updating the model's weights. This technique leverages the model's in-context learning ability to follow the demonstrated pattern for a new input.
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-tuning
Why it's wrong here
Fine-tuning is a supervised training process that adjusts the model’s weights using a large, labeled dataset through an Azure OpenAI training job, not by adding examples to a runtime prompt. The developer here only supplies five exemplars in the prompt, so no gradient updates or training pipeline are involved. In Azure OpenAI, fine-tuning requires preparing a training file and submitting a fine-tune job to create a custom model, which is heavier than prompt-based in-context learning.
- ✗
Zero-shot learning
Why it's wrong here
Zero-shot learning means the model performs a task given only an instruction and no sample input-output pairs, relying entirely on knowledge acquired during pre-training. Since the developer includes five product-description examples in the prompt, the model is not operating in a zero-shot regime; it receives explicit demonstrations of the desired tone, structure, and content. The presence of those exemplars is what moves the task from zero-shot to few-shot in-context learning.
- ✓
Few-shot learning
Why this is correct
Few-shot learning, in the Azure OpenAI context, is an in-context learning technique where a small set of input-output examples is placed directly in the prompt so the model conditions its generation on the demonstrated style, format, and semantics. The developer’s five product-description examples act as exemplars that teach the model what the desired output should look like without any weight updates or training job. This is the correct answer because the model infers the task pattern purely from the prompt content at inference time.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning trains an agent by applying rewards or penalties to actions, with the goal of maximizing cumulative reward; for language models, this is done via RLHF-style training loops that adjust weights based on human or automated feedback. Providing five product descriptions in an API prompt does not generate reward signals or backpropagate gradient updates—it is just inference-time context. Azure OpenAI may expose models trained with RLHF, but the developer’s action is not reinforcement learning itself.
Go deeper
Related to this question
About these practice questions
One of 985 original AI-900 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.