The answer is few-shot learning. This technique is correct because the developer provides five examples of product descriptions to guide the model’s output format and style without retraining or updating the model’s weights, relying instead on the model’s in-context learning ability to follow the demonstrated pattern for a new input. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how Azure OpenAI leverages prompt engineering to adapt responses—a common trap is confusing few-shot learning with fine-tuning, which requires updating model weights. Remember, few-shot learning uses a handful of examples in the prompt itself, while zero-shot gives no examples and one-shot gives just one. A simple memory tip: “Few shots in the prompt, no weights to hunt.”
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. A key principle to apply: few-shot learning provides examples directly in the prompt.. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Why wrong: Fine-tuning requires further training the model on a labeled dataset, not simply providing examples in the prompt.
B
Zero-shot learning
Why wrong: Zero-shot learning involves giving the model a task without any examples, relying solely on its pre-trained knowledge.
C
Few-shot learning
Few-shot learning uses a small number of examples in the prompt to demonstrate the desired output format or style, exactly as the developer does with five examples.
D
Reinforcement learning
Why wrong: Reinforcement learning involves training the model using rewards and penalties based on its outputs, not by providing examples in a prompt.
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.
Key principle: Few-shot learning provides examples directly in the prompt.
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 requires further training the model on a labeled dataset, not simply providing examples in the prompt.
✗
Zero-shot learning
Why it's wrong here
Zero-shot learning involves giving the model a task without any examples, relying solely on its pre-trained knowledge.
✓
Few-shot learning
Why this is correct
Few-shot learning uses a small number of examples in the prompt to demonstrate the desired output format or style, exactly as the developer does with five examples.
Related concept
Few-shot learning provides examples directly in the prompt.
✗
Reinforcement learning
Why it's wrong here
Reinforcement learning involves training the model using rewards and penalties based on its outputs, not by providing examples in a prompt.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Trap categories for this question
Command / output trap
Reinforcement learning involves training the model using rewards and penalties based on its outputs, not by providing examples in a prompt.
Detailed technical explanation
How to think about this question
Few-shot learning works by placing examples in the model's context window, allowing the transformer's attention mechanism to infer the desired output structure from the provided patterns. The number of examples (k) can affect performance; too few may not establish the pattern, while too many may exceed the context window limit (e.g., 4096 tokens for GPT-3.5). In real-world scenarios, this approach is used for tasks like text classification or formatting where labeled data is scarce but a quick adaptation is needed.
KKey Concepts to Remember
Few-shot learning provides examples directly in the prompt.
It guides the model's output format or style without retraining.
Ideal for quick adaptation to specific task requirements.
Relies on the model's in-context learning capabilities.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Few-shot learning provides examples directly in the prompt.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Few-shot learning provides examples directly in the prompt. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Few-shot learning provides examples directly in the prompt..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 question wrong?
Review few-shot learning provides examples directly in the prompt., then practise related AI-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Few-shot learning provides examples directly in the prompt.
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Variation 1. A developer wants to use Azure OpenAI to generate text that follows a specific style, such as formal business letters. They provide three examples of the desired output format in the prompt and then ask the model to generate a new letter. Which technique is the developer using?
easy
A.Zero-shot learning
✓ B.Few-shot learning
C.Fine-tuning
D.Temperature scaling
Why B: The developer is using few-shot learning, a technique where a prompt includes several examples (in this case, three formal business letters) to guide the model's output style and format without updating the model's weights. This approach leverages the model's in-context learning ability to generalize from the provided examples, making it ideal for tasks requiring specific stylistic adherence.
Last reviewed: Jun 11, 2026
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