The correct answer is prompt engineering with few-shot learning. This technique works by including a small set of input-output examples—in this case, function definitions paired with their descriptions—directly in the prompt to guide the model’s behavior without retraining or modifying its weights. The developer is leveraging the model’s in-context learning ability, which allows Azure OpenAI to generalize from those few examples and generate a new function for a new description. On the AI-900 exam, this scenario tests your understanding of how prompt engineering shapes model responses, often appearing as a scenario-based question where you must distinguish few-shot learning from zero-shot or fine-tuning approaches. A common trap is confusing few-shot learning with fine-tuning, but remember: few-shot learning uses examples in the prompt, while fine-tuning updates the model’s weights. Memory tip: “Few in the prompt, fine in the weights.”
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. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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 Service to generate code. They provide a few examples of function definitions and their corresponding descriptions, then ask the model to write a new function based on a new description. Which technique is the developer using?
Why wrong: Fine-tuning requires retraining the model on additional data, which is not what is happening here; the model is not being retrained.
B
Prompt engineering with few-shot learning
In few-shot learning, you provide a few examples in the prompt to inform the model's output, without modifying the model itself.
C
Training a custom model from scratch
Why wrong: Azure OpenAI provides pre-trained models; training from scratch is not supported in this service.
D
Using reinforcement learning from human feedback
Why wrong: Reinforcement learning from human feedback (RLHF) is used in later stages to align model behavior, not for providing examples in a single prompt.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Prompt engineering with few-shot learning
The developer is using prompt engineering with few-shot learning, a technique where a small set of input-output examples (here, function definitions and descriptions) is included in the prompt to guide the model's behavior without modifying its weights. This leverages the model's in-context learning ability to generalize from the provided examples and generate a new function for a new description.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 the model with the examples
Why it's wrong here
Fine-tuning requires retraining the model on additional data, which is not what is happening here; the model is not being retrained.
✓
Prompt engineering with few-shot learning
Why this is correct
In few-shot learning, you provide a few examples in the prompt to inform the model's output, without modifying the model itself.
Related concept
Read the scenario before looking for a memorised answer.
✗
Training a custom model from scratch
Why it's wrong here
Azure OpenAI provides pre-trained models; training from scratch is not supported in this service.
✗
Using reinforcement learning from human feedback
Why it's wrong here
Reinforcement learning from human feedback (RLHF) is used in later stages to align model behavior, not for providing examples in a single prompt.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse providing examples in the prompt (few-shot learning) with fine-tuning, because both involve using examples, but fine-tuning permanently alters the model's weights while prompt engineering does not.
Detailed technical explanation
How to think about this question
Few-shot learning in Azure OpenAI Service relies on the transformer architecture's attention mechanism, which allows the model to condition its output on the context window containing the examples. The number of examples (typically 2-5) is critical—too few may not establish the pattern, while too many can exceed the token limit (e.g., 4096 tokens for GPT-3.5) and cause truncation. In real-world scenarios, this technique is often used for code generation tasks where fine-tuning is impractical due to cost or data scarcity.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Prompt engineering with few-shot learning — The developer is using prompt engineering with few-shot learning, a technique where a small set of input-output examples (here, function definitions and descriptions) is included in the prompt to guide the model's behavior without modifying its weights. This leverages the model's in-context learning ability to generalize from the provided examples and generate a new function for a new description.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company wants to use Azure OpenAI to generate realistic customer conversations for training a chatbot. They have a set of example conversation snippets and want the model to mimic the style and structure of those examples. The company does not want to retrain the model. Which approach should they use?
medium
A.Fine-tune the model on the conversation dataset
✓ B.Use prompt engineering with few-shot examples in the prompt
C.Use DALL-E to generate the conversations
D.Apply a content filter to restrict the output style
Why B: Option B is correct because prompt engineering with few-shot examples allows the model to mimic the style and structure of provided conversation snippets without retraining. By including a few example conversations in the prompt, the model learns the desired pattern through in-context learning, leveraging its pre-trained capabilities to generate realistic customer conversations.
Last reviewed: Jun 11, 2026
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