The correct technique is few-shot learning, which involves providing the example outline directly in the prompt to guide the model’s output without retraining. This works because Azure OpenAI’s in-context learning ability allows it to recognize and replicate the given pattern—introduction, three key points, conclusion—from a single example, making it ideal for generating new outlines that follow the same structure. On the AI-900 exam, this scenario tests your understanding of prompt engineering techniques, specifically how few-shot learning differs from zero-shot (no examples) and fine-tuning (retraining the model). A common trap is confusing few-shot learning with fine-tuning, but remember: few-shot uses examples in the prompt, while fine-tuning updates the model’s weights. For a memory tip, think “few in the prompt, fine in the training”—if you can count the examples on one hand and haven’t retrained, it’s few-shot learning.
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 marketing team wants to use Azure OpenAI to generate blog post outlines. They have a single example of an outline that follows their preferred structure: introduction, three key points, conclusion. They want the model to generate new outlines that follow the same structure without retraining the model. Which technique should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Providing the example outline in the prompt (few-shot learning)
Option B is correct because few-shot learning involves providing a small number of examples (in this case, one example outline) directly in the prompt to guide the model's output format and structure without any retraining. This technique leverages the model's in-context learning ability to mimic the given pattern, making it ideal for generating new outlines that follow the same structure.
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 on a large dataset of blog outlines
Why it's wrong here
Incorrect: Fine-tuning requires retraining the model, which is not desired here.
✓
Providing the example outline in the prompt (few-shot learning)
Why this is correct
Correct: Few-shot learning uses examples in the prompt to guide the model's output without retraining.
Related concept
Read the scenario before looking for a memorised answer.
✗
Setting the temperature parameter to a high value
Why it's wrong here
Incorrect: Temperature controls randomness, not adherence to a specific structure.
✗
Using the Azure OpenAI embeddings API
Why it's wrong here
Incorrect: Embeddings are for measuring text similarity, not generating text content.
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 any task requiring consistent output format must involve retraining the model, when in fact in-context learning via prompt engineering is sufficient for small numbers of examples.
Trap categories for this question
Similar concept trap
Incorrect: Embeddings are for measuring text similarity, not generating text content.
Detailed technical explanation
How to think about this question
Few-shot learning works by conditioning the model on a prefix of input-output pairs within the prompt, leveraging the transformer's attention mechanism to infer the pattern from the provided examples. The temperature parameter controls the probability distribution over tokens; a high value (e.g., >1.0) flattens the distribution, increasing diversity but reducing adherence to the prompt's structure. In contrast, a low temperature (e.g., 0.1) makes the model more deterministic and likely to follow the given format precisely.
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: Providing the example outline in the prompt (few-shot learning) — Option B is correct because few-shot learning involves providing a small number of examples (in this case, one example outline) directly in the prompt to guide the model's output format and structure without any retraining. This technique leverages the model's in-context learning ability to mimic the given pattern, making it ideal for generating new outlines that follow the same structure.
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
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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 developer wants to use Azure OpenAI to build a customer service chatbot that can answer questions about a company's return policy. They create a set of example question-answer pairs in the prompt without retraining the model. Which technique is being used?
easy
A.Fine-tuning
✓ B.Few-shot learning
C.Reinforcement learning
D.Transfer learning
Why B: Few-shot learning is the correct technique because the developer provides a small set of example question-answer pairs directly in the prompt to guide the model's responses, without retraining or updating the model's weights. This leverages the model's pre-existing knowledge to generalize from the examples, which is a hallmark of few-shot prompting in Azure OpenAI.
Last reviewed: Jun 11, 2026
Question Discussion
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