- A
Fine-tuning
Why wrong: Fine-tuning retrains the model on new data, updating its weights. The scenario describes only adding examples to the prompt, not retraining.
- B
Few-shot learning
Few-shot learning uses a handful of examples in the prompt to condition the model's responses, which matches the described approach.
- C
Reinforcement learning
Why wrong: Reinforcement learning uses rewards and punishments to train a model over many interactions. This is not about providing examples in a single prompt.
- D
Transfer learning
Why wrong: Transfer learning involves taking a pre-trained model and adapting it to a new task, often via fine-tuning. Few-shot learning is a form of transfer learning, but the specific technique described is few-shot, not transfer in general.
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 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?
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
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.
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
Why it's wrong here
Fine-tuning retrains the model on new data, updating its weights. The scenario describes only adding examples to the prompt, not retraining.
- ✓
Few-shot learning
Why this is correct
Few-shot learning uses a handful of examples in the prompt to condition the model's responses, which matches the described approach.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning uses rewards and punishments to train a model over many interactions. This is not about providing examples in a single prompt.
- ✗
Transfer learning
Why it's wrong here
Transfer learning involves taking a pre-trained model and adapting it to a new task, often via fine-tuning. Few-shot learning is a form of transfer learning, but the specific technique described is few-shot, not transfer in general.
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 any use of examples requires retraining, but Azure OpenAI's prompt-based examples are a distinct inference-time technique that does not modify the model.
Trap categories for this question
Scenario analysis trap
Fine-tuning retrains the model on new data, updating its weights. The scenario describes only adding examples to the prompt, not retraining.
Detailed technical explanation
How to think about this question
Few-shot learning in Azure OpenAI works by including a few input-output examples in the system or user message, which the model uses as context to infer the desired pattern for new queries. This technique relies on the model's in-context learning ability, where the attention mechanism processes the examples to adjust its output without any gradient updates. A real-world scenario is a customer service bot that uses 3-5 example Q&A pairs to handle policy questions, avoiding the cost and complexity of fine-tuning.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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: Few-shot learning — 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.
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.
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Last reviewed: Jun 11, 2026
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.
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