- A
Training a model with very few labelled examples using transfer learning
Why wrong: Few-shot learning with transfer learning involves actual training — few-shot prompting is an inference technique using examples in the prompt.
- B
Including a small number of input-output examples in the prompt to demonstrate the desired task format
Few-shot prompting provides task demonstrations in the prompt — no training required, just examples that show the model what's expected.
- C
Generating a short (few-shot) response rather than a detailed answer
Why wrong: 'Few-shot' in prompting refers to the number of examples provided — not the length of the generated response.
- D
Running the model for only a few seconds to save compute costs
Why wrong: Inference time is a performance concern — few-shot prompting is a technique for improving response quality through in-prompt examples.
Quick Answer
The answer is that few-shot prompting is the technique of including a small number of input-output examples directly in the prompt to demonstrate the desired task format. This improves model outputs by leveraging the model’s in-context learning ability, allowing it to infer the required reasoning pattern, style, or structure from the provided examples without any fine-tuning or retraining. On the Microsoft Azure AI-900 exam, this concept tests your understanding of how to guide generative AI models efficiently, often appearing in scenarios where you must choose between zero-shot, one-shot, and few-shot approaches. A common trap is confusing few-shot with fine-tuning—remember that few-shot changes only the prompt, not the model’s weights. For a quick memory tip, think “few examples, no retraining” to distinguish it from other techniques.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
What is 'few-shot prompting' and how does it improve model outputs?
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
Including a small number of input-output examples in the prompt to demonstrate the desired task format
Few-shot prompting improves model outputs by providing a small number of input-output examples directly in the prompt, which helps the model understand the desired task format, style, or reasoning pattern without requiring any fine-tuning or retraining. This technique leverages the model's in-context learning ability to generalize from the given examples and produce more accurate, consistent responses.
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.
- ✗
Training a model with very few labelled examples using transfer learning
Why it's wrong here
Few-shot learning with transfer learning involves actual training — few-shot prompting is an inference technique using examples in the prompt.
- ✓
Including a small number of input-output examples in the prompt to demonstrate the desired task format
Why this is correct
Few-shot prompting provides task demonstrations in the prompt — no training required, just examples that show the model what's expected.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Generating a short (few-shot) response rather than a detailed answer
Why it's wrong here
'Few-shot' in prompting refers to the number of examples provided — not the length of the generated response.
- ✗
Running the model for only a few seconds to save compute costs
Why it's wrong here
Inference time is a performance concern — few-shot prompting is a technique for improving response quality through in-prompt examples.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'few-shot' with 'fewer training data' or 'shorter responses,' when the term specifically refers to the number of examples included in the prompt to guide the model's output.
Detailed technical explanation
How to think about this question
Under the hood, few-shot prompting works by providing the model with a sequence of input-output pairs that condition its next-token predictions via the attention mechanism, effectively priming the model's internal representations for the specific task. A subtle behavior is that the order and quality of examples matter significantly—poorly chosen or misordered examples can degrade performance, and the model may overfit to superficial patterns in the examples rather than the intended task. In real-world scenarios, few-shot prompting is commonly used in Azure OpenAI Service to quickly adapt GPT models for custom classification, extraction, or summarization tasks without 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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Including a small number of input-output examples in the prompt to demonstrate the desired task format — Few-shot prompting improves model outputs by providing a small number of input-output examples directly in the prompt, which helps the model understand the desired task format, style, or reasoning pattern without requiring any fine-tuning or retraining. This technique leverages the model's in-context learning ability to generalize from the given examples and produce more accurate, consistent responses.
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
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