The correct approach is few-shot prompting with the examples in the prompt. This technique works by including a small number of high-quality example input-output pairs directly in the prompt, which allows the Azure OpenAI model to infer the desired style, tone, and structure for new product descriptions without any retraining or fine-tuning. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of how to apply few-shot prompting for style transfer tasks, distinguishing it from zero-shot prompting (no examples) or fine-tuning (which requires retraining the model). A common trap is confusing few-shot prompting with fine-tuning—remember that few-shot prompting uses examples in the prompt itself, while fine-tuning modifies the model’s weights. For the exam, think of the memory tip: “Few in the prompt, fine in the training.”
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 company wants to use Azure OpenAI to generate product descriptions. They have a few example descriptions that perfectly match their desired style and structure. They want the model to produce new descriptions in the same style without retraining the underlying model. Which approach should they use?
Why wrong: Fine-tuning requires retraining the model with a labeled dataset, which is unnecessarily complex for this simple use case and not the most direct approach.
B
Few-shot prompting with the examples in the prompt
Correct. Few-shot prompting uses the provided examples in the prompt to condition the model on the desired style without any retraining.
C
Embeddings and similarity search
Why wrong: Embeddings are used for finding similar content or for classification, not for generating text in a specific style.
D
Content filtering configurations
Why wrong: Content filtering screens for harmful content but does not influence the style or structure of the generated text.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Few-shot prompting with the examples in the prompt
Few-shot prompting provides the model with a small number of example inputs and outputs directly in the prompt, allowing it to infer the desired style and structure without any training. This approach is ideal when you have a few high-quality examples and want to generate new content that matches them, without the cost and complexity of fine-tuning.
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-tune the model on the example descriptions
Why it's wrong here
Fine-tuning requires retraining the model with a labeled dataset, which is unnecessarily complex for this simple use case and not the most direct approach.
✓
Few-shot prompting with the examples in the prompt
Why this is correct
Correct. Few-shot prompting uses the provided examples in the prompt to condition the model on the desired style without any retraining.
Related concept
Read the scenario before looking for a memorised answer.
✗
Embeddings and similarity search
Why it's wrong here
Embeddings are used for finding similar content or for classification, not for generating text in a specific style.
✗
Content filtering configurations
Why it's wrong here
Content filtering screens for harmful content but does not influence the style or structure of the generated text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse fine-tuning with few-shot prompting, assuming that any use of examples requires retraining the model, when in fact the examples can simply be placed in the prompt to achieve the same effect without modifying the model.
Trap categories for this question
Similar concept trap
Embeddings are used for finding similar content or for classification, not for generating text in a specific style.
Detailed technical explanation
How to think about this question
Few-shot prompting leverages the model's in-context learning ability, where the prompt includes a few complete input-output pairs (e.g., product name and its description) followed by a new input. The model uses the patterns in these examples to generate a response that mimics the format, tone, and level of detail, without any gradient updates or weight changes. This technique is especially effective with large language models like GPT-4, which have been trained on diverse data and can generalize from a handful of examples.
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.
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: Few-shot prompting with the examples in the prompt — Few-shot prompting provides the model with a small number of example inputs and outputs directly in the prompt, allowing it to infer the desired style and structure without any training. This approach is ideal when you have a few high-quality examples and want to generate new content that matches them, without the cost and complexity of fine-tuning.
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 personalized marketing emails. They have a large dataset of customer purchase histories. They want the model to generate emails that recommend products based on individual customer preferences without retraining the entire model. Which technique should they use?
medium
A.Fine-tuning
✓ B.Prompt engineering with few-shot learning
C.Reinforcement learning from human feedback
D.Creating a custom neural network
Why B: Prompt engineering with few-shot learning is correct because it allows the model to generate personalized marketing emails by providing a few examples of customer-product pairs in the prompt, without modifying the underlying model weights. This technique leverages the pre-trained knowledge of Azure OpenAI to recommend products based on individual customer purchase histories, avoiding the need for costly retraining.
Last reviewed: Jun 11, 2026
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