- A
Fine-tuning
Why wrong: Fine-tuning retrains the model on a specific dataset, which is more resource-intensive and not necessary if the goal is to use existing customer data as context in the prompt.
- B
Prompt engineering with few-shot learning
This technique provides examples in the prompt to guide the model's output for a specific task without retraining, making it ideal for generating personalized emails based on customer data.
- C
Reinforcement learning from human feedback
Why wrong: This is used to align the model with human preferences, not to incorporate specific business data into individual prompts.
- D
Creating a custom neural network
Why wrong: Building a custom model from scratch is not leveraging Azure OpenAI's pre-built LLM and would require extensive training data and compute resources.
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 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?
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
Prompt engineering with few-shot learning
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.
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 a specific dataset, which is more resource-intensive and not necessary if the goal is to use existing customer data as context in the prompt.
- ✓
Prompt engineering with few-shot learning
Why this is correct
This technique provides examples in the prompt to guide the model's output for a specific task without retraining, making it ideal for generating personalized emails based on customer data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reinforcement learning from human feedback
Why it's wrong here
This is used to align the model with human preferences, not to incorporate specific business data into individual prompts.
- ✗
Creating a custom neural network
Why it's wrong here
Building a custom model from scratch is not leveraging Azure OpenAI's pre-built LLM and would require extensive training data and compute resources.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse fine-tuning with prompt engineering, assuming that any customization requires retraining, when in fact few-shot learning can achieve personalization without modifying model weights.
Detailed technical explanation
How to think about this question
Few-shot learning in prompt engineering works by including a small number of input-output examples (e.g., 'Customer bought X, recommend Y') in the prompt context window, which the model uses as a pattern to generate relevant outputs. Azure OpenAI models like GPT-4 have a context window of up to 128K tokens, allowing inclusion of multiple customer histories and examples. This approach is stateless per request, meaning each prompt must contain all relevant context, which can increase token usage and cost but avoids model retraining.
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: Prompt engineering with few-shot learning — 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.
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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