- A
Fine-tuning the model on their product catalog.
Why wrong: Fine-tuning updates the model's weights using the catalog, but it requires retraining for every catalog change and is less flexible for dynamic data.
- B
Using few-shot learning with examples.
Why wrong: Few-shot learning provides a small number of examples in the prompt, but it cannot handle a large product catalog effectively and may not generalize.
- C
Implementing Retrieval Augmented Generation (RAG) with their catalog.
RAG retrieves relevant documents from the catalog and uses them as context for generation, keeping outputs up-to-date without retraining.
- D
Increasing the temperature parameter.
Why wrong: Temperature controls the randomness of the output, not the factual accuracy or grounding in specific data.
Quick Answer
The correct answer is implementing Retrieval Augmented Generation (RAG) with their catalog. RAG grounds the Azure OpenAI model’s output in your specific data by dynamically retrieving relevant product details and pricing from an external knowledge base at inference time, rather than relying on the model’s generic training. This ensures the generated descriptions are accurate and current without the cost or complexity of fine-tuning. On the AI-900 exam, this scenario tests your understanding of how to customize generative AI responses using Azure OpenAI’s built-in data sources feature, often contrasting RAG with fine-tuning or prompt engineering. A common trap is assuming fine-tuning is needed for factual accuracy, but RAG is the correct choice when you need real-time access to proprietary data without retraining. Memory tip: think “RAG retrieves, fine-tuning retrains”—if the data changes often, RAG is your friend.
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 Service to generate product descriptions. They need to ensure the model's output is based on their specific product catalog and pricing, not on generic information. Which approach 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
Implementing Retrieval Augmented Generation (RAG) with their catalog.
Option C is correct because Retrieval Augmented Generation (RAG) allows the model to dynamically retrieve relevant product catalog and pricing information from an external knowledge base at inference time, ensuring the generated descriptions are grounded in the company's specific data rather than relying on the model's generic training data. This approach avoids the need for costly fine-tuning and keeps the output up-to-date without 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 the model on their product catalog.
Why it's wrong here
Fine-tuning updates the model's weights using the catalog, but it requires retraining for every catalog change and is less flexible for dynamic data.
- ✗
Using few-shot learning with examples.
Why it's wrong here
Few-shot learning provides a small number of examples in the prompt, but it cannot handle a large product catalog effectively and may not generalize.
- ✓
Implementing Retrieval Augmented Generation (RAG) with their catalog.
Why this is correct
RAG retrieves relevant documents from the catalog and uses them as context for generation, keeping outputs up-to-date without retraining.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increasing the temperature parameter.
Why it's wrong here
Temperature controls the randomness of the output, not the factual accuracy or grounding in specific data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse fine-tuning (A) as the only way to inject custom data, overlooking that RAG is more practical for dynamic, large-scale, or frequently updated knowledge bases without retraining.
Trap categories for this question
Command / output trap
Temperature controls the randomness of the output, not the factual accuracy or grounding in specific data.
Detailed technical explanation
How to think about this question
RAG works by embedding the product catalog into a vector database (e.g., using Azure Cognitive Search), then at query time retrieving the top-k relevant chunks via cosine similarity search and injecting them into the prompt context window. This ensures the model's output is grounded in retrieved facts, reducing hallucinations and enabling real-time updates to the catalog without retraining. A subtle behavior is that the retrieval quality directly impacts output accuracy, so chunking strategy and embedding model choice (e.g., text-embedding-ada-002) are critical.
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: Implementing Retrieval Augmented Generation (RAG) with their catalog. — Option C is correct because Retrieval Augmented Generation (RAG) allows the model to dynamically retrieve relevant product catalog and pricing information from an external knowledge base at inference time, ensuring the generated descriptions are grounded in the company's specific data rather than relying on the model's generic training data. This approach avoids the need for costly fine-tuning and keeps the output up-to-date without 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.
About these practice questions
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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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