The answer is Retrieval Augmented Generation (RAG). RAG is the correct technique because it connects Azure OpenAI to an external knowledge base—such as the team’s latest catalog and current pricing—allowing the model to retrieve and incorporate fresh, domain-specific data at inference time rather than relying on its static training data. This dynamic injection of up-to-date information ensures the generated product descriptions are accurate and relevant without requiring model retraining. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of how RAG overcomes the limitation of a model’s knowledge cutoff, often appearing in questions about grounding outputs with real-time or proprietary data. A common trap is choosing fine-tuning, but remember: fine-tuning updates the model’s weights, while RAG retrieves external data on the fly. Memory tip: RAG stands for “Retrieve And Generate”—think of it as the model “looking up” the latest catalog before writing.
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 marketing team uses Azure OpenAI to generate product descriptions. They want the output to reflect their latest catalog and current pricing, not the model's general knowledge. Which technique should they use?
Why wrong: Few-shot learning uses examples in the prompt to guide the model, but does not ensure the output is based on a specific live data source.
B
Fine-tuning
Why wrong: Fine-tuning retrains the model on a specific dataset, which is costly and not ideal for data that changes frequently like catalogs and pricing.
C
Retrieval Augmented Generation (RAG)
RAG retrieves relevant data from an external knowledge base (e.g., product catalog) and uses it as context, grounding the model's output in the latest information.
D
Prompt engineering
Why wrong: Prompt engineering involves crafting prompts but does not by itself inject real-time or specific external data into the model's response.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) is the correct technique because it allows the model to retrieve up-to-date information from an external knowledge base—such as the latest catalog and current pricing—and incorporate that data into the generated output. Unlike the model's static training data, RAG dynamically injects fresh, domain-specific content at inference time, ensuring accuracy and relevance without modifying the model itself.
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.
✗
Few-shot learning
Why it's wrong here
Few-shot learning uses examples in the prompt to guide the model, but does not ensure the output is based on a specific live data source.
✗
Fine-tuning
Why it's wrong here
Fine-tuning retrains the model on a specific dataset, which is costly and not ideal for data that changes frequently like catalogs and pricing.
✓
Retrieval Augmented Generation (RAG)
Why this is correct
RAG retrieves relevant data from an external knowledge base (e.g., product catalog) and uses it as context, grounding the model's output in the latest information.
Related concept
Read the scenario before looking for a memorised answer.
✗
Prompt engineering
Why it's wrong here
Prompt engineering involves crafting prompts but does not by itself inject real-time or specific external data into the model's response.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the misconception that fine-tuning is the only way to inject new knowledge, but the trap here is that fine-tuning creates a static model, whereas RAG provides dynamic, up-to-date information without retraining.
Trap categories for this question
Command / output trap
Few-shot learning uses examples in the prompt to guide the model, but does not ensure the output is based on a specific live data source.
Detailed technical explanation
How to think about this question
RAG works by embedding a user query, retrieving relevant documents from a vector database (e.g., Azure Cognitive Search) using cosine similarity, and then concatenating those documents into the prompt as context for the generative model. This approach leverages a retrieval step that can index thousands of documents and update them independently, enabling near-real-time accuracy without retraining. A subtle behavior is that the model may still hallucinate if the retrieved context is incomplete or irrelevant, so careful chunking and ranking of documents is 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.
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: Retrieval Augmented Generation (RAG) — Retrieval Augmented Generation (RAG) is the correct technique because it allows the model to retrieve up-to-date information from an external knowledge base—such as the latest catalog and current pricing—and incorporate that data into the generated output. Unlike the model's static training data, RAG dynamically injects fresh, domain-specific content at inference time, ensuring accuracy and relevance without modifying the model itself.
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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