- A
Using Azure Storage to retrieve training data for model fine-tuning
Why wrong: Training data retrieval is part of model fine-tuning — RAG retrieves documents at inference time to provide context for answers.
- B
Combining Azure AI Search (retrieval) with Azure OpenAI (generation) to ground LLM responses in a knowledge base
RAG: AI Search retrieves relevant documents → provided as context to Azure OpenAI → LLM generates answers grounded in retrieved content.
- C
Using Azure CDN to deliver AI-generated content faster globally
Why wrong: CDN delivers web content — RAG is an AI architecture for grounding LLM responses in retrieved knowledge.
- D
A method of compressing large datasets before training language models
Why wrong: Dataset compression is data engineering — RAG is a runtime architecture for accurate, grounded AI responses.
Quick Answer
The answer is that retrieval augmented generation (RAG) combines Azure AI Search for retrieval with Azure OpenAI for generation to ground large language model responses in a specific knowledge base. This pattern is correct because it solves a fundamental limitation of generative models: without RAG, an LLM like GPT-4 can only rely on its training data, which may be outdated or incomplete, leading to hallucinations. By first retrieving relevant documents or chunks from a curated knowledge base via Azure AI Search, then passing that context to the Azure OpenAI model, the generated answer is fact-based and verifiable. On the AI-900 exam, this concept tests your understanding of how to make AI outputs trustworthy and domain-specific, often appearing in questions about reducing hallucinations or using enterprise data. A common trap is confusing RAG with fine-tuning—remember that RAG retrieves external data at inference time, while fine-tuning updates the model itself. A helpful memory tip: think of RAG as a librarian handing the right book to a writer, ensuring the story stays accurate.
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.
What is 'retrieval augmented generation' (RAG) and which Azure services typically implement it?
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
Combining Azure AI Search (retrieval) with Azure OpenAI (generation) to ground LLM responses in a knowledge base
Retrieval Augmented Generation (RAG) is a pattern that combines a retrieval step with a generative step. In Azure, this is typically implemented by using Azure AI Search to retrieve relevant documents or chunks from a knowledge base, then passing those results as context to an Azure OpenAI model (e.g., GPT-4) to generate a grounded, fact-based response. This approach reduces hallucinations and ensures the output is based on authoritative data rather than the model's training data alone.
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.
- ✗
Using Azure Storage to retrieve training data for model fine-tuning
Why it's wrong here
Training data retrieval is part of model fine-tuning — RAG retrieves documents at inference time to provide context for answers.
- ✓
Combining Azure AI Search (retrieval) with Azure OpenAI (generation) to ground LLM responses in a knowledge base
Why this is correct
RAG: AI Search retrieves relevant documents → provided as context to Azure OpenAI → LLM generates answers grounded in retrieved content.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Using Azure CDN to deliver AI-generated content faster globally
Why it's wrong here
CDN delivers web content — RAG is an AI architecture for grounding LLM responses in retrieved knowledge.
- ✗
A method of compressing large datasets before training language models
Why it's wrong here
Dataset compression is data engineering — RAG is a runtime architecture for accurate, grounded AI responses.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse RAG with fine-tuning, mistakenly thinking retrieval modifies the model's training data, whereas RAG is a prompt-time augmentation that leaves the model unchanged.
Detailed technical explanation
How to think about this question
Under the hood, RAG works by embedding the user query into a vector space using an embedding model (e.g., text-embedding-ada-002), then performing a similarity search against a vector index in Azure AI Search. The retrieved top-k chunks are inserted into the system prompt of the generative model, effectively grounding the output in the retrieved content. A real-world scenario is a customer support chatbot that retrieves the latest product documentation from Azure AI Search before generating a response, ensuring answers reflect current policies without retraining the LLM.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Combining Azure AI Search (retrieval) with Azure OpenAI (generation) to ground LLM responses in a knowledge base — Retrieval Augmented Generation (RAG) is a pattern that combines a retrieval step with a generative step. In Azure, this is typically implemented by using Azure AI Search to retrieve relevant documents or chunks from a knowledge base, then passing those results as context to an Azure OpenAI model (e.g., GPT-4) to generate a grounded, fact-based response. This approach reduces hallucinations and ensures the output is based on authoritative data rather than the model's training data alone.
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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Same concept, more angles
2 more ways this is tested on AI-900
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. What is 'retrieval-augmented generation' (RAG) and what problem does it solve?
medium- A.Storing model responses in a cache to retrieve them faster for repeated questions
- ✓ B.Retrieving relevant documents from a knowledge base to provide accurate context for LLM responses
- C.Generating random responses and selecting the most relevant using a ranker model
- D.A technique for making LLM responses shorter by removing irrelevant sections
Why B: Retrieval-augmented generation (RAG) combines a retrieval step with a generative language model. It first retrieves relevant documents or passages from an external knowledge base (e.g., Azure Cognitive Search) and then feeds that context into the LLM to ground its response. This solves the problem of LLMs producing outdated, hallucinated, or factually incorrect answers by ensuring the model has access to current, authoritative information.
Variation 2. What is the primary benefit of using Retrieval Augmented Generation (RAG) over relying solely on an LLM's trained knowledge?
medium- A.RAG makes LLMs faster by skipping the training process
- ✓ B.RAG grounds LLM responses in current, specific information — reducing hallucination and knowledge cutoff issues
- C.RAG reduces the cost of API calls by batching requests
- D.RAG allows LLMs to process images alongside text
Why B: RAG enhances LLM outputs by retrieving relevant, up-to-date information from an external knowledge base (e.g., Azure Cognitive Search) and injecting it into the prompt context. This grounds the model's response in verifiable data, significantly reducing hallucinations and overcoming the knowledge cutoff limitation inherent in static training data.
Last reviewed: Jun 11, 2026
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