- A
Storing model responses in a cache to retrieve them faster for repeated questions
Why wrong: Response caching is a performance optimisation — RAG retrieves source documents to ground LLM answers in real information.
- B
Retrieving relevant documents from a knowledge base to provide accurate context for LLM responses
RAG grounds LLM answers in retrieved documents — solving hallucination, knowledge cutoff, and private data limitations.
- C
Generating random responses and selecting the most relevant using a ranker model
Why wrong: Candidate generation and ranking is a different approach — RAG retrieves documents first, then generates one grounded response.
- D
A technique for making LLM responses shorter by removing irrelevant sections
Why wrong: Response truncation is output post-processing — RAG adds retrieved context to make responses more accurate, not shorter.
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 what problem does it solve?
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
Retrieving relevant documents from a knowledge base to provide accurate context for LLM responses
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 This solves the problem of LLMs producing outdated, hallucinated, or factually incorrect answers by ensuring the model has access to current, authoritative information.
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.
- ✗
Storing model responses in a cache to retrieve them faster for repeated questions
Why it's wrong here
Response caching is a performance optimisation — RAG retrieves source documents to ground LLM answers in real information.
- ✓
Retrieving relevant documents from a knowledge base to provide accurate context for LLM responses
Why this is correct
RAG grounds LLM answers in retrieved documents — solving hallucination, knowledge cutoff, and private data limitations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Generating random responses and selecting the most relevant using a ranker model
Why it's wrong here
Candidate generation and ranking is a different approach — RAG retrieves documents first, then generates one grounded response.
- ✗
A technique for making LLM responses shorter by removing irrelevant sections
Why it's wrong here
Response truncation is output post-processing — RAG adds retrieved context to make responses more accurate, not shorter.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse RAG with simple caching or response shortening, overlooking that the core innovation is grounding generation in externally retrieved, up-to-date knowledge rather than relying solely on the model's parametric memory.
Trap categories for this question
Command / output trap
Response truncation is output post-processing — RAG adds retrieved context to make responses more accurate, not shorter.
Detailed technical explanation
How to think about this question
Under the hood, RAG typically uses a vector database (e.g., Azure Cosmos DB with vector search) to store embeddings of documents. When a query arrives, it is embedded and compared via cosine similarity to retrieve the top-k most relevant chunks. These chunks are then inserted into the LLM's prompt as context, often with a system message instructing the model to answer only from the provided material. A real-world scenario is a customer support chatbot that retrieves the latest product manual pages before generating a troubleshooting answer, ensuring the response reflects the most recent firmware updates.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: Retrieving relevant documents from a knowledge base to provide accurate context for LLM responses — 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.
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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