- A
The model citing academic papers when asked about scientific topics
Why wrong: Academic citation is one use case — citation broadly refers to the model indicating which source documents support its specific claims.
- B
Indicating which source documents support an answer — enabling verification and reducing hallucination risk
Citation grounds responses in sources — users can fact-check against cited documents, building trust in high-stakes applications.
- C
Quoting user messages back to them to confirm the AI understood the question
Why wrong: Question confirmation is a UX technique — citation is about attributing AI claims to source documents.
- D
Copyright attribution when the model quotes text from its training data
Why wrong: Copyright attribution is an IP concern — citation in RAG refers to grounding answers in retrieved source documents.
Quick Answer
The correct answer is that citation in generative AI means indicating which source documents support an answer, enabling verification and reducing hallucination risk. This is technically crucial because citations ground the model’s output in verifiable data, allowing users to trace each claim back to its original source rather than relying on the model’s internal, potentially fabricated knowledge. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of Azure OpenAI Service’s “grounding with your data” feature, where citations appear alongside responses to build trust and transparency. A common trap is confusing citation with simple referencing or metadata—remember, citation here is an active, verifiable link to specific source documents that directly reduces hallucinations. For a memory tip, think “Cite to Verify”: citation provides the source, verification builds trust, and together they cut down hallucination risk.
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 'citation' in generative AI and why is it important for trust?
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
Indicating which source documents support an answer — enabling verification and reducing hallucination risk
Option B is correct because citation in generative AI refers to explicitly linking generated content back to specific source documents, which allows users to verify the information and reduces the risk of hallucination by grounding the model's output in verifiable data. This is a key feature in Azure OpenAI Service's 'grounding with your data' capability, where citations are provided alongside responses to build trust and transparency.
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.
- ✗
The model citing academic papers when asked about scientific topics
Why it's wrong here
Academic citation is one use case — citation broadly refers to the model indicating which source documents support its specific claims.
- ✓
Indicating which source documents support an answer — enabling verification and reducing hallucination risk
Why this is correct
Citation grounds responses in sources — users can fact-check against cited documents, building trust in high-stakes applications.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Quoting user messages back to them to confirm the AI understood the question
Why it's wrong here
Question confirmation is a UX technique — citation is about attributing AI claims to source documents.
- ✗
Copyright attribution when the model quotes text from its training data
Why it's wrong here
Copyright attribution is an IP concern — citation in RAG refers to grounding answers in retrieved source documents.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse citation with generic referencing or legal attribution, but the AI-900 exam specifically tests citation as a mechanism for grounding and verifiability in enterprise generative AI workloads.
Detailed technical explanation
How to think about this question
Under the hood, citation works by having the model retrieve relevant chunks from a vector index (e.g., Azure Cognitive Search) using embedding-based similarity search, then the model generates an answer while the system appends source references (e.g., document name, page number) to the output. In a real-world scenario, a customer support chatbot citing a specific product manual page allows the user to click through and confirm the answer, directly mitigating the 'black box' trust issue common in LLMs.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe features of generative AI workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of generative AI workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Indicating which source documents support an answer — enabling verification and reducing hallucination risk — Option B is correct because citation in generative AI refers to explicitly linking generated content back to specific source documents, which allows users to verify the information and reduces the risk of hallucination by grounding the model's output in verifiable data. This is a key feature in Azure OpenAI Service's 'grounding with your data' capability, where citations are provided alongside responses to build trust and transparency.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.