- A
Set the temperature parameter to a high value.
Why wrong: High temperature increases randomness and creativity, which can actually increase the likelihood of the model inventing facts. It does not restrict the model to a provided source.
- B
Use grounding by including the report text in the prompt and explicitly instructing the model to base the summary only on that text.
Grounding confines the model's response to the content of the provided document, directly addressing the goal of factual accuracy and preventing external knowledge from being introduced.
- C
Set the frequency penalty to the maximum allowed value.
Why wrong: A high frequency penalty reduces the repetition of phrases but does not anchor the model to a specific source text; it can still generate ungrounded statements.
- D
Set the max_tokens parameter to a very small number.
Why wrong: Limiting max_tokens only truncates the output length; it does not ensure that the output is factually based on the provided text. The model may still generate unsupported content within the token limit.
Quick Answer
The correct answer is to use grounding by including the report text in the prompt and explicitly instructing the model to base the summary only on that text. This technique directly reduces hallucination by constraining the generative AI to extract facts exclusively from the provided source material, rather than drawing on its training data or inventing speculative details. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of how to prevent hallucination in Azure OpenAI Service, often appearing as a question about controlling model output with prompt engineering. A common trap is assuming fine-tuning or temperature adjustment alone suffices, but grounding with explicit source text and instructions is the most reliable method. Remember the memory tip: “Ground it, don’t guess it”—if you provide the raw data and a strict command to stick to it, the model stays anchored to reality.
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 financial analyst uses Azure OpenAI Service to generate summaries of quarterly earnings reports. The analyst provides the raw text of the report in the prompt and wants the summary to stick strictly to the facts presented in that text, without adding any external information or speculation. Which technique should the analyst employ to minimize the risk of the model inventing information?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Use grounding by including the report text in the prompt and explicitly instructing the model to base the summary only on that text.
Option B is correct because grounding the model with the source text and explicitly instructing it to base the summary solely on that text is the most direct way to reduce hallucination. Azure OpenAI Service relies on the prompt for context; by providing the raw report and a strict instruction, the model is constrained to extract facts from the provided content rather than generating novel 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.
- ✗
Set the temperature parameter to a high value.
Why it's wrong here
High temperature increases randomness and creativity, which can actually increase the likelihood of the model inventing facts. It does not restrict the model to a provided source.
- ✓
Use grounding by including the report text in the prompt and explicitly instructing the model to base the summary only on that text.
Why this is correct
Grounding confines the model's response to the content of the provided document, directly addressing the goal of factual accuracy and preventing external knowledge from being introduced.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set the frequency penalty to the maximum allowed value.
Why it's wrong here
A high frequency penalty reduces the repetition of phrases but does not anchor the model to a specific source text; it can still generate ungrounded statements.
- ✗
Set the max_tokens parameter to a very small number.
Why it's wrong here
Limiting max_tokens only truncates the output length; it does not ensure that the output is factually based on the provided text. The model may still generate unsupported content within the token limit.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse hyperparameter tuning (temperature, frequency penalty, max_tokens) with content control, mistakenly believing these parameters can enforce factual accuracy, when in fact only explicit grounding and instruction can reliably prevent hallucination.
Trap categories for this question
Keyword trap
A high frequency penalty reduces the repetition of phrases but does not anchor the model to a specific source text; it can still generate ungrounded statements.
Command / output trap
Limiting max_tokens only truncates the output length; it does not ensure that the output is factually based on the provided text. The model may still generate unsupported content within the token limit.
Detailed technical explanation
How to think about this question
Grounding in Azure OpenAI works by providing the model with a trusted source (e.g., the earnings report text) and using system or user messages to enforce that the response must be derived exclusively from that source. Under the hood, this leverages the model's attention mechanism to focus on the provided context, reducing the probability of sampling from its parametric knowledge. In real-world scenarios, failing to ground can lead to 'hallucinations' where the model fabricates financial figures or trends, which is critical to avoid in regulated industries like finance.
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: Use grounding by including the report text in the prompt and explicitly instructing the model to base the summary only on that text. — Option B is correct because grounding the model with the source text and explicitly instructing it to base the summary solely on that text is the most direct way to reduce hallucination. Azure OpenAI Service relies on the prompt for context; by providing the raw report and a strict instruction, the model is constrained to extract facts from the provided content rather than generating novel 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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 →
Same concept, more angles
5 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. A legal research firm uses Azure OpenAI Service to answer questions about specific case law documents. They want the model to base its answers exclusively on the content of the provided documents, without using any external knowledge from its training. Which approach should they use?
hard- A.Increase the 'temperature' parameter to 0.0
- B.Use the system message to instruct the model to only use provided documents
- ✓ C.Use the 'Azure OpenAI on your data' feature with a 'Search' data source containing the documents
- D.Set the 'max_tokens' parameter to a low value
Why C: Option C is correct because the 'Azure OpenAI on your data' feature with a 'Search' data source allows the model to retrieve and ground its answers exclusively on the content of the provided documents. This approach uses a search index (e.g., Azure Cognitive Search) to fetch relevant document chunks and inject them into the prompt, ensuring the model does not rely on its pre-trained knowledge. It is the only method that enforces strict document-based grounding without external knowledge leakage.
Variation 2. A company wants to build a chatbot that can answer questions based on its internal policy documents. The documents are stored in Azure Blob Storage. They plan to use Azure OpenAI to generate answers. Which approach should they use to ensure the answers are grounded in the actual policy content?
medium- A.Fine-tune GPT-4 on all policy documents
- ✓ B.Use Azure AI Search to index the documents and provide relevant passages as context to GPT-4
- C.Include the entire policy document text in the prompt each time
- D.Use DALL-E to visualize policy concepts
Why B: Option B is correct because Azure AI Search can index the policy documents stored in Azure Blob Storage, enabling retrieval of relevant passages based on the user's query. These passages are then provided as context in the prompt to GPT-4, ensuring the generated answer is grounded in the actual policy content rather than relying on the model's pre-trained knowledge.
Variation 3. A company wants to build a chatbot that answers customer questions using a large language model. The company has an extensive internal knowledge base with accurate, up-to-date product information. To ensure the chatbot's answers are based on this reliable source rather than the model's internal knowledge, which technique should they use?
medium- A.Fine-tuning the model on the knowledge base
- B.Zero-shot learning
- ✓ C.Grounding with retrieval-augmented generation
- D.Prompt engineering with few-shot examples
Why C: Option C is correct because grounding with retrieval-augmented generation (RAG) retrieves relevant, up-to-date chunks from the internal knowledge base and provides them as context to the large language model (LLM) at inference time. This ensures the chatbot's answers are factually based on the company's reliable source rather than relying on the model's potentially outdated or incorrect parametric memory.
Variation 4. A company wants to build a chatbot that answers customer questions using only their internal knowledge base, which consists of several PDFs and Word documents. They do not want the chatbot to use any information from the model's pre-trained knowledge. Which Azure OpenAI feature should they use to achieve this?
easy- A.Content filtering
- B.Prompt flow
- ✓ C.Azure OpenAI on your data
- D.Temperature parameter
Why C: Azure OpenAI on your data allows you to connect Azure OpenAI models to your own data sources (such as PDFs and Word documents) and restrict the model to generate responses solely from that data, without using the model's pre-trained knowledge. This is achieved by indexing the documents into an Azure Cognitive Search index and using retrieval-augmented generation (RAG) to ground the model's responses in your specific content.
Variation 5. A company uses a large language model to generate answers to employee questions about internal HR policies. However, the model sometimes produces answers that are factually incorrect or not based on the official policies. To reduce these inaccuracies, the company wants to provide the model with relevant, up-to-date policy documents as extra context before generating a response. Which technique is being applied?
medium- A.Prompt engineering only
- B.Fine-tuning the model on policy documents
- ✓ C.Grounding with relevant data (RAG)
- D.Using a content filter
Why C: The technique described is Retrieval-Augmented Generation (RAG), which retrieves relevant, up-to-date policy documents from an external knowledge base and provides them as context to the large language model before generating a response. This grounds the model's output in verified data, reducing factual inaccuracies without modifying the model itself. Option C is correct because RAG directly addresses the need to supply extra context from authoritative sources.
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.