- A
Increase the temperature parameter
Why wrong: Temperature controls randomness, not grounding. Higher temperature increases variability but does not prevent hallucination.
- B
Increase the frequency_penalty parameter
Why wrong: Frequency_penalty reduces repetition by penalizing tokens that have already appeared, but it does not anchor the model to source data.
- C
Use the system message to instruct the model to only use provided content
Why wrong: A system message can guide behavior but is a soft instruction. The model may still ignore it, especially for longer or complex queries. It does not mechanically restrict the model to use only supplied data.
- D
Use the 'Add your data' feature (also known as 'Azure OpenAI on your data')
This feature enables you to connect your own data sources to the model. The model then retrieves relevant information from your data to generate responses, significantly reducing hallucinations and ensuring the output is based on the provided content.
Quick Answer
The correct answer is to use the 'Add your data' feature, also known as Azure OpenAI on your data. This feature grounds the model's responses by anchoring its knowledge base exclusively to the specific documents you upload, such as the original report, which directly prevents the model from fabricating details not present in the source material. By restricting the model's context to your provided content, it eliminates the hallucination issue described, ensuring the generated executive summaries are faithful to the report. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of how to mitigate model inaccuracies through data grounding, often appearing as a question about improving response reliability. A common trap is confusing this with content filtering or fine-tuning, but remember: fine-tuning changes behavior, while 'Add your data' restricts the source. Memory tip: think of it as "anchoring the AI to your own dock" — the model can only sail within the waters of your uploaded documents.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 company uses Azure OpenAI Service to generate executive summaries of lengthy reports. The generated summaries sometimes include information that was not present in the original report, making them unreliable. Which Azure OpenAI Service feature should the company use to anchor the model to the provided report content?
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 the 'Add your data' feature (also known as 'Azure OpenAI on your data')
The 'Add your data' feature (Azure OpenAI on your data) allows the model to ground its responses in the specific content you provide, such as the original report. This prevents the model from generating information not present in the source, addressing the hallucination issue directly by restricting the model's knowledge base to the uploaded documents.
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.
- ✗
Increase the temperature parameter
Why it's wrong here
Temperature controls randomness, not grounding. Higher temperature increases variability but does not prevent hallucination.
- ✗
Increase the frequency_penalty parameter
Why it's wrong here
Frequency_penalty reduces repetition by penalizing tokens that have already appeared, but it does not anchor the model to source data.
- ✗
Use the system message to instruct the model to only use provided content
Why it's wrong here
A system message can guide behavior but is a soft instruction. The model may still ignore it, especially for longer or complex queries. It does not mechanically restrict the model to use only supplied data.
- ✓
Use the 'Add your data' feature (also known as 'Azure OpenAI on your data')
Why this is correct
This feature enables you to connect your own data sources to the model. The model then retrieves relevant information from your data to generate responses, significantly reducing hallucinations and ensuring the output is based on the provided content.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often think a system message or parameter adjustment can reliably enforce content grounding, but only the 'Add your data' feature provides a technical mechanism to restrict the model's knowledge to the provided documents.
Detailed technical explanation
How to think about this question
The 'Add your data' feature uses a retrieval-augmented generation (RAG) pattern, where the user's documents are indexed via Azure Cognitive Search and relevant chunks are injected into the prompt at inference time. This ensures the model's output is constrained to the retrieved content, effectively eliminating hallucinations from external knowledge. In real-world scenarios, this is critical for compliance-heavy industries like legal or finance, where even minor fabrications can have serious consequences.
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
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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: Use the 'Add your data' feature (also known as 'Azure OpenAI on your data') — The 'Add your data' feature (Azure OpenAI on your data) allows the model to ground its responses in the specific content you provide, such as the original report. This prevents the model from generating information not present in the source, addressing the hallucination issue directly by restricting the model's knowledge base to the uploaded documents.
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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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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