- A
Increase the 'temperature' parameter to 0.0
Why wrong: Temperature controls randomness; setting it to 0 makes outputs deterministic but does not prevent the model from using its training knowledge beyond the provided documents.
- B
Use the system message to instruct the model to only use provided documents
Why wrong: While system messages can guide behavior, they are not a reliable guarantee that the model will ignore its training data. The model may still incorporate external knowledge.
- C
Use the 'Azure OpenAI on your data' feature with a 'Search' data source containing the documents
This feature ingests and indexes the documents, then grounds the model's responses to the retrieved content, ensuring answers are based solely on the provided data.
- D
Set the 'max_tokens' parameter to a low value
Why wrong: Max_tokens limits the length of the response, but does not restrict the model from using external knowledge; it only truncates the output.
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 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?
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 'Azure OpenAI on your data' feature with a 'Search' data source containing the documents
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.
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 to 0.0
Why it's wrong here
Temperature controls randomness; setting it to 0 makes outputs deterministic but does not prevent the model from using its training knowledge beyond the provided documents.
- ✗
Use the system message to instruct the model to only use provided documents
Why it's wrong here
While system messages can guide behavior, they are not a reliable guarantee that the model will ignore its training data. The model may still incorporate external knowledge.
- ✓
Use the 'Azure OpenAI on your data' feature with a 'Search' data source containing the documents
Why this is correct
This feature ingests and indexes the documents, then grounds the model's responses to the retrieved content, ensuring answers are based solely on the provided data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set the 'max_tokens' parameter to a low value
Why it's wrong here
Max_tokens limits the length of the response, but does not restrict the model from using external knowledge; it only truncates the output.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think a system message or parameter tuning (like temperature or max_tokens) can restrict the model's knowledge source, but only the 'on your data' feature with a search index enforces exclusive grounding in provided documents.
Trap categories for this question
Command / output trap
Temperature controls randomness; setting it to 0 makes outputs deterministic but does not prevent the model from using its training knowledge beyond the provided documents.
Detailed technical explanation
How to think about this question
Under the hood, 'Azure OpenAI on your data' integrates with Azure Cognitive Search to create a search index from the uploaded documents. When a query is made, the system retrieves the top-k relevant document chunks via vector or keyword search, appends them to the prompt as context, and instructs the model to answer solely from that context. This technique, known as Retrieval-Augmented Generation (RAG), effectively grounds the model's responses in the provided data, eliminating reliance on pre-trained weights. A subtle behavior is that if the search index does not contain relevant chunks, the model may still attempt to answer from its training data unless the system message explicitly forbids it, but the RAG architecture combined with proper prompt engineering minimizes this risk.
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 'Azure OpenAI on your data' feature with a 'Search' data source containing the documents — 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.
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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