AI-900 Practice Question: Describe features of generative AI workloads on Azure
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?
⚠ Common exam trap
Many candidates 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.
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
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.
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 is a sampling parameter that controls the randomness of token selection; setting it to 0 makes the output deterministic (greedy decoding). It has no impact on the model's knowledge source or its ability to access external information. Even with temperature at 0, the model will still produce answers from its pretrained parameters and may include facts not present in the provided documents.
When this WOULD be correct
When the goal is to reduce randomness and ensure consistent, factual responses from the model, such as in a customer service chatbot where answers must be uniform and predictable.
- ✗
Use the system message to instruct the model to only use provided documents
Why it's wrong here
System messages set the conversational behavior and can request that the model base answers on provided context, but they are not a hard guarantee. The underlying transformer model has no built-in enforcement mechanism for such instructions; without retrieval grounding, the model may fall back on its training data, especially if the provided documents lack the specific answer. System messages are advisory, not a technical constraint.
When this WOULD be correct
This option would be correct in a scenario where the model must follow a specific instruction (e.g., 'only use the provided text') but the underlying model inherently respects such instructions without needing a retrieval mechanism, such as in a controlled fine-tuned environment or when the documents are included in the prompt.
- ✓
Use the 'Azure OpenAI on your data' feature with a 'Search' data source containing the documents
Why this is correct
This feature uses Azure Cognitive Search to index and chunk the provided documents, then performs retrieval-augmented generation (RAG). At inference, the model receives the top retrieved passages as part of the prompt context, effectively constraining its responses to the content of those documents. Because the grounding data is injected into the prompt and the model is instructed to answer based on that data, it prevents reliance on the model's parametric training knowledge.
- ✗
Set the 'max_tokens' parameter to a low value
Why it's wrong here
The max_tokens parameter controls the maximum number of tokens (words/subwords) in the generated response, effectively capping output length. It does not influence what information the model uses to formulate the answer; the model still draws on its pretrained knowledge from public internet data. A low value may truncate the answer mid-sentence or force overly terse responses, but it cannot confine the model to the provided documents.
When this WOULD be correct
A scenario where the question is about controlling the length of the model's output, such as 'A chatbot must provide very short answers to conserve tokens. Which parameter should be adjusted?' In that case, setting max_tokens to a low value would be correct.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Use the 'Azure OpenAI on your data' feature with a 'Search' data source containing the documentsCorrect answer▾
Why this is correct
This feature uses Azure Cognitive Search to index and chunk the provided documents, then performs retrieval-augmented generation (RAG). At inference, the model receives the top retrieved passages as part of the prompt context, effectively constraining its responses to the content of those documents. Because the grounding data is injected into the prompt and the model is instructed to answer based on that data, it prevents reliance on the model's parametric training knowledge.
✗Increase the 'temperature' parameter to 0.0Wrong answer — click to see why▾
Why this is wrong here
Setting temperature to 0.0 makes output more deterministic but does not restrict the model to use only provided documents; the model can still rely on its training data.
★ When this WOULD be the correct answer
When the goal is to reduce randomness and ensure consistent, factual responses from the model, such as in a customer service chatbot where answers must be uniform and predictable.
Why candidates choose this
Candidates may think that lowering temperature to 0.0 eliminates creativity and thus forces the model to stick to given facts, misunderstanding that temperature controls randomness, not knowledge source.
✗Use the system message to instruct the model to only use provided documentsWrong answer — click to see why▾
Why this is wrong here
Using a system message to instruct the model to only use provided documents does not prevent the model from using its pre-trained knowledge; it can still generate answers based on its training data, which violates the requirement of exclusive reliance on the provided documents.
★ When this WOULD be the correct answer
This option would be correct in a scenario where the model must follow a specific instruction (e.g., 'only use the provided text') but the underlying model inherently respects such instructions without needing a retrieval mechanism, such as in a controlled fine-tuned environment or when the documents are included in the prompt.
Why candidates choose this
Candidates may believe that a clear system message is sufficient to constrain the model's behavior, underestimating the model's tendency to incorporate its training knowledge even when instructed otherwise.
✗Set the 'max_tokens' parameter to a low valueWrong answer — click to see why▾
Why this is wrong here
Setting max_tokens to a low value limits the length of the model's response, but does not restrict the model from using external knowledge from its training data. The model can still generate answers based on its pre-trained knowledge, which contradicts the requirement to base answers exclusively on provided documents.
★ When this WOULD be the correct answer
A scenario where the question is about controlling the length of the model's output, such as 'A chatbot must provide very short answers to conserve tokens. Which parameter should be adjusted?' In that case, setting max_tokens to a low value would be correct.
Why candidates choose this
Candidates may mistakenly believe that limiting the output length (max_tokens) also limits the model's knowledge source, confusing token limits with data grounding or source control.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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Azure Machine Learning Studio
Key term
Azure OpenAI Service
Azure OpenAI Service is a cloud platform from Microsoft that lets developers use powerful artificial intelligence models, like GPT-4, to build applications that can understand and generate human-like text, code, images, and more.
Key term
Search index
A search index is a data structure that stores a mapping of content to locations, enabling fast and efficient information retrieval in search systems.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.