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 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?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Azure AI Search to index the documents and provide relevant passages as context to GPT-4
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.
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.
✗
Fine-tune GPT-4 on all policy documents
Why it's wrong here
Fine-tuning is expensive and may cause the model to memorize or forget information; it is not optimal for dynamic document sets.
✓
Use Azure AI Search to index the documents and provide relevant passages as context to GPT-4
Why this is correct
This is the RAG approach: retrieve relevant content and pass it as context, ensuring answers are based on actual policy text.
Related concept
Read the scenario before looking for a memorised answer.
✗
Include the entire policy document text in the prompt each time
Why it's wrong here
Prompt size limits make this impractical for large documents, and it would be inefficient.
✗
Use DALL-E to visualize policy concepts
Why it's wrong here
DALL-E generates images, not text answers; it is not suitable for question answering.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse fine-tuning (Option A) with retrieval-augmented generation, assuming that training the model on the data is the only way to ground answers, when in fact RAG provides a more flexible and cost-effective solution for dynamic or large document sets.
Detailed technical explanation
How to think about this question
This approach is known as Retrieval-Augmented Generation (RAG), where Azure AI Search performs vector and keyword hybrid search over indexed chunks of the policy documents, retrieving the top-k relevant passages. These passages are injected into the GPT-4 system message or user prompt, allowing the model to synthesize answers based on the provided context while reducing hallucination. In real-world deployments, chunk size and overlap strategies (e.g., 500 tokens with 100-token overlap) are critical to balance retrieval precision and context completeness.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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 Azure AI Search to index the documents and provide relevant passages as context to GPT-4 — 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.
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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Question Discussion
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