AI-102 Implement generative AI solutions Practice Question
You are developing a generative AI application that uses Azure OpenAI Service. The application must generate responses that are grounded in a specific set of documents stored in Azure Blob Storage. You want to use the simplest approach that allows the model to reference these documents without building a custom retrieval pipeline. What should you use?
⚠ Common exam trap
The trap here is assuming that fine-tuning or manual prompt stuffing can achieve grounding, when a managed retrieval service is specifically designed for this.
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
✓
Azure OpenAI On Your Data with Azure Blob Storage as the data source.
Azure OpenAI On Your Data is a managed feature that integrates with Azure Blob Storage, enabling the model to retrieve and cite documents without custom code. It handles indexing and retrieval automatically, making it the simplest solution for grounding responses in a specific document set. Other options require more effort or are not designed for this purpose.
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 the model with the documents and deploy the fine-tuned model.
Why it's wrong here
Fine-tuning is used to adapt a model's style or behavior, not to provide dynamic grounding in a document set. It requires training data in a specific format and does not automatically cite sources. Moreover, fine-tuning does not allow the model to reference updated documents without retraining.
- ✗
Implement a custom RAG pipeline using Azure Cognitive Search and the Chat Completions API.
Why it's wrong here
A custom RAG pipeline requires significant development effort to index, retrieve, and format documents. While it offers flexibility, it is not the simplest approach. Azure OpenAI On Your Data provides a managed alternative that reduces complexity and time to deployment.
- ✓
Azure OpenAI On Your Data with Azure Blob Storage as the data source.
Why this is correct
Azure OpenAI On Your Data natively supports Azure Blob Storage as a data source. It handles indexing, retrieval, and citation generation automatically, requiring minimal custom code. This is the simplest way to ground responses in documents stored in Blob Storage without building a retrieval pipeline.
- ✗
Use the Completions API with a prompt that includes the full text of all documents.
Why it's wrong here
Including all documents in the prompt is impractical due to token limits and cost. It also lacks retrieval logic and citation support. This approach is not scalable and would fail for large document sets, making it unsuitable for the requirement.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This AI-102 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-102 exam.