AI-102 Implement generative AI solutions Practice Question
Which THREE components are required to build a custom chat application using Azure OpenAI Service that can answer questions based on your own private data?
⚠ Common exam trap
Many candidates confuse fine-tuning (Option D) with retrieval-augmented generation, assuming that custom data requires model retraining, when in fact the 'Add your data' feature uses a RAG approach that does not modify the base model.
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
✓
The 'Add your data' feature configured in Azure OpenAI Studio.
The 'Add your data' feature in Azure OpenAI Studio provides a no-code interface to connect your private data sources (e.g., Azure Blob Storage, local files) to an Azure OpenAI chat model. It automatically chunks the data, creates an Azure AI Search index, and configures the retrieval-augmented generation (RAG) pipeline, enabling the model to answer questions grounded in your proprietary content without fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The 'Add your data' feature configured in Azure OpenAI Studio.
Why this is correct
Enables grounding on private data.
- ✓
Azure AI Search index.
Why this is correct
Required for data retrieval.
- ✓
An Azure OpenAI Service deployment.
Why this is correct
Required to host the model.
- ✗
A fine-tuned custom model.
Why it's wrong here
Fine-tuning is not required for a RAG approach.
- ✗
An Azure OpenAI embeddings model deployment.
Why it's wrong here
Embeddings are used internally but not a separate 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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