AI-102 Implement generative AI solutions Practice Question
Your organization is building a chatbot using Azure OpenAI Service. The chatbot must provide citations from a set of internal documents stored in Azure Blob Storage. You need to configure the solution to minimize token usage while ensuring citations are accurate. Which approach should you use?
⚠ Common exam trap
Test-takers frequently confuse fine-tuning with retrieval-augmented generation (RAG), assuming fine-tuning can store factual knowledge for citation, when in reality RAG with a search index is required for accurate, token-efficient document grounding.
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 Azure OpenAI on your data with Azure Cognitive Search for hybrid retrieval
Azure OpenAI on your data with Azure Cognitive Search for hybrid retrieval combines vector search and keyword search to efficiently find relevant document chunks from Azure Blob Storage, minimizing token usage by only sending the most pertinent content to the model for citation generation. This approach ensures accurate citations without embedding all documents into the prompt or relying on model memory.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Embed all document content into the system prompt
Why it's wrong here
Embedding full documents consumes many tokens and may exceed context limits.
- ✗
Fine-tune a model on the documents so it can recall them from memory
Why it's wrong here
Fine-tuning cannot guarantee accurate citations and may introduce hallucination.
- ✗
Use a large context window model (e.g., 32K) and include all documents in the prompt
Why it's wrong here
Large context windows still waste tokens and increase latency.
- ✓
Use Azure OpenAI on your data with Azure Cognitive Search for hybrid retrieval
Why this is correct
Hybrid retrieval reduces token usage by fetching only relevant chunks.
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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Senior Network & Security Engineer · founder of Courseiva
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