AI-102 Implement generative AI solutions Practice Question
You are designing a generative AI solution using Azure OpenAI Service with your own data indexed in Azure AI Search. Which THREE components are essential for the retrieval-augmented generation (RAG) pattern?
⚠ Common exam trap
Watch out — candidates often confuse optional production components (like Azure Functions for orchestration or API Management for rate limiting) with the core, mandatory components of the RAG pattern, which are the data source, search index, and the LLM 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
✓
Data ingestion pipeline to Azure AI Search
A data ingestion pipeline is essential to load and index your data into Azure AI Search, enabling the retrieval step in RAG. Without this pipeline, the search index would have no data to query, breaking the retrieval-augmented generation pattern.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Data ingestion pipeline to Azure AI Search
Why this is correct
Data must be ingested into the search index.
- ✓
Azure AI Search index
Why this is correct
The index stores and retrieves relevant data chunks.
- ✗
Azure Functions for orchestration
Why it's wrong here
Orchestration can be done without Functions.
- ✗
Azure API Management for rate limiting
Why it's wrong here
Rate limiting is not part of the core RAG pattern.
- ✓
Azure OpenAI model
Why this is correct
The model generates responses based on retrieved context.
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