The correct answer is an embedding model deployment in Azure OpenAI, Azure AI Search, and a data source connection to Azure Blob Storage. These three components form the backbone of a retrieval-augmented generation solution: the embedding model converts your internal documents into vector representations, Azure AI Search indexes those vectors and enables hybrid retrieval, and Blob Storage serves as the source of truth for your document chunks. On the AI-102 exam, this question tests your understanding of how RAG pipelines integrate Azure services—a common trap is selecting Azure OpenAI’s chat completion model alone, forgetting that without an embedding model and a search index, the model cannot retrieve relevant context. Remember the mnemonic “E-S-B” for Embeddings, Search, Blob: you need all three to ground your chatbot in your own data.
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. 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.
Your organization is deploying a generative AI chatbot using Azure OpenAI Service. The chatbot must answer questions based on internal documents stored in Azure Blob Storage. You need to implement a retrieval-augmented generation (RAG) solution. Which THREE components are required? (Select THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure AI Search index
Option B is correct because Azure AI Search is the core indexing and retrieval engine in a RAG solution. It ingests documents from Azure Blob Storage, creates a searchable index, and enables vector or hybrid search to retrieve relevant chunks. The chatbot then uses these retrieved chunks as context for the Azure OpenAI model to generate grounded answers.
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.
✗
Azure Functions for preprocessing
Why it's wrong here
Not a required component; can be done by other means.
✓
Azure AI Search index
Why this is correct
Stores embeddings and enables vector search.
Related concept
Read the scenario before looking for a memorised answer.
✓
Azure OpenAI On Your Data configuration
Why this is correct
Orchestrates retrieval and generation with the model.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure SQL Database for metadata
Why it's wrong here
Metadata can be stored in Azure AI Search; SQL is not required.
✓
Embedding model deployment in Azure OpenAI
Why this is correct
Generates embeddings for documents and queries.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse optional preprocessing components (like Azure Functions) or auxiliary storage (like Azure SQL Database) as mandatory, when the three essential pillars are the search index, the embedding model, and the Azure OpenAI On Your Data integration that ties retrieval to generation.
Detailed technical explanation
How to think about this question
Under the hood, the RAG pattern uses an embedding model (e.g., text-embedding-ada-002) deployed in Azure OpenAI to convert document chunks into vector embeddings, which are stored in the Azure AI Search index alongside textual content. During query time, the user's question is also embedded, and Azure AI Search performs a hybrid search (combining vector similarity with keyword search) to retrieve the most relevant chunks. The Azure OpenAI On Your Data configuration then orchestrates the retrieval step and passes the context to the chat model (e.g., GPT-4) without requiring custom code.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure AI Search index — Option B is correct because Azure AI Search is the core indexing and retrieval engine in a RAG solution. It ingests documents from Azure Blob Storage, creates a searchable index, and enables vector or hybrid search to retrieve relevant chunks. The chatbot then uses these retrieved chunks as context for the Azure OpenAI model to generate grounded answers.
What should I do if I get this AI-102 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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