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.
Which TWO Azure services can be used together with Azure OpenAI to implement a Retrieval-Augmented Generation (RAG) solution?
Azure Blob Storage
Why wrong: Blob Storage is for file storage, not search.
Azure Cosmos DB
Cosmos DB supports vector indexing.
Azure AI Search
Azure AI Search provides vector and hybrid search.
Azure SQL Database
Why wrong: SQL Database lacks native vector search.
Azure Functions
Why wrong: Functions is compute, not a data store.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Azure Cosmos DB
Azure Cosmos DB is correct because it can serve as a vector database for storing and querying embeddings generated by Azure OpenAI. When combined with Azure AI Search, which provides hybrid search (vector + keyword) and indexing capabilities, it enables a complete RAG pipeline where relevant documents are retrieved and passed as context to the OpenAI model for grounded generation.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Azure Blob Storage
Why it's wrong here
Blob Storage is for file storage, not search.
Azure Cosmos DB
Why this is correct
Cosmos DB supports vector indexing.
Related concept
Read the scenario before looking for a memorised answer.
Azure AI Search
Why this is correct
Azure AI Search provides vector and hybrid search.
Related concept
Read the scenario before looking for a memorised answer.
Azure SQL Database
Why it's wrong here
SQL Database lacks native vector search.
Azure Functions
Why it's wrong here
Functions is compute, not a data store.
Common exam traps
The trap here is that candidates often assume any storage service (like Blob Storage or SQL Database) can directly support RAG, but they overlook the need for native vector search or hybrid retrieval capabilities that only Azure AI Search and Cosmos DB (with vector support) provide.
Detailed technical explanation
In a RAG solution, Azure Cosmos DB can store vector embeddings using its vCore-based MongoDB or NoSQL API with vector indexing, while Azure AI Search supports integrated vectorization with skillsets that chunk and embed documents automatically. A subtle behavior is that Azure AI Search can also use its built-in vectorizer to generate embeddings at query time, reducing the need for separate embedding calls to Azure OpenAI, but this requires careful tuning of the vector search parameters (e.g., HNSW algorithm, efConstruction) to balance recall and latency.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Cosmos DB — Azure Cosmos DB is correct because it can serve as a vector database for storing and querying embeddings generated by Azure OpenAI. When combined with Azure AI Search, which provides hybrid search (vector + keyword) and indexing capabilities, it enables a complete RAG pipeline where relevant documents are retrieved and passed as context to the OpenAI model for grounded generation.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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