AI-200 Data Management Services And Vector Search Practice Question
You are integrating Azure OpenAI Service's text-embedding-ada-002 model with Azure AI Search. You want to automate the generation of embeddings directly inside the search service during data ingestion. Which component of Azure AI Search should you configure?
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
✓
Azure AI Search Indexer with integrated vectorization
Indexers in Azure AI Search support integrated vectorization, allowing you to connect to Azure OpenAI to automatically chunk text and generate embeddings during the indexing pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Semantic ranker configuration profile
Why it's wrong here
The semantic ranker is applied post-retrieval to re-rank results, not during ingestion to generate embeddings.
- ✓
Azure AI Search Indexer with integrated vectorization
Why this is correct
Integrated vectorization in Azure AI Search automatically handles embedding generation from raw text fields using Azure OpenAI connections during indexing.
- ✗
Azure Function scheduled trigger for batch updates
Why it's wrong here
An Azure Function is an external architecture pattern, whereas integrated vectorization is a native platform feature.
- ✗
Custom Web API skill in Azure AI Services enrichment pipeline
Why it's wrong here
While custom skills can call external APIs, Azure AI Search now features native integrated vectorization for Azure OpenAI without requiring custom skill code.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-200 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-200 exam.