AI-200 Data Management Services And Vector Search Practice Question
When designing an AI architecture that uses Azure AI Search, which TWO methods can be used to ingest documents and their vector embeddings into the search index? (Choose two)
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
✓
Push API using HTTP POST requests to upload documents directly
Azure AI Search supports pushing data via the Push API (REST/SDK) or pulling data using indexers connected to data sources like Blob Storage or Cosmos DB.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Direct mounting of an NFS network share as the search index
Why it's wrong here
Search indexes are managed stores, not directly mounted NFS shares.
- ✗
FTP file transfer into the search engine storage directory
Why it's wrong here
FTP is not a supported ingestion method for Azure AI Search.
- ✓
Push API using HTTP POST requests to upload documents directly
Why this is correct
Applications can push documents and pre-computed embeddings directly via the REST API or SDK.
- ✗
Direct SQL Server backup restore (.bak file import)
Why it's wrong here
Azure AI Search does not support direct SQL Server backup restores.
- ✓
Indexers connected to supported data sources with integrated vectorization
Why this is correct
Indexers can automatically pull data from sources and generate vectors using integrated vectorization.
About these practice questions
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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.