AI-200 Data Management Services And Vector Search Practice Question
When configuring vector search in Azure AI Search, which TWO components are required to successfully define and execute a vector search query? (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
✓
A vector query payload specifying the query vector and target field
Executing a vector search query requires a vector-typed field in the index and a query request specifying the vector query vector values and target field.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A Cosmos DB container partition key
Why it's wrong here
Cosmos partition keys do not apply to Azure AI Search queries.
- ✗
An Azure SQL Server database connection string
Why it's wrong here
Azure SQL connections are not required to execute queries against Azure AI Search.
- ✓
A vector query payload specifying the query vector and target field
Why this is correct
The search request must supply the vector query embedding and target field name.
- ✓
A vector field defined in the index schema
Why this is correct
An index must contain a vector field (Edm.SingleCollection) to store and query embeddings.
- ✗
An HDInsight Hadoop cluster endpoint
Why it's wrong here
Hadoop clusters are not involved in Azure AI Search query execution.
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.