AI-200 Data Management Services And Vector Search Practice Question
You are building a multi-modal AI application that searches across both product images and product descriptions. You store your data in Azure AI Search. How should you define your index schema to support searching with both image embeddings and text embeddings?
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
✓
Define two separate Edm.SingleCollection vector fields with distinct dimensions and vector profiles
To support multi-modal search, you define two separate vector fields in the index schema, each with its own dimensions and vector profile mapped to the respective embedding model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Concatenate the image and text embeddings into a single 3072-dimensional vector field
Why it's wrong here
Concatenating heterogeneous embeddings without aligning vector spaces degrades retrieval quality.
- ✗
Use a single Edm.String field containing both embeddings as JSON strings
Why it's wrong here
String fields cannot be queried using vector search functions in Azure AI Search.
- ✗
Create two completely separate search services
Why it's wrong here
A single search service and index can handle multiple vector fields.
- ✓
Define two separate Edm.SingleCollection vector fields with distinct dimensions and vector profiles
Why this is correct
Multiple vector fields with different configurations allow simultaneous text and image vector searches.
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.