Data Management Services And Vector Search →hardMultiple SelectObjective-mapped

AI-200 Data Management Services And Vector Search Practice Question

You are designing an enterprise search architecture on Azure that requires hybrid search capabilities. Which THREE components or features must be configured to successfully execute a hybrid query with semantic ranking in Azure AI Search? (Choose three)

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 searchable Edm.String text field for keyword matching

To run hybrid search with semantic ranking, you need a vector field, a text field for BM25 keyword search, and semantic configuration enabled on the index.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Azure Blob Storage configured as an external data source
Why it's wrong here
Data sources are only needed if using indexers; you can push data via API without an external data source.
✓
A searchable Edm.String text field for keyword matching
Why this is correct
BM25 full-text search relies on searchable Edm.String fields.
✓
A vector field defined with Edm.SingleCollection and a vector profile
Why this is correct
A vector field is required to store embeddings for vector search.
✗
An Azure Functions application for manual score merging
Why it's wrong here
Azure AI Search handles hybrid score merging and RRF natively without requiring custom Azure Functions.
✓
A semantic configuration defined on the index
Why this is correct
Semantic configuration specifies fields for title, content, and keywords used by the semantic ranker.

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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

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