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AI-200 Data Management Services And Vector Search Practice Question

You are designing a vector search solution using Azure AI Search. Which THREE of the following are necessary steps to configure an index to support vector search?

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 a 'vectorSearch' configuration within the index schema.

To enable vector search, one must define the vector search configuration (algorithm/metric), create fields of type 'Collection(Edm.Single)', and assign the vector search profile to those fields.

Answer analysis

Option-by-option breakdown

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

✓
Define a 'vectorSearch' configuration within the index schema.
Why this is correct
This defines the algorithms and metrics to be used.
✗
Set the index to 'ReadOnly' mode.
Why it's wrong here
Read-only mode would prevent the indexing of new vectors.
✓
Assign a 'vectorSearchProfile' to the vector-capable fields.
Why this is correct
Fields must be mapped to a search profile to enable search operations.
✗
Ensure all vector fields are set to 'Edm.String'.
Why it's wrong here
Vector fields must be 'Collection(Edm.Single)'.
✓
Define vector fields as type 'Collection(Edm.Single)'.
Why this is correct
This is the required data type for vector embeddings.

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Last reviewed August 2026 · checked against the official Microsoft exam blueprint

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