Data Management Services And Vector Search →easyMultiple ChoiceObjective-mapped

AI-200 Data Management Services And Vector Search Practice Question

You are storing vector embeddings generated by Azure OpenAI in Azure Cosmos DB for NoSQL. Which built-in SQL function must you use within your query to compute vector distance when executing a similarity 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

✓

VectorDistance()

Azure Cosmos DB for NoSQL provides native vector search functions including VectorDistance, which computes the distance between two vectors using Cosine, DotProduct, or Euclidean distance metrics.

Answer analysis

Option-by-option breakdown

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

✗
EmbeddingDistance()
Why it's wrong here
EmbeddingDistance is not a supported function in Azure Cosmos DB.
✗
GetVectorDistance()
Why it's wrong here
GetVectorDistance is an incorrect function name.
✓
VectorDistance()
Why this is correct
VectorDistance is the official built-in SQL function in Azure Cosmos DB for NoSQL designed to compute distance metrics between vector arrays.
✗
CosineSimilarity()
Why it's wrong here
CosineSimilarity is not a native built-in SQL function in Azure Cosmos DB for NoSQL; VectorDistance handles multiple distance algorithms including cosine.

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

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