AI-200 Data Management Services And Vector Search Practice Question
When designing a data layer architecture for an AI application using Azure AI Search, what is the primary role of an integrated vectorizer?
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
✓
To automatically convert raw source text fields into vector embeddings during indexing and queries without requiring external orchestration code.
An integrated vectorizer in Azure AI Search automates the process of transforming raw text chunks into vector embeddings directly within the search service pipeline, calling an endpoint like Azure OpenAI automatically during document ingestion and queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To execute full-text BM25 keyword matching alongside vector calculations.
Why it's wrong here
BM25 execution is handled by the lexical search engine component, not the vectorizer.
- ✓
To automatically convert raw source text fields into vector embeddings during indexing and queries without requiring external orchestration code.
Why this is correct
Integrated vectorization handles the translation from text to embeddings natively within Azure AI Search.
- ✗
To compress vector indexes using lossy compression algorithms to save disk storage.
Why it's wrong here
Compression is handled by quantization settings, not integrated vectorizers.
- ✗
To generate cryptographic hashes for document deduplication.
Why it's wrong here
Vectorizers generate semantic embeddings, not cryptographic hashes.
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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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