AI-200 Data Management Services And Vector Search Practice Question
You are configuring a vector index in Azure AI Search and want to apply scalar quantization to reduce the memory footprint. What is the primary effect of scalar quantization on float32 vector data?
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
✓
It compresses float32 vectors to int8 integers, reducing memory usage by approximately 75%
Scalar quantization converts 32-bit floating-point numbers into 8-bit integers (int8), reducing memory consumption by roughly 4x while maintaining high search recall accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It increases vector dimensions from 1536 to 3072 to improve accuracy
Why it's wrong here
Quantization reduces memory, it does not increase dimensions.
- ✗
It eliminates the need for an HNSW graph structure
Why it's wrong here
Quantization optimizes the existing HNSW graph nodes, it does not remove the graph structure.
- ✗
It converts vectors into plain text strings for keyword indexing
Why it's wrong here
Quantization keeps data in numeric vector format optimized for distance calculations.
- ✓
It compresses float32 vectors to int8 integers, reducing memory usage by approximately 75%
Why this is correct
Converting 32-bit floats to 8-bit integers reduces memory size significantly.
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Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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