AI-200 Data Management Services And Vector Search Practice Question
When configuring vector search in Azure AI Search, which TWO compression options or algorithm settings can be defined to optimize resource usage? (Choose two)
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
✓
Scalar quantization configuration
Scalar quantization and HNSW algorithm parameters are key configurations used to optimize vector search performance and resource usage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Scalar quantization configuration
Why this is correct
Scalar quantization compresses vectors to reduce memory consumption.
- ✓
HNSW algorithm parameters (m, efConstruction)
Why this is correct
HNSW parameters control index build time, memory, and accuracy.
- ✗
Blob storage container access level
Why it's wrong here
Blob storage access level does not affect search index compression.
- ✗
SQL table partitioning scheme
Why it's wrong here
SQL partitioning does not apply to Azure AI Search configuration.
- ✗
Cosmos DB throughput auto-scale limits
Why it's wrong here
Cosmos DB auto-scale is unrelated to Azure AI Search.
About these practice questions
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JA
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
This AI-200 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-200 exam.