AI-200 Data Management Services And Vector Search Practice Question
You are designing an AI search architecture that requires handling millions of documents with sub-second vector similarity queries. You choose Azure AI Search. How does Azure AI Search ensure high query throughput for vector indexes?
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
✓
By utilizing HNSW approximate nearest neighbor indexing combined with multi-replica scale-out
Azure AI Search uses HNSW approximate nearest neighbor graphs combined with multi-replica scale-out architecture to deliver high throughput and low latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
By caching every possible query result indefinitely in memory
Why it's wrong here
Infinite caching is unfeasible given infinite potential vector query variations.
- ✗
By executing sequential full table scans across all storage nodes simultaneously
Why it's wrong here
Sequential scans are slow and would not achieve sub-second queries on millions of documents without indexing.
- ✓
By utilizing HNSW approximate nearest neighbor indexing combined with multi-replica scale-out
Why this is correct
HNSW indexing and replica scale-out provide high throughput for vector queries.
- ✗
By converting all vectors into relational database tables with SQL foreign keys
Why it's wrong here
Relational table conversion does not provide high-throughput vector 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.