AI-200 Data Management Services And Vector Search Practice Question
You are designing an application that requires low-latency vector search for a high-traffic AI chatbot. You need to ensure the vector index remains performant as the dataset grows into millions of documents. Which index configuration in Azure AI Search should you implement to optimize for speed over absolute recall accuracy?
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
✓
HNSW
HNSW (Hierarchical Navigable Small World) allows for approximate nearest neighbor search, providing the best performance-to-accuracy balance for large datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Flat Indexing
Why it's wrong here
Flat indexing is computationally expensive and not suitable for large-scale low-latency requirements.
- ✗
Exhaustive KNN
Why it's wrong here
Exhaustive KNN provides perfect accuracy but is too slow for large-scale production workloads.
- ✓
HNSW
Why this is correct
HNSW is the recommended index type for performance in large-scale vector search scenarios.
- ✗
Partitioned Indexing
Why it's wrong here
Partitioning is an architectural strategy, not a specific vector indexing algorithm choice in 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.