AI-200 Data Management Services And Vector Search Practice Question
You are optimizing search performance in Azure AI Search. You notice that vector queries are consuming excessive memory and slowing down. You decide to enable exhaustive k-NN fallback for specific queries. What does exhaustive k-NN do?
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 searches all vectors exactly without relying on an approximate nearest neighbor graph
Exhaustive k-NN performs an exact search over all vectors in the index rather than an approximate search using an HNSW graph, ensuring 100% recall at the expense of higher query 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.
- ✗
It compresses the vector index by 50%
Why it's wrong here
Compression is handled by quantization, not exhaustive search.
- ✗
It shards the index across multiple search units
Why it's wrong here
Sharding is an architectural partitioning feature, not exhaustive search behavior.
- ✓
It searches all vectors exactly without relying on an approximate nearest neighbor graph
Why this is correct
Exhaustive k-NN computes exact distances against all vectors to guarantee 100% recall.
- ✗
It caches query results in Redis
Why it's wrong here
Exhaustive k-NN does not involve Redis caching.
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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.