AI-200 Data Management Services And Vector Search Practice Question
You are troubleshooting slow query performance in an enterprise Azure AI Search vector deployment. Which THREE architectural or configuration adjustments can improve query throughput and latency? Choose three.
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
✓
Scale out the number of search service replicas
To improve vector search performance in Azure AI Search, practitioners can scale out replicas, tune HNSW efSearch parameters, and leverage batching or appropriate tier sizing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Scale out the number of search service replicas
Why this is correct
Adding replicas increases query throughput (QPS) capacity.
- ✗
Replace all vector fields with standard ASCII folding tokenizers
Why it's wrong here
ASCII folding tokenizers are for text normalization, which would break vector search entirely.
- ✓
Ensure the search service is deployed on a Standard tier or higher with adequate RAM
Why this is correct
Standard tiers provide dedicated resources and memory required for high-performance HNSW index caching.
- ✓
Tune the efSearch parameter lower to trade off marginal recall for faster search traversal
Why this is correct
Lowering efSearch reduces graph traversal time, improving query latency.
- ✗
Configure the index to use exhaustive KNN exclusively for high-QPS production workloads
Why it's wrong here
Exhaustive KNN reduces query throughput at scale; HNSW should be used for high-QPS production workloads.
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.