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AI-200 Data Management Services And Vector Search Practice Question

You are optimizing query performance for an Azure AI Search index experiencing high query volumes with vector search. You need to scale out query processing capabilities without increasing index build time. Which action should you perform?

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

Increase the number of search units by adding replica instances

To scale out query throughput (QPS) and handle higher query load for vector search in Azure AI Search, you should increase the number of replica units.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Rebuild the index using exhaustiveKnn

    Why it's wrong here

    Switching to exhaustiveKNN would degrade query performance and throughput.

  • Increase the number of partition instances

    Why it's wrong here

    Adding partitions increases storage capacity and is used when the index exceeds single-partition limits, whereas replicas handle query load.

  • Increase the number of search units by adding replica instances

    Why this is correct

    Adding replicas scales out query throughput and provides high availability for vector search workloads.

  • Upgrade the search service from Standard to Basic tier

    Why it's wrong here

    The Basic tier has fewer scaling capabilities and lower performance than Standard tiers.

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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

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