AI-102 Plan and manage an Azure AI solution Practice Question
You manage an Azure AI Search service that indexes legal documents. The search latency is high, and you need to improve query performance without reducing index size. Which action should you take?
⚠ Common exam trap
It's easy for candidates to confuse partitions (which scale storage and indexing) with replicas (which scale query performance), leading them to incorrectly choose increasing partitions when the real need is to reduce query latency.
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 replicas
Increasing the number of replicas distributes query load across multiple copies of the index, which directly improves query throughput and reduces latency. Replicas are designed for scaling query operations without changing the index size or storage capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Upgrade to a higher pricing tier
Why it's wrong here
A higher tier supplies more storage and replicas, but latency here stems from query construction, not capacity; the tier change leaves the slow query pattern intact. It tempts because tiers are the standard answer for scaling an Azure AI Search service, and would be right if the bottleneck were resource exhaustion rather than the query itself.
- ✗
Increase the number of partitions
Why it's wrong here
Adding partitions spreads the index across more nodes, raising storage and throughput ceilings rather than cutting per-query latency; the stem forbids reducing index size, and partitions do not do that anyway. It tempts because partitions are the documented lever for query volume, which is the correct fix when concurrent query load saturates replica capacity.
- ✗
Reduce the number of searchable fields
Why it's wrong here
Removing fields from the searchable set shrinks what full-text queries can match, so users lose access to content they need; it does not reduce latency for the remaining fields. It tempts because trimming the schema genuinely cuts index size and cost, which is the right move when storage, not query speed, is the constraint.
- ✓
Increase the number of replicas
Why this is correct
Replicas serve query execution, so adding them distributes search load across more nodes and lowers latency while leaving index size untouched. Partitions would increase storage and index capacity instead, which the stem explicitly rules out.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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