Quick Answer
The answer is to increase the number of replicas. Adding replicas distributes the query load across multiple identical copies of your index, enabling parallel processing of search requests which directly reduces query latency. This approach preserves search quality because it does not alter the underlying search logic, custom scoring profiles, or semantic enrichment configurations. On the AI-102 exam, this scenario tests your understanding of scaling strategies in Azure AI Search, often appearing as a distractor where candidates might mistakenly adjust the partition count or modify the index schema. A common trap is confusing replicas (for query performance) with partitions (for indexing throughput and storage). Remember the memory tip: “Replicas for reads, partitions for writes” to quickly recall that adding replicas is the correct move when latency is the issue.
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Your Azure AI Search index is experiencing high query latency. You have enabled semantic search and custom scoring profiles. You need to reduce latency without degrading search quality. Which action should you take?
Remove custom scoring profiles.
Why wrong: Removing scoring profiles degrades relevance.
Increase the number of replicas.
More replicas distribute query load and reduce latency.
Reduce the number of partitions.
Why wrong: Reducing partitions may reduce indexing capacity, not directly help query latency.
Disable semantic search.
Why wrong: Disabling semantic search degrades search quality.
Answer choices
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, allowing parallel processing of search requests. This directly reduces query latency without altering the search logic, scoring profiles, or semantic enrichment, thus preserving search quality.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Remove custom scoring profiles.
Why it's wrong here
Removing scoring profiles degrades relevance.
Increase the number of replicas.
Why this is correct
More replicas distribute query load and reduce latency.
Related concept
Read the scenario before looking for a memorised answer.
Reduce the number of partitions.
Why it's wrong here
Reducing partitions may reduce indexing capacity, not directly help query latency.
Disable semantic search.
Why it's wrong here
Disabling semantic search degrades search quality.
Common exam traps
The trap here is that candidates confuse partitions (which affect storage and indexing speed) with replicas (which affect query throughput), leading them to incorrectly reduce partitions or disable features instead of scaling query capacity.
Detailed technical explanation
In Azure AI Search, replicas are copies of the index that handle query requests independently; each replica can serve concurrent queries, so adding replicas linearly increases query throughput (QPS). Partitions, by contrast, control how the index is sharded for storage and indexing performance—reducing them can cause index rebuilds and does not improve query latency. The semantic search feature uses a separate ranking pipeline that adds minimal latency per query, but the primary bottleneck in high-latency scenarios is often insufficient replicas to handle concurrent requests, not the semantic processing itself.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the number of replicas. — Increasing the number of replicas distributes query load across multiple copies of the index, allowing parallel processing of search requests. This directly reduces query latency without altering the search logic, scoring profiles, or semantic enrichment, thus preserving search quality.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
hardWhy D: 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.
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Last reviewed: Jun 24, 2026
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