Quick Answer
The answer is Search latency (average and P99) and Throttled search queries count. These two monitoring metrics are essential for ensuring the health and performance of an Azure AI Search service because latency directly reflects the responsiveness of your customer-facing product catalog, while throttled queries indicate when the service is overwhelmed and rate-limiting requests to protect itself. On the Microsoft Azure AI Engineer Associate AI-102 exam, this concept tests your understanding of operational monitoring for search solutions, often appearing in scenario-based questions where you must choose metrics that align with user experience and capacity planning. A common trap is focusing only on average latency, but the P99 percentile reveals tail-end performance issues that degrade the worst-case user experience. Remember the mnemonic “LATE” for Latency, Average, Throttled, and Extreme (P99) to recall the core health indicators for Azure AI Search.
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.
Which TWO monitoring metrics should you track to ensure the health and performance of an Azure AI Search service used for a customer-facing product catalog?
Throttled search queries count.
Throttling indicates capacity bottlenecks.
Indexer execution history and duration.
Why wrong: Important for indexing health but not primary for service health.
Storage used in GB.
Why wrong: Capacity metric, not real-time health.
Search latency (average and P99).
Latency directly affects user experience.
Number of successful search requests.
Why wrong: Volume metric, not indicative of problems.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Throttled search queries count.
Throttled search queries count (Option A) is a critical health metric because it directly indicates when the search service is under excessive load, causing requests to be rate-limited. For a customer-facing product catalog, throttling degrades user experience and can lead to failed searches. Tracking this metric helps you decide when to scale up replicas or partitions to maintain service level agreements.
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.
Throttled search queries count.
Why this is correct
Throttling indicates capacity bottlenecks.
Related concept
Read the scenario before looking for a memorised answer.
Indexer execution history and duration.
Why it's wrong here
Important for indexing health but not primary for service health.
Storage used in GB.
Why it's wrong here
Capacity metric, not real-time health.
Search latency (average and P99).
Why this is correct
Latency directly affects user experience.
Related concept
Read the scenario before looking for a memorised answer.
Number of successful search requests.
Why it's wrong here
Volume metric, not indicative of problems.
Common exam traps
The trap here is that candidates often confuse operational metrics (like indexer duration or storage usage) with customer-facing performance metrics, leading them to select indexer execution history instead of search latency.
Detailed technical explanation
Search latency (average and P99) is the second correct metric because it captures the end-to-end response time for queries, with P99 highlighting tail latency that impacts the worst-performing requests. Under the hood, Azure AI Search uses a distributed architecture where latency spikes can occur due to hot partitions, network congestion, or complex scoring profiles. In a real-world product catalog, a P99 latency above 2 seconds can cause user abandonment, making this metric essential for performance monitoring.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Throttled search queries count. — Throttled search queries count (Option A) is a critical health metric because it directly indicates when the search service is under excessive load, causing requests to be rate-limited. For a customer-facing product catalog, throttling degrades user experience and can lead to failed searches. Tracking this metric helps you decide when to scale up replicas or partitions to maintain service level agreements.
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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Last reviewed: Jun 24, 2026
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