AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Your company uses Azure AI Search to power a customer support portal. The search index includes product documentation and known issues. Recently, the portal's search performance has degraded, and users report slow response times. You need to identify the cause of the performance issue. What should you check first?
⚠ Common exam trap
It's easy for candidates to confuse indexing-related metrics (like skillset execution or index size) with query performance metrics, leading them to check storage size or schema complexity instead of the direct performance indicators of query latency and CPU usage.
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
✓
Review the search service metrics for high query latency and CPU usage.
High query latency and CPU usage are direct indicators of performance bottlenecks in Azure AI Search. The search service metrics in the Azure portal provide real-time data on query execution time and resource consumption, which are the first signals to investigate when users report slow response times. Checking these metrics helps identify whether the issue stems from excessive query load, insufficient replicas, or inefficient query execution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Review the search service metrics for high query latency and CPU usage.
Why this is correct
Reviewing service metrics exposes query latency and CPU saturation, the primary indicators of degraded Azure AI Search performance. High CPU usage or elevated query latency directly satisfies the stem's requirement to identify the cause first, since these metrics reveal whether throttling, expensive queries, or insufficient replicas are driving the slow response times.
- ✗
Check the size of the index storage in the Azure portal.
Why it's wrong here
Storage size alone does not cause query latency; document count and partition size matter, and the portal previously performed well. It is tempting because capacity limits are a common search concern, yet checking storage is correct when planning scale, not diagnosing a sudden slowdown.
- ✗
Ensure the index schema does not have too many fields.
Why it's wrong here
Field count affects index size and query latency only marginally; degraded performance after running well points to query or resource causes, not schema design. Checking schema is tempting when designing a new index, but it is not the first diagnostic for a sudden slowdown.
- ✗
Verify that the skillset is not running during peak hours.
Why it's wrong here
Skillsets run at indexing time, not during query serving, so they cannot slow portal searches unless indexing overlaps. It is tempting because AI enrichment consumes resources, and scheduling it off-peak is correct for indexer load, but query latency stems from query-side factors.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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