fixed links

Author: HeidiSteen

articles/search/monitor-azure-cognitive-search.md

@@ -109,8 +109,6 @@ The following table lists common and recommended alert rules for Azure AI Search
 - [Monitor Azure resources with Azure Monitor](/azure/azure-monitor/essentials/monitor-azure-resource)
 - [Monitor queries](search-monitor-queries.md)
 - [Monitor indexer-based indexing](search-howto-monitor-indexers.md)
-- [Monitor client-side interactions](search-traffic-analytics.md)
 - [Visualize resource logs](search-monitor-logs-powerbi.md)
 - [Analyze performance in Azure AI Search](search-performance-analysis.md)
-- [Performance benchmarks](performance-benchmarks.md)
 - [Tips for better performance](search-performance-tips.md)

articles/search/search-performance-analysis.md

@@ -17,8 +17,6 @@ This article describes the tools, behaviors, and approaches for analyzing query
 
 In any large implementation, it's critical to do a performance benchmarking test of your Azure AI Search service before you roll it into production. You should test both the search query load that you expect, but also the expected data ingestion workloads (if possible, run both workloads simultaneously). Having benchmark numbers helps to validate the proper [search tier](search-sku-tier.md), [service configuration](search-capacity-planning.md), and expected [query latency](search-performance-analysis.md#average-query-latency).
 
-<!-- To develop benchmarks, we recommend the [azure-search-performance-testing (GitHub)](https://github.com/Azure-Samples/azure-search-performance-testing) tool. -->
-
 To isolate the effects of a distributed service architecture, try testing on service configurations of one replica and one partition.
 
 > [!NOTE]