Update scaling recommendations and diagnostics usage

Clarified recommendations for scaling based on capacity metrics and added guidance for classic tiers regarding data point validity. There is a known issue where the last data point may produce invalid data. Adding clarification for that.

Author: tehnoonr

articles/api-management/api-management-capacity.md

@@ -170,7 +170,8 @@ Use capacity metrics for making decisions whether to scale an API Management ins
 + Ignore sudden spikes that are most likely not related to an increase in load (see [Capacity metric behavior](#capacity-metric-behavior) section for explanation).
 + As a general rule, upgrade or scale your instance when a capacity metric value exceeds **60% - 70%** for a long period of time (for example, 30 minutes). Different values may work better for your service or scenario.
 + If your instance or workspace gateway is configured with only 1 unit, upgrade or scale it when a capacity metric value exceeds **40%** for a long period. This recommendation is based on the need to reserve capacity for guest OS updates in the underlying service platform.
-+ Use [available diagnostics](monitor-api-management.md) to monitor the response times of API calls. Consider adjusting scaling thresholds if you notice degraded response times with increasing value of capacity metric. 
++ Use [available diagnostics](monitor-api-management.md) to monitor the response times of API calls. Consider adjusting scaling thresholds if you notice degraded response times with increasing value of capacity metric.
++ For classic tiers, discard the most recent 1‑minute data point when reading raw Azure API Management capacity metrics because the derived value can be invalid if source data isn’t available at aggregation time; do not base operational or scaling decisions on 1‑minute values — for autoscaling use average aggregation windows of 30 minutes or longer, evaluate sustained conditions before scaling, and annotate dashboards to exclude the final 1‑minute point so trends reflect reliable data.
 
 > [!TIP]  
 > If you are able to estimate your traffic beforehand, test your API Management instance or workspace gateway on workloads you expect. You can increase the request load gradually and monitor the value of the capacity metric that corresponds to your peak load. Follow the steps from the previous section to use Azure portal to understand how much capacity is used at any given time.