Update pre-aggregated-metrics-log-metrics.md

Author: ShawnJackson

articles/azure-monitor/app/pre-aggregated-metrics-log-metrics.md

@@ -16,7 +16,7 @@ In the past, the application monitoring telemetry data model in Application Insi
 
 Using logs to retain a complete set of events can bring great analytical and diagnostic value. For example, you can get an exact count of requests to a particular URL with the number of distinct users who made these calls. Or you can get detailed diagnostic traces, including exceptions and dependency calls for any user session. Having this type of information can improve visibility into the application health and usage. It can also cut down the time necessary to diagnose issues with an app.
 
-At the same time, collecting a complete set of events might be impractical or even impossible for applications that generate a large volume of telemetry. For situations when the volume of events is too high, Application Insights implements several telemetry volume reduction techniques, such as [sampling](./sampling.md) and [filtering](./api-filtering-sampling.md), that reduce the number of collected and stored events. Unfortunately, lowering the number of stored events also lowers the accuracy of the metrics that, behind the scenes, must perform query-time aggregations of the events stored in logs.
+At the same time, collecting a complete set of events might be impractical or even impossible for applications that generate a large volume of telemetry. For situations when the volume of events is too high, Application Insights implements several telemetry volume reduction techniques that reduce the number of collected and stored events. These techniques include [sampling](./sampling.md) and [filtering](./api-filtering-sampling.md). Unfortunately, lowering the number of stored events also lowers the accuracy of the metrics that, behind the scenes, must perform query-time aggregations of the events stored in logs.
 
 > [!NOTE]
 > In Application Insights, the metrics that are based on the query-time aggregation of events and measurements stored in logs are called log-based metrics. These metrics typically have many dimensions from the event properties, which makes them superior for analytics. The accuracy of these metrics is negatively affected by sampling and filtering.