Update azure-monitor-limits-log-queries.md

Changed the wording of "Concurrent queries"

Author: shemers

includes/azure-monitor-limits-log-queries.md

@@ -24,11 +24,11 @@ Azure Monitor has several throttling limits to protect against users sending an
 
 | Measure | Limit per user | Description |
 |:---|:---|:---|
-| Concurrent queries | 5 | If there are already 5 queries running for the user, any new queries are placed in a per-user concurrency queue. When one of the running queries ends, the next query will be pulled from the queue and started. This does not include queries from alert rules.
+| Concurrent queries | 5 | A user can run up to 5 concurrent queries. Additional queries are added to a queue. When one of the running queries finishes, the first query in the queue is pulled from the queue and starts running. Note: Alerts queries are not part of this limit.
 | Time in concurrency queue | 3 minutes | If a query sits in the queue for more than 3 minutes without being started, it will be terminated with an HTTP error response with code 429. |
 | Total queries in concurrency queue | 200 | Once the number of queries in the queue reaches 200, any additional queries will by rejected with an HTTP error code 429. This number is in addition to the 5 queries that can be running simultaneously. |
 | Query rate | 200 queries per 30 seconds | This is the overall rate that queries can be submitted by a single user to all workspaces.  This limit applies to programmatic queries or queries initiated by visualization parts such as Azure dashboards and the Log Analytics workspace summary page. |
 
 - Optimize your queries as described in [Optimize log queries in Azure Monitor](../articles/azure-monitor/logs/query-optimization.md).
 - Dashboards and workbooks can contain multiple queries in a single view that generate a burst of queries every time they load or refresh. Consider breaking them up into multiple views that load on demand. 
-- In Power BI, consider extracting only aggregated results rather than raw logs.
+- In Power BI, consider extracting only aggregated results rather than raw logs.