Wildcard expansion timeout

Added section that describes what to do if you run into wildcard expansion timeout.

Author: ilijazagorac

articles/synapse-analytics/sql/resources-self-help-sql-on-demand.md

@@ -709,18 +709,18 @@ Describe anything that might be unusual compared to the regular workload. For ex
 
 ### Wildcard expansion timed out
 
-As described in the [Query folders and multiple files](https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/query-folders-multiple-csv-files) section, Serverless SQL pool supports reading multiple files/folders by using wildcards. There is a maximum limit of 10 wildcards per query. You must be aware that  this functionality comes at a cost. It takes time for the serverless pool to list all the files that can match the wildcard. This introduces latency and this latency can increase if the number of files you are trying to query is high. In this case you can run into the following error:
+As described in the [Query folders and multiple files](../sql/query-folders-multiple-csv-files.md) section, Serverless SQL pool supports reading multiple files/folders by using wildcards. There is a maximum limit of 10 wildcards per query. You must be aware that  this functionality comes at a cost. It takes time for the serverless pool to list all the files that can match the wildcard. This introduces latency and this latency can increase if the number of files you are trying to query is high. In this case you can run into the following error:
 
 ```
-"Wildcard expansion timed out after 907 seconds." 
+"Wildcard expansion timed out after X seconds." 
 ```
 
 There are several mitigation steps that you can do to avoid this:
-- Apply best practices described in [Best Practices Serverless SQL Pool](https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-serverless-sql-pool).
+- Apply best practices described in [Best Practices Serverless SQL Pool](../sql/best-practices-serverless-sql-pool.md).
 - Try to reduce the number of files you are trying to query, by compacting files into larger ones.  Try to keep your file sizes above 100MB. 
 - Make sure that filters over partitioning columns are used wherever possible. 
-- If you are using delta file format, use the optimize write feature in Spark.  This can improve the performance of queries by reducing the amount of data that needs to be read and processed. How to use optimize write is described in [Using optimize write on Apache Spark](https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/optimize-write-for-apache-spark). 
-- To avoid some of the top-level wildcards by effectively hardcoding the implicit filters over partitioning columns use [dynamic SQL](https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/develop-dynamic-sql). 
+- If you are using delta file format, use the optimize write feature in Spark.  This can improve the performance of queries by reducing the amount of data that needs to be read and processed. How to use optimize write is described in [Using optimize write on Apache Spark](../spark/optimize-write-for-apache-spark.md). 
+- To avoid some of the top-level wildcards by effectively hardcoding the implicit filters over partitioning columns use [dynamic SQL](../sql/develop-dynamic-sql.md). 
 
 ## Configuration