fixed the block

Author: v-lanjli

articles/synapse-analytics/monitoring/apache-spark-advisor.md

@@ -1,6 +1,6 @@
 ---
 title: Spark Advisor
-description: Spark Advisor would be a system to automatically analyze commands/queries, and show the appropriate advise when customer execute code or query.
+description: Spark Advisor is a system to automatically analyze commands/queries, and show the appropriate advice when a customer executes code or query.
 services: synapse-analytics 
 author: jejiang
 ms.author: jejiang
@@ -13,19 +13,19 @@ ms.date: 06/23/2022
 
 # Spark Advisor
 
-Spark Advisor would be a system to automatically analyze commands/queries, and show the appropriate advise when customer executes code or query. After applying the advise, you would have chance to improve your execution performance, decrease cost and fix the execution failures.
+Spark Advisor is a system to automatically analyze commands/queries, and show the appropriate advise when customer executes code or query. After applying the advice, you would have chance to improve your execution performance, decrease cost and fix the execution failures.
 
 
-## Advises provided
+## Advice provided
 
-## May return inconsistent results when using 'randomSplit'
+### May return inconsistent results when using 'randomSplit'
 Inconsistent or inaccurate results may be returned when working with the results of the 'randomSplit' method. Use Apache Spark (RDD) caching before using the 'randomSplit' method.
 
-Method randomSplit() is equivalent to performing sample() on your data frame multiple times, with each sample refetching, partitioning, and sorting your data frame within partitions. The data distribution across partitions and sorting order is important for both randomSplit() and sample(). If either change upon data refetch, there may be duplicates, or missing values across splits and the same sample using the same seed may produce different results.
+Method randomSplit() is equivalent to performing sample() on your data frame multiple times, with each sample refetching, partitioning, and sorting your data frame within partitions. The data distribution across partitions and sorting order is important for both randomSplit() and sample(). If either changes upon data refetch, there may be duplicates, or missing values across splits and the same sample using the same seed may produce different results.
 
 These inconsistencies may not happen on every run, but to eliminate them completely, cache your data frame, repartition on a column(s), or apply aggregate functions such as groupBy.
 
-## Table/View Name is already in use
+### Table/view name is already in use
 A view already exists with the same name as the created table, or a table already exists with the same name as the created view. 
 When this name is used in queries or applications, only the view will be returned no matter, which one created first. To avoid conflicts, rename either the table or the view. 
 
@@ -37,7 +37,7 @@ The selected query contains a hint that isn't recognized. Verify that the hint i
 spark.sql("SELECT /*+ unknownHint */ * FROM t1")
 ```
 
-### Unable to find a specified Relation name(s)
+### Unable to find a specified relation name(s)
 Unable to find the relation(s) specified in the hint. Verify that the relation(s) are spelled correctly and accessible within the scope of the hint.
 
 ```scala