Merge pull request #116247 from euangms/euang-scalefix

Clarify scale behavior

Author: PRMerger10

articles/synapse-analytics/spark/apache-spark-overview.md

@@ -36,7 +36,7 @@ Spark pools in Azure Synapse offer a fully managed Spark service. The benefits o
 | Support for Azure Data Lake Storage Generation 2| Spark pools in Azure Synapse can use Azure Data Lake Storage Generation 2 as well as BLOB storage. For more information on Data Lake Storage, see [Overview of Azure Data Lake Storage](../../data-lake-store/data-lake-store-overview.md). |
 | Integration with third-party IDEs | Azure Synapse provides an IDE plugin for [Jetbrains' IntelliJ IDEA](https://www.jetbrains.com/idea/) that is useful to create and submit applications to a Spark pool. |
 | Pre-loaded Anaconda libraries |Spark pools in Azure Synapse come with Anaconda libraries pre-installed. [Anaconda](https://docs.continuum.io/anaconda/) provides close to 200 libraries for machine learning, data analysis, visualization, etc. |
-| Scalability | Apache Spark in Azure Synapse pools can have Auto-Scale enabled, so that pools scale up and down as needed. Also, Spark pools can be shut down with no loss of data since all the data is stored in Azure Storage or Data Lake Storage. |
+| Scalability | Apache Spark in Azure Synapse pools can have Auto-Scale enabled, so that pools scale by adding or removing nodes as needed. Also, Spark pools can be shut down with no loss of data since all the data is stored in Azure Storage or Data Lake Storage. |
 
 Spark pools in Azure Synapse include the following components that are available on the pools by default.