Update how-to-use-synapsesparkstep.md

Author: lgayhardt

articles/machine-learning/v1/how-to-use-synapsesparkstep.md

@@ -8,7 +8,7 @@ ms.subservice: mldata
 ms.author: zhanxia
 author: xiaoharper
 ms.reviewer: franksolomon
-ms.date: 03/07/2025
+ms.date: 06/03/2025
 ms.topic: how-to
 ms.custom: UpdateFrequency5, synapse-azureml, sdkv1
 #Customer intent: As a user of both Azure Machine Learning pipelines and Azure Synapse Analytics, I'd like to use Apache Spark for the data preparation of my pipeline
@@ -21,7 +21,7 @@ ms.custom: UpdateFrequency5, synapse-azureml, sdkv1
 [!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
 > [!WARNING]
-> The Azure Synapse Analytics integration with Azure Machine Learning, available in Python SDK v1, is deprecated. Users can still use Synapse workspace, registered with Azure Machine Learning, as a linked service. However, a new Synapse workspace can no longer be registered with Azure Machine Learning as a linked service. We recommend use of serverless Spark compute and attached Synapse Spark pools, available in CLI v2 and Python SDK v2. For more information, see  [Configure Apache Spark jobs in Azure Machine Learning](../quickstart-spark-jobs.md).
+> The Azure Synapse Analytics integration with Azure Machine Learning, available in Python SDK v1, is deprecated. Users can still use Synapse workspace, registered with Azure Machine Learning, as a linked service. However, a new Synapse workspace can no longer be registered with Azure Machine Learning as a linked service. We recommend use of serverless Spark compute and attached Synapse Spark pools, available in CLI v2 and Python SDK v2. For more information, see [Configure Apache Spark jobs in Azure Machine Learning](../quickstart-spark-jobs.md).
 
 In this article you learn how to use Apache Spark pools, powered by Azure Synapse Analytics, as the compute target for a data preparation step in an Azure Machine Learning pipeline. You learn how a single pipeline can use compute resources suited for the specific step - for example, data preparation or training. You'll also learn how data is prepared for the Spark step and how it passes to the next step.