Add blank lines around the ##headings . . .

Author: fbsolo-ms1

articles/machine-learning/apache-spark-environment-configuration.md

@@ -112,6 +112,7 @@ Once the user identity has the appropriate roles assigned, data in the Azure sto
 > If an [attached Synapse Spark pool](./how-to-manage-synapse-spark-pool.md) points to a Synapse Spark pool in an Azure Synapse workspace that has a managed virtual network associated with it, [a managed private endpoint to storage account should be configured](../synapse-analytics/security/connect-to-a-secure-storage-account.md) to ensure data access.
 
 ## Ensuring resource access for Spark jobs
+
 Spark jobs can use either a managed identity or user identity passthrough to access data and other resources. The following table summarizes the different mechanisms for resource access while using Azure Machine Learning Managed (Automatic) Spark compute and attached Synapse Spark pool.
 
 |Spark pool|Supported identities|Default identity|
@@ -122,6 +123,7 @@ Spark jobs can use either a managed identity or user identity passthrough to acc
 If the CLI or SDK code defines an option to use managed identity, Azure Machine Learning Managed (Automatic) Spark compute relies on a user-assigned managed identity attached to the workspace. You can attach a user-assigned managed identity to an existing Azure Machine Learning workspace using Azure Machine Learning CLI v2, or with `ARMClient`.
 
 ## Next steps
+
 - [Apache Spark in Azure Machine Learning (preview)](./apache-spark-azure-ml-concepts.md)
 - [Attach and manage a Synapse Spark pool in Azure Machine Learning (preview)](./how-to-manage-synapse-spark-pool.md)
 - [Interactive Data Wrangling with Apache Spark in Azure Machine Learning (preview)](./interactive-data-wrangling-with-apache-spark-azure-ml.md)

articles/machine-learning/how-to-submit-spark-jobs.md

@@ -58,6 +58,7 @@ These prerequisites cover the submission of a Spark job from Azure Machine Learn
 > To learn more about resource access while using Azure Machine Learning Managed (Automatic) Spark compute, and attached Synapse Spark pool, see [Ensuring resource access for Spark jobs](apache-spark-environment-configuration.md#ensuring-resource-access-for-spark-jobs).
 
 ### Attach user assigned managed identity using CLI v2
+
 1. Create a YAML file that defines the user-assigned managed identity that should be attached to the workspace:
     ```yaml
     identity:
@@ -73,6 +74,7 @@ These prerequisites cover the submission of a Spark job from Azure Machine Learn
     ```
 
 ### Attach user assigned managed identity using `ARMClient`
+
 1. Install [ARMClient](https://github.com/projectkudu/ARMClient), a simple command line tool that invokes the Azure Resource Manager API.
 1. Create a JSON file that defines the user-assigned managed identity that should be attached to the workspace:
     ```json
@@ -139,6 +141,7 @@ The above script takes two arguments `--titanic_data` and `--wrangled_data`, whi
 To create a job, a standalone Spark job can be defined as a YAML specification file, which can be used in the `az ml job create` command, with the `--file` parameter. Define these properties in the YAML file as follows:
 
 ### YAML properties in the Spark job specification
+
 - `type` - set to `spark`.
 - `code` - defines the location of the folder that contains source code and scripts for this job.
 - `entry` - defines the entry point for the job. It should cover one of these properties:
@@ -216,8 +219,9 @@ To create a job, a standalone Spark job can be defined as a YAML specification f
         mode: direct
     ```
 - `identity` - this optional property defines the identity used to submit this job. It can have `user_identity` and `managed` values. If no identity is defined in the YAML specification, the Spark job will use the default identity.
- 
+
 ### Standalone Spark job
+
 This example YAML specification shows a standalone Spark job. It uses an Azure Machine Learning Managed (Automatic) Spark compute:
 
 ```yaml
@@ -393,6 +397,7 @@ ml_client.jobs.stream(returned_spark_job.name)
 # [Studio UI](#tab/ui)
 
 ### Submit a standalone Spark job from Azure Machine Learning studio UI
+
 To submit a standalone Spark job using the Azure Machine Learning studio UI:
 
 :::image type="content" source="media/how-to-submit-spark-jobs/create_standalone_spark_job.png" alt-text="Screenshot showing creation of a new Spark job in Azure Machine Learning studio UI.":::
@@ -479,6 +484,7 @@ To submit a standalone Spark job using the Azure Machine Learning studio UI:
 ---
 
 ## Spark component in a pipeline job
+
 A Spark component offers the flexibility to use the same component in multiple [Azure Machine Learning pipelines](./concept-ml-pipelines.md), as a pipeline step.
 
 # [Azure CLI](#tab/cli)
@@ -688,5 +694,6 @@ This functionality isn't available in the Studio UI. The Studio UI doesn't suppo
 ---
 
 ## Next steps
+
 - [Code samples for Spark jobs using Azure Machine Learning CLI](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/spark)
-- [Code samples for Spark jobs using Azure Machine Learning Python SDK](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/spark)
+- [Code samples for Spark jobs using Azure Machine Learning Python SDK](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/spark)