Update articles/machine-learning/reference-yaml-job-sweep.md

Co-authored-by: s-polly <[email protected]>

Author: cloga

articles/machine-learning/reference-yaml-job-sweep.md

@@ -205,7 +205,7 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 
 | Key | Type | Description | Allowed values | Default value |
 | --- | ---- | ----------- | -------------- | ------------- |
-| `type` | string | The type of job input. Specify `uri_file` for input data that points to a single file source, or `uri_folder` for input data that points to a folder source. [Learning more about data access.](concept-data.md)| `uri_file`, `uri_folder`, `mltable`, `mlflow_model` | `uri_folder` |
+| `type` | string | The type of job input. Specify `uri_file` for input data that points to a single file source, or `uri_folder` for input data that points to a folder source. [Learn more about data access.](concept-data.md)| `uri_file`, `uri_folder`, `mltable`, `mlflow_model` | `uri_folder` |
 | `path` | string | The path to the data to use as input. This can be specified in a few ways: <br><br> - A local path to the data source file or folder, e.g. `path: ./iris.csv`. The data will get uploaded during job submission. <br><br> - A URI of a cloud path to the file or folder to use as the input. Supported URI types are `azureml`, `https`, `wasbs`, `abfss`, `adl`. See [Core yaml syntax](reference-yaml-core-syntax.md) for more information on how to use the `azureml://` URI format. <br><br> - An existing registered Azure ML data asset to use as the input. To reference a registered data asset use the `azureml:<data_name>:<data_version>` syntax or `azureml:<data_name>@latest` (to reference the latest version of that data asset), e.g. `path: azureml:cifar10-data:1` or `path: azureml:cifar10-data@latest`. | | |
 | `mode` | string | Mode of how the data should be delivered to the compute target. <br><br> For read-only mount (`ro_mount`), the data will be consumed as a mount path. A folder will be mounted as a folder and a file will be mounted as a file. Azure ML will resolve the input to the mount path. <br><br> For `download` mode the data will be downloaded to the compute target. Azure ML wil resolve the input to the downloaded path. <br><br> If you only want the URL of the storage location of the data artifact(s) rather than mounting or downloading the data itself, you can use the `direct` mode. This will pass in the URL of the storage location as the job input. Note that in this case you are fully responsible for handling credentials to access the storage. | `ro_mount`, `download`, `direct` | `ro_mount` |