fixing errors

Author: Larry Franks

articles/machine-learning/concept-datastore.md

@@ -48,4 +48,4 @@ Azure Machine Learning Datastores support both credential-based and identity-bas
 
 ## Next steps 
 
-+ [How to create a datastore](how-to-datasore.md)
++ [How to create a datastore](how-to-datastore.md)

articles/machine-learning/how-to-administrate-data-authentication.md

@@ -5,7 +5,7 @@ description: Learn how to manage data access and how to anthenticate in Azure Ma
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: enterprise-readiness
-ms.topic: how-do
+ms.topic: how-to
 ms.author: xunwan
 author: xunwan
 ms.reviewer: larryfr

articles/machine-learning/how-to-create-register-data-assets.md

@@ -166,10 +166,10 @@ description: Data asset created from folder in cloud using wasbs URL.
 type: uri_folder
 path: wasbs://mainstorage9c05dabf5c924.blob.core.windows.net/azureml-blobstore-54887b46-3cb0-485b-bb15-62e7b5578ee6/example-data/
 ```
-
+---
 
 ### Consume registered URI Folder data assets in job
-# [Python-SDK](#tab/Python-SDK)
+
 ```python
 from azure.ai.ml.entities import Data, UriReference, JobInput, CommandJob
 from azure.ai.ml._constants import AssetTypes
@@ -198,7 +198,7 @@ returned_job.services["Studio"].endpoint
 ```
 
 ### Register data as URI File type Data
-
+# [Python-SDK](#tab/Python-SDK)
 ```python
 from azure.ai.ml.entities import Data
 from azure.ai.ml._constants import AssetTypes
@@ -218,9 +218,9 @@ my_data = Data(
 
 ml_client.data.create_or_update(my_data)
 ```
-
-You can also use CLI to register a URI File type Data as below example.
 # [CLI](#tab/CLI)
+You can also use CLI to register a URI File type Data as below example.
+
 ```cli
 > az ml data create -f <file-name>.yml
 ```
@@ -249,7 +249,7 @@ description: Data asset created from folder in cloud using wasbs URL.
 type: uri_file
 path: wasbs://mainstorage9c05dabf5c924.blob.core.windows.net/azureml-blobstore-54887b46-3cb0-485b-bb15-62e7b5578ee6/paths/example-data.csv
 ```
-
+---
 
 ## MLTable
 
@@ -302,7 +302,7 @@ transformations:
       empty_as_string: false
       header: from_first_file
 ```
-
+---
 The contents of the MLTable file specify the underlying data location (here a local path) and also the transforms to perform on the underlying data before materializing into a pandas/spark/dask data frame. The important part here's that the MLTable-artifact doesn't have any absolute paths, making it *self-contained*. All the information stored in one folder; regardless of whether that folder is stored on your local drive or in your cloud drive or on a public http server.
 
 To consume the data in a job or interactive session, use `mltable`:

articles/machine-learning/how-to-datastore.md

@@ -396,7 +396,7 @@ store = AzureDataLakeGen1Datastore(
 ml_client.create_or_update(store)
 ```
 
-# [Python SDK: Service Principal](#tab/sdk-adls-sp)
+# [Python SDK: Service Principal](#tab/sdk-adlsgen1-sp)
 
 ```python
 from azure.ai.ml.entities import AzureDataLakeGen1Datastore
@@ -422,6 +422,7 @@ store = AzureDataLakeGen1Datastore(
 
 ml_client.create_or_update(store)
 ```
+
 ---
 
 ## Next steps

articles/machine-learning/v1/how-to-identity-based-data-access.md

@@ -29,7 +29,7 @@ To create datastores that use **credential-based** authentication, like access k
 There are two scenarios in which you can apply identity-based data access in Azure Machine Learning. These scenarios are a good fit for identity-based access when you're working with confidential data and need more granular data access management:
 
 > [!WARNING]
-> Identity-based data access is not supported for [automated ML experiments](how-to-configure-auto-train.md).
+> Identity-based data access is not supported for [automated ML experiments](../how-to-configure-auto-train.md).
 
 - Accessing storage services
 - Training machine learning models with private data
@@ -246,5 +246,5 @@ identity:
 ## Next steps
 
 * [Create an Azure Machine Learning dataset](how-to-create-register-datasets.md)
-* [Train with datasets](..how-to-train-with-datasets.md)
+* [Train with datasets](../how-to-train-with-datasets.md)
 * [Create a datastore with key-based data access](how-to-access-data.md)