refresh + update external section refs

Author: GitHubber17

articles/machine-learning/concept-automl-forecasting-sweeping.md

@@ -66,7 +66,7 @@ AutoML follows the usual cross-validation procedure, training a separate model o
 
 Cross-validation for forecasting jobs is configured by setting the number of cross-validation folds, and optionally, the number of time periods between two consecutive cross-validation folds. For more information and an example of configuring cross-validation for forecasting, see [Custom cross-validation settings](how-to-auto-train-forecast.md#custom-cross-validation-settings).
 
-You can also bring your own validation data. For more information, see [Configure training, validation, cross-validation, and test data in AutoML (SDK v1)](./v1/how-to-configure-cross-validation-data-splits.md#provide-validation-data).
+You can also bring your own validation data. For more information, see [Configure training, validation, cross-validation, and test data in AutoML (SDK v1)](./v1/how-to-configure-cross-validation-data-splits.md#provide-validation-dataset).
 
 ## Related content
 

articles/machine-learning/v1/concept-automated-ml.md

@@ -253,7 +253,7 @@ To help confirm that such bias isn't applied to the final recommended model, aut
 >[!IMPORTANT]
 > Testing your models with a test dataset to evaluate generated models is a preview feature. This capability is an [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview feature, and may change at any time.
 
-Learn how to [configure AutoML experiments to use test data (preview) with the SDK (v1)](how-to-configure-cross-validation-data-splits.md#provide-test-data-preview) or with the [Azure Machine Learning studio](../how-to-use-automated-ml-for-ml-models.md#create-and-run-experiment).
+Learn how to [configure AutoML experiments to use test data (preview) with the SDK (v1)](how-to-configure-cross-validation-data-splits.md#provide-test-dataset-preview) or with the [Azure Machine Learning studio](../how-to-use-automated-ml-for-ml-models.md#create-and-run-experiment).
 
 You can also [test any existing automated ML model (preview) (v1)](../how-to-configure-auto-train.md)), including models from child jobs, by providing your own test data or by setting aside a portion of your training data. 
 

articles/machine-learning/v1/how-to-auto-train-forecast.md

@@ -69,7 +69,7 @@ automl_config = AutoMLConfig(task='forecasting',
 ```
 
 
-You can also bring your own validation data, learn more in [Configure data splits and cross-validation in AutoML](how-to-configure-cross-validation-data-splits.md#provide-validation-data).
+You can also bring your own validation data, learn more in [Configure data splits and cross-validation in AutoML](how-to-configure-cross-validation-data-splits.md#provide-validation-dataset).
 
 Learn more about how AutoML applies cross validation to [prevent over-fitting models](../concept-manage-ml-pitfalls.md#prevent-overfitting).
 

articles/machine-learning/v1/how-to-configure-auto-train.md

@@ -109,7 +109,7 @@ If you do not explicitly specify a `validation_data` or `n_cross_validation` par
 > [!TIP] 
 > You can upload **test data (preview)** to evaluate models that automated ML generated for you. These features are  [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview capabilities, and may change at any time.
 > Learn how to: 
-> * [Pass in test data to your AutoMLConfig object](how-to-configure-cross-validation-data-splits.md#provide-test-data-preview). 
+> * [Pass in test data to your AutoMLConfig object](how-to-configure-cross-validation-data-splits.md#provide-test-dataset-preview). 
 > * [Test the models automated ML generated for your experiment](#test-models-preview).
 >  
 > If you prefer a no-code experience, see [step 12 in Set up AutoML with the studio UI](../how-to-use-automated-ml-for-ml-models.md#create-and-run-experiment)
@@ -449,7 +449,7 @@ RunDetails(run).show()
 >  * [Forecasting tasks where deep learning neural networks (DNN) are enabled](../how-to-auto-train-forecast.md#enable-deep-learning)
 >  * [Automated ML runs from local computes or Azure Databricks clusters](../how-to-configure-auto-train.md#compute-to-run-experiment)
 
-Passing the `test_data` or `test_size` parameters into the `AutoMLConfig`, automatically triggers a remote test run that uses the provided test data to evaluate the best model that automated ML recommends upon completion of the experiment. This remote test run is done at the end of the experiment, once the best model is determined. See how to [pass test data into your `AutoMLConfig`](how-to-configure-cross-validation-data-splits.md#provide-test-data-preview). 
+Passing the `test_data` or `test_size` parameters into the `AutoMLConfig`, automatically triggers a remote test run that uses the provided test data to evaluate the best model that automated ML recommends upon completion of the experiment. This remote test run is done at the end of the experiment, once the best model is determined. See how to [pass test data into your `AutoMLConfig`](how-to-configure-cross-validation-data-splits.md#provide-test-dataset-preview). 
 
 ### Get test job results