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Fix detected URL bugs . . .
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articles/machine-learning/tutorial-feature-store-domain-specific-language.md

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Before you proceed with this tutorial, make sure that you cover these prerequisites:
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1. An Azure Machine Learning workspace. If you don't have one, visit [Quickstart: Create workspace resources](https://learn.microsoft.com/azure/machine-learning/quickstart-create-resources?view=azureml-api-2) to learn how to create one.
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1. An Azure Machine Learning workspace. If you don't have one, visit [Quickstart: Create workspace resources](./quickstart-create-resources.md?view-azureml-api-2) to learn how to create one.
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1. To perform the steps in this tutorial, your user account needs either the **Owner** or **Contributor** role to the resource group where the feature store will be created.
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## Set up
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1. You'll see the list of feature stores that you have access to. Select the feature store that you created above
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1. Select the storage account link under **Account name** on the **Offline materialization store** card, to navigate to the ADLS Gen2 storage account for the offline store
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:::image type="content" source="./media/tutorial-feature-store-domain-specific-language/offline-store-link.png" lightbox="./media/tutorial-feature-store-domain-specific-language/offline-store-link.png" alt-text="This screenshot shows the storage account link for the offline materialization store on the feature store UI.":::
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1. Visit [this resource](https://learn.microsoft.com/azure/role-based-access-control/role-assignments-portal) for more information about how to assign the **Storage Blob Data Reader** role to your user identity on the ADLS Gen2 storage account for offline store. Allow some time for permissions to propagate.
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1. Visit [this resource](../role-based-access-control/role-assignments-portal.md) for more information about how to assign the **Storage Blob Data Reader** role to your user identity on the ADLS Gen2 storage account for offline store. Allow some time for permissions to propagate.
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## Available DSL expressions and benchmarks
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1. `features`: List of features and their datatypes. If you provide transformation code, the code must return a dataframe that maps to the features and data types
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1. `index_columns`: The join keys required to access values from the feature set
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For more information, read the [top level feature store entities document](https://learn.microsoft.com/azure/machine-learning/concept-top-level-entities-in-managed-feature-store) and the [feature set specification YAML reference](https://learn.microsoft.com/azure/machine-learning/reference-yaml-featureset-spec) resources.
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For more information, read the [top level feature store entities document](./concept-top-level-entities-in-managed-feature-store.md) and the [feature set specification YAML reference](./reference-yaml-featureset-spec.md) resources.
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As an extra benefit of persisting the feature set specification, it can be source controlled.
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