Merge branch 'master' of https://github.com/MicrosoftDocs/azure-docs-pr into tamram22-0119

Author: tamram

articles/active-directory/app-provisioning/sap-successfactors-integration-reference.md

@@ -296,6 +296,20 @@ The SuccessFactors connector supports expansion of the position object. To expan
 | positionNameFR | $.employmentNav.results[0].jobInfoNav.results[0].positionNav.externalName_fr_FR |
 | positionNameDE | $.employmentNav.results[0].jobInfoNav.results[0].positionNav.externalName_de_DE |
 
+### Provisioning users in the Onboarding module
+Inbound user provisioning from SAP SuccessFactors to on-premises Active Directory and Azure AD now supports advance provisioning of pre-hires present in the SAP SuccessFactors Onboarding 2.0 module. Upon encountering a new hire profile with future start date, the Azure AD provisioning service queries SAP SuccessFactors to get new hires with one of the following status codes: `active`, `inactive`, `active_external`. The status code `active_external` corresponds to pre-hires present in the SAP SuccessFactors Onboarding 2.0 module. For a description of these status codes, refer to [SAP support note 2736579](https://launchpad.support.sap.com/#/notes/0002736579).
+
+The default behavior of the provisioning service is to process pre-hires in the Onboarding module. 
+
+If you want to exclude processing of pre-hires in the Onboarding module, update your provisioning job configuration as follows: 
+1. Open the attribute-mapping blade of your SuccessFactors provisioning app.
+1. Under show advanced options, edit the SuccessFactors attribute list to add a new attribute called `userStatus`.
+1. Set the JSONPath API expression for this attribute as: `$.employmentNav.results[0].userNav.status`
+1. Save the schema to return back to the attribute mapping blade. 
+1. Edit the Source Object scope to apply a scoping filter `userStatus NOT EQUALS active_external`
+1. Save the mapping and validate that the scoping filter works using provisioning on demand. 
+
+
 ## Writeback scenarios
 
 This section covers different write-back scenarios. It recommends configuration approaches based on how email and phone number is setup in SuccessFactors.

articles/azure-monitor/app/codeless-overview.md

@@ -12,6 +12,10 @@ Auto-instrumentation allows you to enable application monitoring with Applicatio
 
 Application Insights is integrated with various resource providers and works on different environments. In essence, all you have to do is enable and - in some cases - configure the agent, which will collect the telemetry automatically. In no time, you'll see the metrics, requests, and dependencies in your Application Insights resource, which will allow you to spot the source of potential problems before they occur, and analyze the root cause with end-to-end transaction view.
 
+> [!NOTE] 
+> Auto-instrumentation used to be known as "codeless attach" before October 2021.
+
+
 ## Supported environments, languages, and resource providers
 
 As we're adding new integrations, the auto-instrumentation capability matrix becomes complex. The table below shows you the current state of the matter as far as support for various resource providers, languages, and environments go.
@@ -46,7 +50,7 @@ For [Python](./opencensus-python.md), use the SDK.
 
 ## Azure Functions
 
-The basic monitoring for Azure Functions is enabled by default to collects log, performance, error data, and HTTP requests. For Java applications, you can enable richer monitoring with distributed tracing and get the end-to-end transaction details. This functionality for Java is in public preview for Windows and you can [enable it in Azure portal](./monitor-functions.md).
+The basic monitoring for Azure Functions is enabled by default to collect log, performance, error data, and HTTP requests. For Java applications, you can enable richer monitoring with distributed tracing and get the end-to-end transaction details. This functionality for Java is in public preview for Windows and you can [enable it in Azure portal](./monitor-functions.md).
 
 ## Azure Spring Cloud
 

articles/azure-netapp-files/monitor-azure-netapp-files.md

@@ -12,7 +12,7 @@ ms.service: azure-netapp-files
 ms.workload: storage
 ms.tgt_pltfrm: na
 ms.topic: conceptual
-ms.date: 01/06/2022
+ms.date: 01/24/2022
 ms.author: anfdocs
 ---
 # Ways to monitor Azure NetApp Files
@@ -29,15 +29,21 @@ For Activity log warnings for Azure NetApp Files volumes, see [Activity log warn
 
 ## Azure NetApp Files metrics 
 
-Azure NetApp Files provides metrics on allocated storage, actual storage usage, volume IOPS, and latency. By analyzing these metrics, you can gain a better understanding on the usage pattern and volume performance of your NetApp accounts.
+Azure NetApp Files provides metrics on allocated storage, actual storage usage, volume IOPS, and latency. With these metrics, you can gain a better understanding on the usage pattern and volume performance of your NetApp accounts.
 
 You can find metrics for a capacity pool or volume by selecting the **capacity pool** or **volume**. Then click **Metric** to view the available metrics.  
 
 For more information about Azure NetApp Files metrics, see [Metrics for Azure NetApp Files](azure-netapp-files-metrics.md).
 
+## Azure Service Health
+
+The [Azure Service Health dashboard](https://azure.microsoft.com/features/service-health) keeps you informed about the health of your environment. It provides a personalized view of the status of your Azure services in the regions where they are used. The dashboard provides upcoming planned maintenance and relevant health advisories while allowing you to manage service health alerts.
+
+For more information, see [Azure Service Health dashboard](../service-health/service-health-overview.md) documentation. 
+
 ## Capacity utilization monitoring 
 
-It's important to monitor capacity regularly.  You can monitor capacity utilization at the VM level.  You can check the used and available capacity of a volume by using Windows or Linux clients. You can also configure alerts by using `ANFCapacityManager`.  
+It is important to monitor capacity regularly. You can monitor capacity utilization at the VM level.  You can check the used and available capacity of a volume by using Windows or Linux clients. You can also configure alerts by using `ANFCapacityManager`.  
 
 For more information, see [Monitor capacity utilization](volume-hard-quota-guidelines.md#how-to-operationalize-the-volume-hard-quota-change).
 

articles/azure-resource-manager/bicep/bicep-functions-array.md

@@ -746,7 +746,7 @@ The output from the preceding example with the default values is:
 
 `union(arg1, arg2, arg3, ...)`
 
-Returns a single array or object with all elements from the parameters. Duplicate values or keys are only included once.
+Returns a single array or object with all elements from the parameters. For arrays, duplicate values are included once. For objects, duplicate property names are only included once.
 
 Namespace: [sys](bicep-functions.md#namespaces-for-functions).
 
@@ -762,6 +762,14 @@ Namespace: [sys](bicep-functions.md#namespaces-for-functions).
 
 An array or object.
 
+### Remarks
+
+The union function uses the sequence of the parameters to determine the order and values of the result.
+
+For arrays, the function iterates through each element in the first parameter and adds it to the result if it isn't already present. Then, it repeats the process for the second parameter and any additional parameters. If a value is already present, it's earlier placement in the array is preserved.
+
+For objects, property names and values from the first parameter are added to the result. For later parameters, any new names are added to the result. If a later parameter has a property with the same name, that value overwrites the existing value. The order of the properties isn't guaranteed.
+
 ### Example
 
 The following example shows how to use union with arrays and objects:
@@ -788,6 +796,7 @@ param firstArray array = [
 param secondArray array = [
   'three'
   'four'
+  'two'
 ]
 
 output objectOutput object = union(firstObject, secondObject)

articles/azure-resource-manager/bicep/bicep-functions-object.md

@@ -276,7 +276,7 @@ The output from the preceding example with the default values is:
 
 `union(arg1, arg2, arg3, ...)`
 
-Returns a single array or object with all elements from the parameters. Duplicate values or keys are only included once.
+Returns a single array or object with all elements from the parameters. For arrays, duplicate values are included once. For objects, duplicate property names are only included once.
 
 Namespace: [sys](bicep-functions.md#namespaces-for-functions).
 
@@ -292,6 +292,14 @@ Namespace: [sys](bicep-functions.md#namespaces-for-functions).
 
 An array or object.
 
+### Remarks
+
+The union function uses the sequence of the parameters to determine the order and values of the result.
+
+For arrays, the function iterates through each element in the first parameter and adds it to the result if it isn't already present. Then, it repeats the process for the second parameter and any additional parameters. If a value is already present, it's earlier placement in the array is preserved.
+
+For objects, property names and values from the first parameter are added to the result. For later parameters, any new names are added to the result. If a later parameter has a property with the same name, that value overwrites the existing value. The order of the properties isn't guaranteed.
+
 ### Example
 
 The following example shows how to use union with arrays and objects:
@@ -318,6 +326,7 @@ param firstArray array = [
 param secondArray array = [
   'three'
   'four'
+  'two'
 ]
 
 output objectOutput object = union(firstObject, secondObject)

articles/azure-resource-manager/templates/template-functions-array.md

@@ -475,7 +475,7 @@ The output from the preceding example with the default values is:
 
 `union(arg1, arg2, arg3, ...)`
 
-Returns a single array or object with all elements from the parameters. Duplicate values or keys are only included once.
+Returns a single array or object with all elements from the parameters. For arrays, duplicate values are included once. For objects, duplicate property names are only included once.
 
 ### Parameters
 
@@ -489,6 +489,14 @@ Returns a single array or object with all elements from the parameters. Duplicat
 
 An array or object.
 
+### Remarks
+
+The union function uses the sequence of the parameters to determine the order and values of the result.
+
+For arrays, the function iterates through each element in the first parameter and adds it to the result if it isn't already present. Then, it repeats the process for the second parameter and any additional parameters. If a value is already present, it's earlier placement in the array is preserved.
+
+For objects, property names and values from the first parameter are added to the result. For later parameters, any new names are added to the result. If a later parameter has a property with the same name, that value overwrites the existing value. The order of the properties isn't guaranteed.
+
 ### Example
 
 The following example shows how to use union with arrays and objects.

articles/azure-resource-manager/templates/template-functions-object.md

@@ -258,7 +258,7 @@ The output from the preceding example is:
 
 `union(arg1, arg2, arg3, ...)`
 
-Returns a single array or object with all elements from the parameters. Duplicate values or keys are only included once.
+Returns a single array or object with all elements from the parameters. For arrays, duplicate values are included once. For objects, duplicate property names are only included once.
 
 ### Parameters
 
@@ -272,6 +272,14 @@ Returns a single array or object with all elements from the parameters. Duplicat
 
 An array or object.
 
+### Remarks
+
+The union function uses the sequence of the parameters to determine the order and values of the result.
+
+For arrays, the function iterates through each element in the first parameter and adds it to the result if it isn't already present. Then, it repeats the process for the second parameter and any additional parameters. If a value is already present, it's earlier placement in the array is preserved.
+
+For objects, property names and values from the first parameter are added to the result. For later parameters, any new names are added to the result. If a later parameter has a property with the same name, that value overwrites the existing value. The order of the properties isn't guaranteed.
+
 ### Example
 
 The following example shows how to use union with arrays and objects:

articles/databox/data-box-limits.md

@@ -7,7 +7,7 @@ author: alkohli
 ms.service: databox
 ms.subservice: pod
 ms.topic: article
-ms.date: 01/13/2022
+ms.date: 01/21/2022
 ms.author: alkohli
 ---
 # Azure Data Box limits

articles/ddos-protection/inline-protection-glb.md

@@ -46,6 +46,6 @@ Enabling Azure DDoS Protection Standard on the VNet of the Standard Public Load
 5.	Azure DDoS Protection Standard on the gamer servers Load Balancer protects from L3/4 DDoS attacks and the DDoS protection policies are automatically tuned for game servers traffic profile and application scale. 
 
 ## Next steps
-- Learn more about [inline L7 DDoS protection partners](https://aka.ms/inlineddospartners)
+- Learn more about our launch partner [A10 Networks](https://www.a10networks.com/blog/introducing-l3-7-ddos-protection-for-microsoft-azure-tenants/)
 - Learn more about [Azure DDoS Protection Standard](./ddos-protection-overview.md)
 - Learn more about [Gateway Load Balancer](../load-balancer/gateway-overview.md)

articles/machine-learning/how-to-configure-auto-features.md

@@ -10,7 +10,7 @@ ms.service: machine-learning
 ms.subservice: automl
 ms.topic: how-to
 ms.custom: automl,contperf-fy21q2
-ms.date: 10/21/2021
+ms.date: 01/24/2022
 ---
 
 # Data featurization in automated machine learning
@@ -60,7 +60,7 @@ The following table shows the accepted settings for `featurization` in the [Auto
 
 ## Automatic featurization
 
-The following table summarizes techniques that are automatically applied to your data. These techniques are applied for experiments that are configured by using the SDK or the studio. To disable this behavior, set `"featurization": 'off'` in your `AutoMLConfig` object.
+The following table summarizes techniques that are automatically applied to your data. These techniques are applied for experiments that are configured by using the SDK or the studio UI. To disable this behavior, set `"featurization": 'off'` in your `AutoMLConfig` object.
 
 > [!NOTE]
 > If you plan to export your AutoML-created models to an [ONNX model](concept-onnx.md), only the featurization options indicated with an asterisk ("*") are supported in the ONNX format. Learn more about [converting models to ONNX](how-to-use-automl-onnx-model-dotnet.md).
@@ -74,6 +74,18 @@ The following table summarizes techniques that are automatically applied to your
 |**Word embeddings**|A text featurizer converts vectors of text tokens into sentence vectors by using a pre-trained model. Each word's embedding vector in a document is aggregated with the rest to produce a document feature vector.|
 |**Cluster Distance**|Trains a k-means clustering model on all numeric columns. Produces *k* new features (one new numeric feature per cluster) that contain the distance of each sample to the centroid of each cluster.|
 
+In every automated machine learning experiment, your data is automatically scaled or normalized to help algorithms perform well. During model training, one of the following scaling or normalization techniques are applied to each model. 
+
+|Scaling & processing| Description |
+| ------------- | ------------- |
+| [StandardScaleWrapper](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)  | Standardize features by removing the mean and scaling to unit variance  |
+| [MinMaxScalar](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)  | Transforms features by scaling each feature by that column's minimum and maximum  |
+| [MaxAbsScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler) |Scale each feature by its maximum absolute value |
+| [RobustScalar](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html) | Scales features by their quantile range |
+| [PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) |Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space |
+| [TruncatedSVDWrapper](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html) |This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA, this estimator does not center the data before computing the singular value decomposition, which means it can work with scipy.sparse matrices efficiently |
+| [SparseNormalizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html) | Each sample (that is, each row of the data matrix) with at least one non-zero component is rescaled independently of other samples so that its norm (l1 or l2) equals one |
+
 ## Data guardrails
 
 *Data guardrails* help you identify potential issues with your data (for example, missing values or [class imbalance](concept-manage-ml-pitfalls.md#identify-models-with-imbalanced-data)). They also help you take corrective actions for improved results.