Merge pull request #219040 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)

Author: huypub

articles/azure-monitor/faq.yml

@@ -385,7 +385,7 @@ sections:
       - question: |
           Does the Azure Monitor Agent support non-Azure environments (other clouds, on-premises)?
         answer: |
-          Both on-premises machines and machines connected to other clouds are supported for servers today, once you have the Azure Arc agent installed. For purposes of running AMA and DCR, the ARC requirement comes at **no extra cost or resource consumption**, since the ARC agent is only used as an installation mechanism and you need not enable the paid management features if you don’t wish to use them.
+          Both on-premises machines and machines connected to other clouds are supported for servers today, once you have the Azure Arc agent installed. For purposes of running AMA and DCR, the Arc requirement comes at **no extra cost or resource consumption**, since the Arc agent is only used as an installation mechanism and you need not enable the paid management features if you don’t wish to use them.
       
       - question: |
           Does the Azure Monitor Agent support private links?
@@ -1229,4 +1229,4 @@ additionalContent: |
   - [Log Analytics](/answers/topics/azure-monitor.html)
   - [Application Insights](/answers/topics/azure-monitor.html)
 
-  For general feedback on Azure Monitor please visit the [feedback forum](https://feedback.azure.com/d365community/forum/79b1327d-d925-ec11-b6e6-000d3a4f06a4).
+  For general feedback on Azure Monitor please visit the [feedback forum](https://feedback.azure.com/d365community/forum/79b1327d-d925-ec11-b6e6-000d3a4f06a4).

articles/key-vault/general/azure-policy.md

@@ -255,7 +255,7 @@ If the compliance results show up as "Not Started" it may be due to the followin
 
 > [!NOTE]
 > Azure Policy
-> [Resouce Provider modes](../../governance/policy/concepts/definition-structure.md#resource-provider-modes),
+> [Resource Provider modes](../../governance/policy/concepts/definition-structure.md#resource-provider-modes),
 > such as those for Azure Key Vault, provide information about compliance on the
 > [Component Compliance](../../governance/policy/how-to/get-compliance-data.md#component-compliance)
 > page.

articles/machine-learning/how-to-train-with-custom-image.md

@@ -101,7 +101,7 @@ fastai_env.docker.base_dockerfile = "./Dockerfile"
 > Azure Machine Learning only supports Docker images that provide the following software:
 > * Ubuntu 18.04 or greater.
 > * Conda 4.7.# or greater.
-> * Python 3.6+.
+> * Python 3.7+.
 > * A POSIX compliant shell available at /bin/sh is required in any container image used for training. 
 
 For more information about creating and managing Azure Machine Learning environments, see [Create and use software environments](how-to-use-environments.md). 

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

@@ -45,7 +45,7 @@ Automated ML supports model training for computer vision tasks like image classi
     * [Install the `automl` package yourself](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/README.md#setup-using-a-local-conda-environment), which includes the [default installation](/python/api/overview/azure/ml/install#default-install) of the SDK.
 
     > [!NOTE]
-    > Only Python 3.6 and 3.7 are compatible with automated ML support for computer vision tasks. 
+    > Only Python 3.7 and 3.8 are compatible with automated ML support for computer vision tasks. 
 
 ## Select your task type
 Automated ML for images supports the following task types:

articles/machine-learning/v1/how-to-deploy-inferencing-gpus.md

@@ -147,7 +147,7 @@ name: project_environment
 dependencies:
   # The Python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- python=3.6.2
+- python=3.7
 
 - pip:
   # You must list azureml-defaults as a pip dependency
@@ -292,4 +292,4 @@ aks_target.delete()
 
 * [Deploy model on FPGA](how-to-deploy-fpga-web-service.md)
 * [Deploy model with ONNX](../concept-onnx.md#deploy-onnx-models-in-azure)
-* [Train TensorFlow DNN Models](../how-to-train-tensorflow.md)
+* [Train TensorFlow DNN Models](../how-to-train-tensorflow.md)

articles/machine-learning/v1/how-to-train-keras.md

@@ -140,7 +140,7 @@ First, define your conda dependencies in a YAML file; in this example the file i
 channels:
 - conda-forge
 dependencies:
-- python=3.6.2
+- python=3.7
 - pip:
   - azureml-defaults
   - tensorflow-gpu==2.0.0

articles/machine-learning/v1/how-to-train-pytorch.md

@@ -167,7 +167,7 @@ First, define your conda dependencies in a YAML file; in this example the file i
 channels:
 - conda-forge
 dependencies:
-- python=3.6.2
+- python=3.7
 - pip=21.3.1
 - pip:
   - azureml-defaults

articles/machine-learning/v1/how-to-train-scikit-learn.md

@@ -86,7 +86,7 @@ You can also create your own custom environment. Define your conda dependencies
 
 ```yaml
 dependencies:
-  - python=3.6.2
+  - python=3.7
   - scikit-learn
   - numpy
   - pip:

articles/machine-learning/v1/how-to-train-tensorflow.md

@@ -176,7 +176,7 @@ First, define your conda dependencies in a YAML file; in this example the file i
 channels:
 - conda-forge
 dependencies:
-- python=3.6.2
+- python=3.7
 - pip:
   - azureml-defaults
   - tensorflow-gpu==2.2.0

articles/machine-learning/v1/tutorial-1st-experiment-sdk-train.md

@@ -164,7 +164,7 @@ First you'll create a file with the package dependencies.
         - defaults
         - pytorch
     dependencies:
-        - python=3.6.2
+        - python=3.7
         - pytorch
         - torchvision
     ```
@@ -357,7 +357,7 @@ channels:
     - defaults
     - pytorch
 dependencies:
-    - python=3.6.2
+    - python=3.7
     - pytorch
     - torchvision
     - pip