Merge pull request #79466 from MicrosoftDocs/repo_sync_working_branch

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

Author: huypub

articles/governance/policy/how-to/remediate-resources.md

@@ -60,10 +60,6 @@ the role. To get the ID for the 'Contributor' role in your environment, use the
 az role definition list --name 'Contributor'
 ```
 
-```azurepowershell-interactive
-Get-AzRoleDefinition -Name 'Contributor'
-```
-
 ## Manually configure the managed identity
 
 When creating an assignment using the portal, Azure Policy both generates the managed identity and
@@ -251,4 +247,4 @@ module.
 - Review [Understanding policy effects](../concepts/effects.md).
 - Understand how to [programmatically create policies](programmatically-create.md).
 - Learn how to [get compliance data](getting-compliance-data.md).
-- Review what a management group is with [Organize your resources with Azure management groups](../../management-groups/overview.md).
+- Review what a management group is with [Organize your resources with Azure management groups](../../management-groups/overview.md).

articles/machine-learning/service/how-to-troubleshoot-deployment.md

@@ -58,7 +58,7 @@ Breaking the deployment into tasks is helpful if you are using the [Webservice.d
     ```python
     # configure the image
     image_config = ContainerImage.image_configuration(runtime="python",
-                                                      execution_script="score.py",
+                                                      entry_script="score.py",
                                                       conda_file="myenv.yml")
 
     # create the image

articles/mysql/concepts-query-store.md

@@ -61,7 +61,7 @@ The following query returns information about queries in Query Store:
 SELECT * FROM mysql.query_store;
 ```
 
-Or this query for wait wait statistics:
+Or this query for wait statistics:
 
 ```sql
 SELECT * FROM mysql.query_store_wait_stats;

articles/virtual-machines/linux/sizes.md

@@ -27,7 +27,7 @@ This article describes the available sizes and options for the Azure virtual mac
 | [General purpose](sizes-general.md)          | B, Dsv3, Dv3, DSv2, Dv2, Av2, DC  | Balanced CPU-to-memory ratio. Ideal for testing and development, small to medium databases, and low to medium traffic web servers. |
 | [Compute optimized](sizes-compute.md)        | Fsv2           | High CPU-to-memory ratio. Good for medium traffic web servers, network appliances, batch processes, and application servers.        |
 | [Memory optimized](sizes-memory.md)         | Esv3, Ev3, M, DSv2, Dv2  | High memory-to-CPU ratio. Great for relational database servers, medium to large caches, and in-memory analytics.                 |
-| [Storage optimized](sizes-storage.md)        | Lsv2, Ls                | High disk throughput and IO ideal for Big Data, SQL, NoSQL databases, data warehousing and large transactional databases.  |
+| [Storage optimized](sizes-storage.md)        | Lsv2                | High disk throughput and IO ideal for Big Data, SQL, NoSQL databases, data warehousing and large transactional databases.  |
 | [GPU](sizes-gpu.md)            | NV, NVv2, NC, NCv2, NCv3, ND, NDv2 (Preview)            | Specialized virtual machines targeted for heavy graphic rendering and video editing, as well as model training and inferencing (ND) with deep learning. Available with single or multiple GPUs.       |
 | [High performance compute](sizes-hpc.md) | H       | Our fastest and most powerful CPU virtual machines with optional high-throughput network interfaces (RDMA). |
 

articles/virtual-machines/windows/sizes.md

@@ -28,7 +28,7 @@ This article describes the available sizes and options for the Azure virtual mac
 | [General purpose](sizes-general.md)          | B, Dsv3, Dv3, DSv2, Dv2, Av2, DC | Balanced CPU-to-memory ratio. Ideal for testing and development, small to medium databases, and low to medium traffic web servers. |
 | [Compute optimized](sizes-compute.md)        | Fsv2          | High CPU-to-memory ratio. Good for medium traffic web servers, network appliances, batch processes, and application servers.        |
 | [Memory optimized](../virtual-machines-windows-sizes-memory.md)         | Esv3, Ev3, M, DSv2, Dv2  | High memory-to-CPU ratio. Great for relational database servers, medium to large caches, and in-memory analytics.                 |
-| [Storage optimized](../virtual-machines-windows-sizes-storage.md)        | Lsv2, Ls                | High disk throughput and IO ideal for Big Data, SQL, NoSQL databases, data warehousing and large transactional databases.  |
+| [Storage optimized](../virtual-machines-windows-sizes-storage.md)        | Lsv2                | High disk throughput and IO ideal for Big Data, SQL, NoSQL databases, data warehousing and large transactional databases.  |
 | [GPU](sizes-gpu.md)            | NV, NVv2, NC, NCv2, NCv3, ND, NDv2 (Preview)            | Specialized virtual machines targeted for heavy graphic rendering and video editing, as well as model training and inferencing (ND) with deep learning. Available with single or multiple GPUs.       |
 | [High performance compute](sizes-hpc.md) | H       | Our fastest and most powerful CPU virtual machines with optional high-throughput network interfaces (RDMA). |