Merge pull request #281304 from MicrosoftDocs/repo_sync_working_branch

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Author: Taojunshen

articles/ai-services/openai/whats-new.md

@@ -46,7 +46,7 @@ Refer to our [Default safety policy documentation](./concepts/default-safety-pol
 API version `2024-06-01` is the latest GA data plane inference API release. It replaces API version `2024-02-01` and adds support for:
 
 - embeddings `encoding_format` & `dimensions` parameters.
-- chat completions `logprops` & `top_logprobs` parameters.
+- chat completions `logprobs` & `top_logprobs` parameters.
 
 Refer to our [data plane inference reference documentation](./reference.md) for more information.
 

articles/firewall/tutorial-firewall-dnat.md

@@ -13,7 +13,7 @@ ms.custom: mvc
 
 # Filter inbound Internet traffic with Azure Firewall DNAT using the Azure portal
 
-You can configure Azure Firewall Destination Network Address Translation (DNAT) to translate and filter inbound Internet traffic to your subnets. When you configure DNAT, the NAT rule collection action is set to **Dnat**. Each rule in the NAT rule collection can then be used to translate your firewall public IP address and port to a private IP address and port. DNAT rules implicitly add a corresponding network rule to allow the translated traffic. For security reasons, the recommended approach is to add a specific Internet source to allow DNAT access to the network and avoid using wildcards. To learn more about Azure Firewall rule processing logic, see [Azure Firewall rule processing logic](rule-processing.md).
+You can configure Azure Firewall Destination Network Address Translation (DNAT) to translate and filter inbound Internet traffic to your subnets. When you configure DNAT, the NAT rule collection action is set to **Dnat**. Each rule in the NAT rule collection can then be used to translate your firewall public IP address and port to a private/public IP address and port. DNAT rules implicitly add a corresponding network rule to allow the translated traffic. For security reasons, the recommended approach is to add a specific Internet source to allow DNAT access to the network and avoid using wildcards. To learn more about Azure Firewall rule processing logic, see [Azure Firewall rule processing logic](rule-processing.md).
 
 > [!NOTE]
 > This article uses classic Firewall rules to manage the firewall. The preferred method is to use [Firewall Policy](../firewall-manager/policy-overview.md). To complete this procedure using Firewall Policy, see [Tutorial: Filter inbound Internet traffic with Azure Firewall policy DNAT using the Azure portal](tutorial-firewall-dnat-policy.md)

articles/machine-learning/how-to-use-batch-model-deployments.md

@@ -303,15 +303,15 @@ A model deployment is a set of resources required for hosting the model that doe
     | `environment` | The environment to score the model. The example defines an environment inline using `conda_file` and `image`. The `conda_file` dependencies will be installed on top of the `image`. The environment will be automatically registered with an autogenerated name and version. See the [Environment schema](./reference-yaml-environment.md#yaml-syntax) for more options. As a best practice for production scenarios, you should create the environment separately and reference it here. To reference an existing environment, use the `azureml:<environment-name>:<environment-version>` syntax. |
     | `compute` | The compute to run batch scoring. The example uses the `batch-cluster` created at the beginning and references it using the `azureml:<compute-name>` syntax. |
     | `resources.instance_count` | The number of instances to be used for each batch scoring job. |
-    | `settings.max_concurrency_per_instance` | [Optional] The maximum number of parallel `scoring_script` runs per instance. |
-    | `settings.mini_batch_size` | [Optional] The number of files the `scoring_script` can process in one `run()` call. |
-    | `settings.output_action` | [Optional] How the output should be organized in the output file. `append_row` will merge all `run()` returned output results into one single file named `output_file_name`. `summary_only` won't merge the output results and will only calculate `error_threshold`. |
-    | `settings.output_file_name` | [Optional] The name of the batch scoring output file for `append_row` `output_action`. |
-    | `settings.retry_settings.max_retries` | [Optional] The number of max tries for a failed `scoring_script` `run()`. |
-    | `settings.retry_settings.timeout` | [Optional] The timeout in seconds for a `scoring_script` `run()` for scoring a mini batch. |
-    | `settings.error_threshold` | [Optional] The number of input file scoring failures that should be ignored. If the error count for the entire input goes above this value, the batch scoring job will be terminated. The example uses `-1`, which indicates that any number of failures is allowed without terminating the batch scoring job. |
-    | `settings.logging_level` | [Optional] Log verbosity. Values in increasing verbosity are: WARNING, INFO, and DEBUG. |
-    | `settings.environment_variables` | [Optional] Dictionary of environment variable name-value pairs to set for each batch scoring job.  |
+    | `settings.max_concurrency_per_instance` | The maximum number of parallel `scoring_script` runs per instance. |
+    | `settings.mini_batch_size` | The number of files the `scoring_script` can process in one `run()` call. |
+    | `settings.output_action` | How the output should be organized in the output file. `append_row` will merge all `run()` returned output results into one single file named `output_file_name`. `summary_only` won't merge the output results and will only calculate `error_threshold`. |
+    | `settings.output_file_name` | The name of the batch scoring output file for `append_row` `output_action`. |
+    | `settings.retry_settings.max_retries` | The number of max tries for a failed `scoring_script` `run()`. |
+    | `settings.retry_settings.timeout` | The timeout in seconds for a `scoring_script` `run()` for scoring a mini batch. |
+    | `settings.error_threshold` | The number of input file scoring failures that should be ignored. If the error count for the entire input goes above this value, the batch scoring job will be terminated. The example uses `-1`, which indicates that any number of failures is allowed without terminating the batch scoring job. |
+    | `settings.logging_level` | Log verbosity. Values in increasing verbosity are: WARNING, INFO, and DEBUG. |
+    | `settings.environment_variables` | Dictionary of environment variable name-value pairs to set for each batch scoring job.  |
 
     # [Python](#tab/python)