Merge pull request #927 from MicrosoftDocs/repo_sync_working_branch

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

Author: Taojunshen

articles/ai-services/language-service/question-answering/tutorials/active-learning.md

@@ -20,9 +20,9 @@ In this tutorial, you learn how to:
 > * Accept/reject active learning suggestions
 > * Add alternate questions
 
-This tutorial shows you how to enhance your custom question answering project with active learning. If you notice that customers are asking questions, which are not part of your project. There are often variations of questions that are paraphrased differently.
+This tutorial shows you how to enhance your custom question answering project with active learning. If you notice that customers are asking questions that are not covered in your project, they may be paraphrased variations of questions.
 
-These variations when added as alternate questions to the relevant question answer pair, help to optimize the project to answer real world user queries. You can manually add alternate questions to question answer pairs through the editor. At the same time, you can also use the active learning feature to generate active learning suggestions based on user queries. The active learning feature, however, requires that the project receives regular user traffic to generate suggestions.
+These variations, when added as alternate questions to the relevant question answer pair, help to optimize the project to answer real world user queries. You can manually add alternate questions to question answer pairs through the editor. At the same time, you can also use the active learning feature to generate active learning suggestions based on user queries. The active learning feature, however, requires that the project receives regular user traffic to generate suggestions.
 
 ## Use active learning
 

articles/ai-services/openai/concepts/provisioned-throughput.md

@@ -37,7 +37,7 @@ An Azure OpenAI Deployment is a unit of management for a specific OpenAI Model.
 | Estimating size | Provided calculator in the studio & benchmarking script. |
 
 
-## How much thoughput per PTU you get for each model
+## How much throughput per PTU you get for each model
 The amount of throughput (tokens per minute or TPM) a deployment gets per PTU is a function of the input and output tokens in the minute. Generating output tokens requires more processing than input tokens and so the more output tokens generated the lower your overall TPM. The service dynamically balances the input & output costs, so users do not have to set specific input and output limits. This approach means your deployment is resilient to fluctuations in the workload shape. 
 
 To help with simplifying the sizing effort, the following table outlines the TPM per PTU for the `gpt-4o` and `gpt-4o-mini` models
@@ -109,7 +109,7 @@ Azure OpenAI is a highly sought-after service where customer demand might exceed
 
 To find the capacity needed for their deployments, use the capacity  API or the Studio deployment experience to provide real-time information on capacity availability.
 
-In Azure OpenAI Studio, the deployment experience identifies when a region lacks the capacity needed to deploy the model. This looks at the desired model, version and number of PTUs. If cpacity is unavailable, the experience direct  users to a select an alternative region.
+In Azure OpenAI Studio, the deployment experience identifies when a region lacks the capacity needed to deploy the model. This looks at the desired model, version and number of PTUs. If capacity is unavailable, the experience direct  users to a select an alternative region.
 
 Details on the new deployment experience can be found in the Azure OpenAI [Provisioned get started guide](../how-to/provisioned-get-started.md).