wording, link, quickstart changes

Author: aahill

articles/cognitive-services/language-service/conversational-language-understanding/how-to/deploy-query-model.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: how-to
-ms.date: 01/26/2022
+ms.date: 03/15/2022
 ms.author: aahi
 ms.devlang: csharp, python
 ms.custom: language-service-clu, ignite-fall-2021
@@ -31,11 +31,11 @@ When a model is deployed, you will be able to test the model directly in the por
 
 1.	Click on *Add deployment* to submit a new deployment job
 
-:::image type="content" source="../media/add-deployment-model.png" alt-text="A screenshot showing the model deployment page in Language Studio." lightbox="../media/add-deploy-model.png":::
+:::image type="content" source="../media/add-deployment-model.png" alt-text="A screenshot showing the model deployment button in Language Studio." lightbox="../media/add-deployment-model.png":::
 
 2. In the window that appears, you can create a new deployment name by giving the deployment a name or override an existing deployment name. Then, you can add a trained model to this deployment name.
 
-:::image type="content" source="../media/create-deployment-job.png" alt-text="A screenshot showing deployment job creation in Language Studio." lightbox="../media/create-deployment-job.png":::
+:::image type="content" source="../media/create-deployment-job.png" alt-text="A screenshot showing the add deployment job screen in Language Studio." lightbox="../media/create-deployment-job.png":::
 
 
 #### Swap deployments

articles/cognitive-services/language-service/conversational-language-understanding/includes/quickstarts/language-studio.md

@@ -5,7 +5,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: include
-ms.date: 01/27/2022
+ms.date: 03/15/2022
 ms.author: aahi
 ms.custom: ignite-fall-2021
 ---
@@ -102,15 +102,15 @@ When you're done, select **Save Changes** to save the utterances and labels to t
 Select **train model** on the left of the screen. Select **Start a training job**. To train your model, you need to provide a name for the model. Write a name like "*v1*" and press the enter key.
 
 > [!NOTE]
-> If you did not [add utterances to testing set](https://docs.microsoft.com/azure/cognitive-services/language-service/conversational-language-understanding/how-to/tag-utterances#tag-utterances), you will only be allowed to train using the **Automatically split the testing set from all data** option.
+> If you did not [tag utterances](#tag-utterances) you will only be allowed to train using the **Automatically split the testing set from all data** option. See [Add utterances to testing set](../../how-to/tag-utterances#tag-utterances) for more information.
 
 When the training job is complete, which may take some time, you should see the output model performance in the **View model details** page.
 
 ## Deploy your model
 
 From the **Deploy model** page on the left of the screen, select **Add deployment**.
 
-In the window that appears, give your deployment a **deploment name** and then assign your trained model to this deployment name and then select **Submit**.
+In the window that appears, give your deployment a **deployment name** and then assign your trained model to this deployment name and then select **Submit**.
 
 ## Test your model