Minor updates to MVAD from .NET user studies

specification/cognitiveservices/AnomalyDetector/multivariate/models.tsp

@@ -38,9 +38,13 @@ enum AlignMode {
 }
 
 enum ModelStatus {
+  @doc("The model has been created. Training has been scheduled but not yet started.")
   Created: "CREATED",
+  @doc("The model is being trained.")
   Running: "RUNNING",
+  @doc("The model has been trained and is ready to be used for anomaly detection.")
   Ready: "READY",
+  @doc("The model training failed.")
   Failed: "FAILED",
 }
 
@@ -118,9 +122,11 @@ model MultivariateBatchDetectionOptions {
 Source link to the input data to indicate an accessible Azure Storage URI.
 It either points to an Azure Blob Storage folder or points to a CSV file in
 Azure Blob Storage, based on your data schema selection. The data schema should
-be exactly the same as those used in the training phase.
+be exactly the same as those used in the training phase. The input data must
+contain at least slidingWindow entries preceding the start time of the data
+to be detected.
 """)
-  dataSource: string;
+  dataSource: url;
 
   @doc("""
 Number of top contributed
@@ -206,7 +212,7 @@ Source link to the input data to indicate an accessible Azure Storage URI.
 It either points to an Azure Blob Storage folder or points to a CSV file in
 Azure Blob Storage, based on your data schema selection.
 """)
-  dataSource: string;
+  dataSource: url;
 
   @doc("""
 Data schema of the input data source. The default

specification/cognitiveservices/AnomalyDetector/multivariate/routes.tsp

@@ -48,6 +48,10 @@ Storage URI that's accessible to the service. There are two types of data input.
 Storage folder that contains multiple CSV files, where each CSV file has
 two columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file that has all the variables and a
 time stamp column.
+The model object will be created and returned in the response, but the
+training process happens asynchronously. To check the training status, call
+GetMultivariateModel with the modelId value and check the status field in the
+modelInfo object.
 """)
 op TrainMultivariateModel is MultivariateServiceAction<
   // TParams

specification/cognitiveservices/data-plane/AnomalyDetector/stable/v1.1/openapi.json

@@ -112,7 +112,7 @@
       "post": {
         "operationId": "Multivariate_TrainMultivariateModel",
         "summary": "Train a Multivariate Anomaly Detection Model",
-        "description": "Create and train a multivariate anomaly detection model. The request must\ninclude a source parameter to indicate an Azure Blob\nStorage URI that's accessible to the service. There are two types of data input. The Blob Storage URI can point to an Azure Blob\nStorage folder that contains multiple CSV files, where each CSV file has\ntwo columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file that has all the variables and a\ntime stamp column.",
+        "description": "Create and train a multivariate anomaly detection model. The request must\ninclude a source parameter to indicate an Azure Blob\nStorage URI that's accessible to the service. There are two types of data input. The Blob Storage URI can point to an Azure Blob\nStorage folder that contains multiple CSV files, where each CSV file has\ntwo columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file that has all the variables and a\ntime stamp column.\nThe model object will be created and returned in the response, but the\ntraining process happens asynchronously. To check the training status, call\nGetMultivariateModel with the modelId value and check the status field in the\nmodelInfo object.",
         "parameters": [
           {
             "name": "modelInfo",
@@ -755,6 +755,7 @@
       "properties": {
         "dataSource": {
           "type": "string",
+          "format": "uri",
           "description": "Source link to the input data to indicate an accessible Azure Storage URI.\nIt either points to an Azure Blob Storage folder or points to a CSV file in\nAzure Blob Storage, based on your data schema selection."
         },
         "dataSchema": {
@@ -903,19 +904,23 @@
         "values": [
           {
             "name": "Created",
-            "value": "CREATED"
+            "value": "CREATED",
+            "description": "The model has been created. Training has been scheduled but not yet started."
           },
           {
             "name": "Running",
-            "value": "RUNNING"
+            "value": "RUNNING",
+            "description": "The model is being trained."
           },
           {
             "name": "Ready",
-            "value": "READY"
+            "value": "READY",
+            "description": "The model has been trained and is ready to be used for anomaly detection."
           },
           {
             "name": "Failed",
-            "value": "FAILED"
+            "value": "FAILED",
+            "description": "The model training failed."
           }
         ]
       }
@@ -925,7 +930,8 @@
       "properties": {
         "dataSource": {
           "type": "string",
-          "description": "Source link to the input data to indicate an accessible Azure Storage URI.\nIt either points to an Azure Blob Storage folder or points to a CSV file in\nAzure Blob Storage, based on your data schema selection. The data schema should\nbe exactly the same as those used in the training phase."
+          "format": "uri",
+          "description": "Source link to the input data to indicate an accessible Azure Storage URI.\nIt either points to an Azure Blob Storage folder or points to a CSV file in\nAzure Blob Storage, based on your data schema selection. The data schema should\nbe exactly the same as those used in the training phase. The input data must\ncontain at least slidingWindow entries preceding the start time of the data\nto be detected."
         },
         "topContributorCount": {
           "type": "integer",