Update how-to-monitor-datasets.md

Author: SturgeonMi

articles/machine-learning/v1/how-to-monitor-datasets.md

@@ -513,7 +513,7 @@ Metrics in the chart depend on the type of feature.
 
     | Metric | Description |
     | ------ | ----------- |
-    | Euclidian distance   |  Computed for categorical columns. Euclidean distance is computed on two vectors, generated from empirical distribution of the same categorical column from two datasets. 0 indicates no difference in the empirical distributions.  The more it deviates from 0, the more this column has drifted. Trends can be observed from a time series plot of this metric and can be helpful in uncovering a drifting feature.  |
+    | Euclidian distance   | Computed for categorical columns. Euclidean distance is computed on two vectors, generated from empirical distribution of the same categorical column from two datasets. 0 indicates no difference in the empirical distributions.  The more it deviates from 0, the more this column has drifted. Trends can be observed from a time series plot of this metric and can be helpful in uncovering a drifting feature.  |
     | Unique values | Number of unique values (cardinality) of the feature. |
 
 On this chart, select a single date to compare the feature distribution between the target and this date for the displayed feature. For numeric features, this shows two probability distributions. If the feature is numeric, a bar chart is shown.
@@ -522,7 +522,7 @@ On this chart, select a single date to compare the feature distribution between
 
 ## Metrics, alerts, and events
 
-Metrics can be queried in the [Azure Application Insights](../../azure-monitor/app/app-insights-overview.md) resource associated with your machine learning workspace. You have access to all features of Application Insights including set up for custom alert rules and action groups to trigger an action such as, an Email/SMS/Push/Voice or Azure Function. Refer to the complete Application Insights documentation for details.
+Metrics can be queried in the [Azure Application Insights](/azure/azure-monitor/app/app-insights-overview) resource associated with your machine learning workspace. You have access to all features of Application Insights including set up for custom alert rules and action groups to trigger an action such as, an Email/SMS/Push/Voice or Azure Function. Refer to the complete Application Insights documentation for details.
 
 To get started, navigate to the [Azure portal](https://portal.azure.com) and select your workspace's **Overview** page. The associated Application Insights resource is on the far right:
 
@@ -568,6 +568,10 @@ Limitations and known issues for data drift monitors:
 
 * If the SDK `backfill()` function doesn't generate the expected output, it may be due to an authentication issue. When you create the compute to pass into this function, don't use `Run.get_context().experiment.workspace.compute_targets`. Instead, use [ServicePrincipalAuthentication](/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication) such as the following to create the compute that you pass into that `backfill()` function:
 
+> [!NOTE]
+> Do not hard code the service principal password in your code. Instead, retrieve it from the Python environment, key store, or other secure method of accessing secrets.
+> 
+
   ```python
    auth = ServicePrincipalAuthentication(
           tenant_id=tenant_id,