acrolinx updates

Author: JeffKoMS

learn-pr/wwl-data-ai/deploy-containers-azure-container-apps/includes/3-deploy-container-app.md

@@ -1,4 +1,4 @@
-Deployment mechanics shape how reliably you can ship changes to an AI service. Azure Container Apps supports a fast, CLI-first workflow and a more controlled workflow where configuration is stored in YAML. This unit shows both approaches and explains when each one helps.
+Deployment mechanics shape how reliably you can ship changes to an AI service. Azure Container Apps supports a fast, CLI-first workflow, and a more controlled workflow where configuration is stored in YAML. This unit shows both approaches and explains when each one helps.
 
 ## Prepare Azure CLI for Container Apps
 
@@ -17,7 +17,7 @@ az provider register --namespace Microsoft.OperationalInsights
 
 ## Deploy quickly with `az containerapp up`
 
-The `az containerapp up` command is the fastest way to deploy an initial version of an app. It can create the environment and supporting resources for you, which reduces the number of decisions you must make up front. This is useful for prototypes or for the first deployment when you are still exploring the service.
+The `az containerapp up` command is the fastest way to deploy an initial version of an app. It can create the environment and supporting resources for you, which reduces the number of decisions you must make up front. This is useful for prototypes or for the first deployment when you're still exploring the service.
 
 The following example deploys a public container image and returns the fully qualified domain name (FQDN) for the app.
 
@@ -87,7 +87,7 @@ az containerapp update \
 
 ## Be aware of other deployment options
 
-In real projects, you often move from CLI-first deployment to more structured deployment pipelines. Azure Container Apps supports multiple approaches, and you should choose one based on team workflow and compliance requirements. For example, you can use Bicep for infrastructure as code, GitHub Actions for continuous deployment, or the Azure portal for ad hoc changes.
+In real projects, you often move from CLI-first deployment to more structured deployment pipelines. Azure Container Apps supports multiple approaches, and you should choose one based on team workflow and compliance requirements. For example, you can use Bicep for infrastructure as code, GitHub Actions for continuous deployment, or the Azure portal for unplanned changes.
 
 Even when you use these other approaches, the CLI and YAML patterns in this unit remain useful. They help you understand which properties change when you deploy and where to look when troubleshooting.
 

learn-pr/wwl-data-ai/deploy-containers-azure-container-apps/includes/4-configure-runtime.md

@@ -1,6 +1,6 @@
 AI services often depend on external systems and require many runtime settings. You typically configure endpoints, feature flags, and model selection settings using environment variables. You handle sensitive settings, such as API keys, using secrets and secret references.
 
-## Configure non-sensitive settings with environment variables
+## Configure nonsensitive settings with environment variables
 
 Environment variables are a good fit for non-sensitive configuration like log levels, feature flags, and dependency URLs. This approach keeps container images portable across environments because you can run the same image with different configuration values. For AI workloads, environment variables also help you tune behavior such as request timeouts and batch sizes without rebuilding images.
 
@@ -23,7 +23,7 @@ az containerapp update -n ai-api -g rg-aca-demo \
 
 ## Store sensitive settings as Container Apps secrets
 
-Secrets let you keep sensitive values out of container images and out of source control. This matters for AI services because provider API keys, database credentials, and signing keys rotate regularly. By storing secrets separately, you can rotate secrets without rebuilding or redeploying the image.
+Secrets let you keep sensitive values out of container images and out of source control. Protecting sensitive values matters for AI services because provider API keys, database credentials, and signing keys rotate regularly. By storing secrets separately, you can rotate secrets without rebuilding or redeploying the image.
 
 You can create or update secrets using `az containerapp secret set`. Secrets are provided as `key=value` pairs.
 
@@ -36,7 +36,7 @@ If you need to reference Azure Key Vault, the CLI also supports a Key Vault refe
 
 ## Reference secrets from environment variables
 
-Most application frameworks read configuration from environment variables. Container Apps supports secret references so you can map a secret to an environment variable without exposing the value. This is a common pattern for AI apps that call external model endpoints.
+Most application frameworks read configuration from environment variables. Container Apps supports secret references so you can map a secret to an environment variable without exposing the value, which is a common pattern for AI apps that call external model endpoints.
 
 You reference a secret by setting the environment variable value to `secretref:<secret-name>`. The following example sets `EMBEDDINGS_API_KEY` to the secret value.
 
@@ -76,7 +76,7 @@ az containerapp update -n ai-api -g rg-aca-demo \
 Runtime configuration should reduce risk and simplify operations. You can use the following practices to keep configuration manageable as your AI solution grows.
 
 - **Separate config from images:** Use environment variables and secrets so the same image can run across multiple environments.
-- **Prefer secret references:** Use `secretref:` and `secretRef` so secret values do not appear in YAML files or shell history.
+- **Prefer secret references:** Use `secretref:` and `secretRef` so secret values don't appear in YAML files or shell history.
 - **Rotate secrets intentionally:** Update secrets independently of images so you can respond quickly to credential rotation requirements.
 - **Use YAML for consistency:** Store YAML in source control to reduce drift and make configuration changes reviewable.
 

learn-pr/wwl-data-ai/deploy-containers-azure-container-apps/includes/5-connect-to-registries.md

@@ -5,7 +5,7 @@ Production AI services often run images from private registries. A private regis
 
 ## Understand registry options
 
-When you configure a container app to pull from a private registry, you must provide credentials or an identity that can pull images. The simplest option is username and password, which is useful for quick validation and for registries that do not support Azure-managed identities. For Azure Container Registry, managed identity is often a better fit because it avoids storing long-lived credentials.
+When you configure a container app to pull from a private registry, you must provide credentials or an identity that can pull images. The simplest option is username and password, which is useful for quick validation and for registries that don't support Azure-managed identities. For Azure Container Registry, managed identity is often a better fit because it avoids storing long-lived credentials.
 
 Your choice is a trade-off. Username and password works broadly, but it increases secret management overhead. Managed identity requires Azure role assignments, but it reduces the number of secrets your team must rotate.
 
@@ -22,7 +22,7 @@ az containerapp registry set -n ai-api -g rg-aca-demo \
     --password MyRegistryPassword
 ```
 
-If you are using Azure Container Registry, Azure CLI can sometimes infer credentials if you omit them. You should still prefer an explicit authentication approach in production so behavior is predictable.
+If you're using Azure Container Registry, Azure CLI can sometimes infer credentials if you omit them. You should still prefer an explicit authentication approach in production so behavior is predictable.
 
 ## Configure a registry using managed identity for Azure Container Registry
 

learn-pr/wwl-data-ai/deploy-containers-azure-container-apps/includes/6-verify-deployment.md

@@ -1,7 +1,7 @@
 Verification closes the loop between deployment intent and what actually runs in production. For AI services, verification is especially important because startup failures can look like model connectivity problems, missing secrets, or registry authentication errors. In Azure Container Apps, you can verify deployments through logs, revisions, and replica status.
 
 > [!NOTE]
-> Container Apps logs can include console logs from your container and system logs from the platform. These two log types answer different questions, so choose the one that matches the symptom you are investigating.
+> Container Apps logs can include console logs from your container and system logs from the platform. These two log types answer different questions, so choose the one that matches the symptom you're investigating.
 
 ## Confirm app configuration and ingress
 
@@ -15,7 +15,7 @@ az containerapp show -n ai-api -g rg-aca-demo
 
 ## Investigate container logs
 
-Container logs are usually the fastest way to understand why a revision fails. They commonly show errors such as missing environment variables, connection failures, or application crashes. When you are deploying an AI API, logs often show HTTP binding issues, dependency endpoint issues, or authentication failures when calling model providers.
+Container logs are usually the fastest way to understand why a revision fails. They commonly show errors such as missing environment variables, connection failures, or application crashes. When you're deploying an AI API, logs often show HTTP binding issues, dependency endpoint issues, or authentication failures when calling model providers.
 
 You can view recent console logs using `az containerapp logs show`. By default, this command returns a limited number of lines.
 
@@ -55,7 +55,7 @@ az containerapp revision list -n ai-api -g rg-aca-demo --all
 
 ## Check replicas to understand scaling and availability
 
-Replicas are the running instances of a revision. For request-driven AI workloads, replica behavior affects latency and throughput because cold starts and scale-out events can add delay. Listing replicas helps you determine whether the app is running and whether it is scaling as expected.
+Replicas are the running instances of a revision. For request-driven AI workloads, replica behavior affects latency and throughput because cold starts and scale-out events can add delay. Listing replicas helps you determine whether the app is running and whether it's scaling as expected.
 
 You can list replicas for the latest revision, or you can provide a revision name.
 
@@ -70,7 +70,7 @@ az containerapp replica list -n ai-api -g rg-aca-demo \
 
 ## Best practices for verification
 
-Verification is most effective when it is consistent and repeatable. You can use the following practices to reduce time-to-diagnosis for AI services.
+Verification is most effective when it's consistent and repeatable. You can use the following practices to reduce time-to-diagnosis for AI services.
 
 - **Start with logs:** Use `az containerapp logs show` first to capture startup and runtime errors.
 - **Use revisions for rollout validation:** List revisions after updates to confirm a new revision exists and becomes active.
@@ -79,7 +79,7 @@ Verification is most effective when it is consistent and repeatable. You can use
 
 ## Additional resources
 
-These resources help you go deeper on day-two verification and troubleshooting for Container Apps. Use the CLI reference pages to confirm flags for targeting specific revisions and replicas when you are narrowing down an incident. You can also use these references to build automation that extracts the exact fields you validate in deployment pipelines.
+These resources help you go deeper on day-two verification and troubleshooting for Container Apps. Use the CLI reference pages to confirm flags for targeting specific revisions and replicas when you're narrowing down an incident. You can also use these references to build automation that extracts the exact fields you validate in deployment pipelines.
 
 - [Azure CLI: az containerapp logs](/cli/azure/containerapp/logs?view=azure-cli-latest)
 - [Azure CLI: az containerapp revision](/cli/azure/containerapp/revision?view=azure-cli-latest)

learn-pr/wwl-data-ai/deploy-containers-azure-container-apps/includes/7-exercise-deploy-backend-api.md

@@ -20,7 +20,7 @@ To complete the exercise, you need:
 
 - An Azure subscription with the permissions to deploy the necessary Azure services. If you don't already have one, you can [sign up for one](https://azure.microsoft.com/).
 - [Visual Studio Code](https://code.visualstudio.com/) on one of the [supported platforms](https://code.visualstudio.com/docs/supporting/requirements#_platforms).
-- The latest version of the [Azure CLI](cli/azure/install-azure-cli?view=azure-cli-latest).
+- The latest version of the [Azure CLI](cli/azure/install-azure-cli).
 - Optional - [Python 3.12](https://www.python.org/downloads/) or greater.
 
 ## Get started