A would instead validate the other design where a high-traffic web storefront repeatedly reads expensive product catalog summaries from SQL. That evidence is consistent with introduce an application cache using Azure Managed Redis with appropriate TTL, because managed Redis reduces redundant backend reads while retaining an explicit expiry strategy. It does not demonstrate that the target case achieves clients load the intended labeled values for their environment.

C would instead validate the other design where a microservices suite needs centrally managed nonsecret feature flags changed without rebuilding binaries. That evidence is consistent with store application feature flags in Azure App Configuration, because app Configuration centralizes feature switches separate from application deployment. It does not demonstrate that the target case achieves clients load the intended labeled values for their environment.

D would instead validate the other design where a service must retrieve secrets without putting secret values in its ordinary configuration store. That evidence is consistent with use Azure App Configuration Key Vault references plus managed identity, because app Configuration can reference secrets that remain stored in Key Vault. It does not demonstrate that the target case achieves clients load the intended labeled values for their environment.

“Choose Azure Queue Storage for the basic work queue” relies on this Azure capability: storage queues provide a straightforward queue model when advanced broker features are unnecessary. This workload involves A service must retrieve secrets without putting secret values in its ordinary configuration store. The decisive Azure design mechanism is: app Configuration can reference secrets that remain stored in Key Vault. Thus “Use Azure App Configuration Key Vault references plus managed identity” is the applicable architecture choice.

“Organize configuration keys using Azure App Configuration labels” relies on this Azure capability: labeled keys support environment-scoped configuration variants. This workload involves A service must retrieve secrets without putting secret values in its ordinary configuration store. The decisive Azure design mechanism is: app Configuration can reference secrets that remain stored in Key Vault. Thus “Use Azure App Configuration Key Vault references plus managed identity” is the applicable architecture choice.

“Store application feature flags in Azure App Configuration” relies on this Azure capability: app Configuration centralizes feature switches separate from application deployment. This workload involves A service must retrieve secrets without putting secret values in its ordinary configuration store. The decisive Azure design mechanism is: app Configuration can reference secrets that remain stored in Key Vault. Thus “Use Azure App Configuration Key Vault references plus managed identity” is the applicable architecture choice.

A would instead validate the other design where a simple asynchronous worker needs low-complexity queued work with inexpensive storage integration. That evidence is consistent with choose Azure Queue Storage for the basic work queue, because storage queues provide a straightforward queue model when advanced broker features are unnecessary. It does not demonstrate that the target case achieves the workload resolves an authorized secret reference without exposing raw secret content in App Configuration.

B would instead validate the other design where a distributed application uses different settings for development and production but wants a single governed configuration store. That evidence is consistent with organize configuration keys using Azure App Configuration labels, because labeled keys support environment-scoped configuration variants. It does not demonstrate that the target case achieves the workload resolves an authorized secret reference without exposing raw secret content in App Configuration.

C would instead validate the other design where a microservices suite needs centrally managed nonsecret feature flags changed without rebuilding binaries. That evidence is consistent with store application feature flags in Azure App Configuration, because app Configuration centralizes feature switches separate from application deployment. It does not demonstrate that the target case achieves the workload resolves an authorized secret reference without exposing raw secret content in App Configuration.

“Use Azure App Service deployment slots for the eligible plan” relies on this Azure capability: deployment slots allow warming and validating a staged version before swap. This workload involves An enterprise architecture assessment records: A cloud foundation team wants repeatable code-reviewed infrastructure rollouts across subscriptions. The decisive Azure design mechanism is: bicep models ARM resources declaratively and enables repeatable deployments. Thus “Author and deploy Azure Bicep modules in an infrastructure-as-code pipeline” is the applicable architecture choice.

“Publish versioned APIs through Azure API Management” relies on this Azure capability: APIM supports explicit versions and revisions for managed API products. This workload involves An enterprise architecture assessment records: A cloud foundation team wants repeatable code-reviewed infrastructure rollouts across subscriptions. The decisive Azure design mechanism is: bicep models ARM resources declaratively and enables repeatable deployments. Thus “Author and deploy Azure Bicep modules in an infrastructure-as-code pipeline” is the applicable architecture choice.

“Build a GitHub Actions workflow with secure cloud authentication” relies on this Azure capability: cI/CD automation gates deployments on reproducible test and build steps. This workload involves An enterprise architecture assessment records: A cloud foundation team wants repeatable code-reviewed infrastructure rollouts across subscriptions. The decisive Azure design mechanism is: bicep models ARM resources declaratively and enables repeatable deployments. Thus “Author and deploy Azure Bicep modules in an infrastructure-as-code pipeline” is the applicable architecture choice.

A would instead validate the other design where a production web app needs near-zero-downtime release validation and rapid slot swap. That evidence is consistent with use Azure App Service deployment slots for the eligible plan, because deployment slots allow warming and validating a staged version before swap. It does not demonstrate that the target case achieves a repeat deployment matches the desired resource state without manual portal edits.

C would instead validate the other design where a company hosts several backend API versions and needs stable consumer-facing contracts. That evidence is consistent with publish versioned APIs through Azure API Management, because aPIM supports explicit versions and revisions for managed API products. It does not demonstrate that the target case achieves a repeat deployment matches the desired resource state without manual portal edits.

D would instead validate the other design where an engineering team wants every approved code commit to build, test, and deploy its Azure application. That evidence is consistent with build a GitHub Actions workflow with secure cloud authentication, because cI/CD automation gates deployments on reproducible test and build steps. It does not demonstrate that the target case achieves a repeat deployment matches the desired resource state without manual portal edits.

“Author and deploy Azure Bicep modules in an infrastructure-as-code pipeline” relies on this Azure capability: bicep models ARM resources declaratively and enables repeatable deployments. This workload involves A cloud migration design must satisfy the following: An engineering team wants every approved code commit to build, test, and deploy its Azure application. The decisive Azure design mechanism is: cI/CD automation gates deployments on reproducible test and build steps. Thus “Build a GitHub Actions workflow with secure cloud authentication” is the applicable architecture choice.

“Use Azure App Service deployment slots for the eligible plan” relies on this Azure capability: deployment slots allow warming and validating a staged version before swap. This workload involves A cloud migration design must satisfy the following: An engineering team wants every approved code commit to build, test, and deploy its Azure application. The decisive Azure design mechanism is: cI/CD automation gates deployments on reproducible test and build steps. Thus “Build a GitHub Actions workflow with secure cloud authentication” is the applicable architecture choice.

“Use an Azure Service Bus queue with the required broker features” relies on this Azure capability: service Bus supports enterprise messaging operations and failure handling. This workload involves A cloud migration design must satisfy the following: An engineering team wants every approved code commit to build, test, and deploy its Azure application. The decisive Azure design mechanism is: cI/CD automation gates deployments on reproducible test and build steps. Thus “Build a GitHub Actions workflow with secure cloud authentication” is the applicable architecture choice.

A would instead validate the other design where a production web app needs near-zero-downtime release validation and rapid slot swap. That evidence is consistent with use Azure App Service deployment slots for the eligible plan, because deployment slots allow warming and validating a staged version before swap. It does not demonstrate that the target case achieves a passing change deploys automatically while a failing test blocks release.

B would instead validate the other design where a cloud foundation team wants repeatable code-reviewed infrastructure rollouts across subscriptions. That evidence is consistent with author and deploy Azure Bicep modules in an infrastructure-as-code pipeline, because bicep models ARM resources declaratively and enables repeatable deployments. It does not demonstrate that the target case achieves a passing change deploys automatically while a failing test blocks release.

C would instead validate the other design where a payment service needs durable ordered processing, duplicate detection, and dead-letter handling. That evidence is consistent with use an Azure Service Bus queue with the required broker features, because service Bus supports enterprise messaging operations and failure handling. It does not demonstrate that the target case achieves a passing change deploys automatically while a failing test blocks release.

“Build a GitHub Actions workflow with secure cloud authentication” relies on this Azure capability: cI/CD automation gates deployments on reproducible test and build steps. This workload involves A production web app needs near-zero-downtime release validation and rapid slot swap. The decisive Azure design mechanism is: deployment slots allow warming and validating a staged version before swap. Thus “Use Azure App Service deployment slots for the eligible plan” is the applicable architecture choice.

“Evaluate Azure API Management self-hosted gateway for the supported topology” relies on this Azure capability: self-hosted gateway extends API policy enforcement into customer-operated environments. This workload involves A production web app needs near-zero-downtime release validation and rapid slot swap. The decisive Azure design mechanism is: deployment slots allow warming and validating a staged version before swap. Thus “Use Azure App Service deployment slots for the eligible plan” is the applicable architecture choice.

“Author and deploy Azure Bicep modules in an infrastructure-as-code pipeline” relies on this Azure capability: bicep models ARM resources declaratively and enables repeatable deployments. This workload involves A production web app needs near-zero-downtime release validation and rapid slot swap. The decisive Azure design mechanism is: deployment slots allow warming and validating a staged version before swap. Thus “Use Azure App Service deployment slots for the eligible plan” is the applicable architecture choice.

A would instead validate the other design where a cloud foundation team wants repeatable code-reviewed infrastructure rollouts across subscriptions. That evidence is consistent with author and deploy Azure Bicep modules in an infrastructure-as-code pipeline, because bicep models ARM resources declaratively and enables repeatable deployments. It does not demonstrate that the target case achieves the staged app passes tests and a swap changes live traffic without replacing the running plan.

C would instead validate the other design where an engineering team wants every approved code commit to build, test, and deploy its Azure application. That evidence is consistent with build a GitHub Actions workflow with secure cloud authentication, because cI/CD automation gates deployments on reproducible test and build steps. It does not demonstrate that the target case achieves the staged app passes tests and a swap changes live traffic without replacing the running plan.

D would instead validate the other design where a hybrid developer team wants one API control plane while executing selected gateway traffic in its own network. That evidence is consistent with evaluate Azure API Management self-hosted gateway for the supported topology, because self-hosted gateway extends API policy enforcement into customer-operated environments. It does not demonstrate that the target case achieves the staged app passes tests and a swap changes live traffic without replacing the running plan.

“Apply CAF strategy and governance practices with documented responsibilities and success metrics” relies on this Azure capability: cAF ties technical migration work to organizational adoption outcomes. This workload involves An Azure solution needs to solve the following challenge: An enterprise wants to decide which applications to retire, rehost, refactor, or replace before moving infrastructure. The decisive Azure design mechanism is: cAF planning aligns business value, dependencies, and modernization choices before execution. Thus “Perform workload rationalization and migration-wave planning under the Cloud Adoption Framework” is the applicable architecture choice.

“Evaluate migration from SQL Server to Azure SQL Managed Instance” relies on this Azure capability: managed Instance supports many SQL Server features that single databases may not. This workload involves An Azure solution needs to solve the following challenge: An enterprise wants to decide which applications to retire, rehost, refactor, or replace before moving infrastructure. The decisive Azure design mechanism is: cAF planning aligns business value, dependencies, and modernization choices before execution. Thus “Perform workload rationalization and migration-wave planning under the Cloud Adoption Framework” is the applicable architecture choice.

“Build an Azure landing zone aligned with the Cloud Adoption Framework before broad workload migration” relies on this Azure capability: landing zones provide foundational governance and connectivity for cloud adoption. This workload involves An Azure solution needs to solve the following challenge: An enterprise wants to decide which applications to retire, rehost, refactor, or replace before moving infrastructure. The decisive Azure design mechanism is: cAF planning aligns business value, dependencies, and modernization choices before execution. Thus “Perform workload rationalization and migration-wave planning under the Cloud Adoption Framework” is the applicable architecture choice.

A would instead validate the other design where a vendor depends on SQL Server instance features and wants a managed target with minimal application redesign. That evidence is consistent with evaluate migration from SQL Server to Azure SQL Managed Instance, because managed Instance supports many SQL Server features that single databases may not. It does not demonstrate that the target case achieves each application has a documented migration treatment and ordered release wave.

B would instead validate the other design where an organization has not yet established identity, networking, and policies for multiple migration teams. That evidence is consistent with build an Azure landing zone aligned with the Cloud Adoption Framework before broad workload migration, because landing zones provide foundational governance and connectivity for cloud adoption. It does not demonstrate that the target case achieves each application has a documented migration treatment and ordered release wave.

C would instead validate the other design where a multi-year migration program needs ownership, risks, and measurable business outcomes for each migration phase. That evidence is consistent with apply CAF strategy and governance practices with documented responsibilities and success metrics, because cAF ties technical migration work to organizational adoption outcomes. It does not demonstrate that the target case achieves each application has a documented migration treatment and ordered release wave.