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.

“Use Azure Migrate server migration for supported source virtualization” relies on this Azure capability: server migration supports replicate-test-cutover workflows for rehosting. This workload involves An organization has not yet established identity, networking, and policies for multiple migration teams. The decisive Azure design mechanism is: landing zones provide foundational governance and connectivity for cloud adoption. Thus “Build an Azure landing zone aligned with the Cloud Adoption Framework before broad workload migration” is the applicable architecture choice.

“Perform workload rationalization and migration-wave planning under the Cloud Adoption Framework” relies on this Azure capability: cAF planning aligns business value, dependencies, and modernization choices before execution. This workload involves An organization has not yet established identity, networking, and policies for multiple migration teams. The decisive Azure design mechanism is: landing zones provide foundational governance and connectivity for cloud adoption. Thus “Build an Azure landing zone aligned with the Cloud Adoption Framework before broad workload migration” is the applicable architecture choice.

“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 organization has not yet established identity, networking, and policies for multiple migration teams. The decisive Azure design mechanism is: landing zones provide foundational governance and connectivity for cloud adoption. Thus “Build an Azure landing zone aligned with the Cloud Adoption Framework before broad workload migration” is the applicable architecture choice.

A would instead validate the other design where an enterprise has hundreds of eligible on-premises VMs and wants to rehost them as Azure VMs with minimal application change. That evidence is consistent with use Azure Migrate server migration for supported source virtualization, because server migration supports replicate-test-cutover workflows for rehosting. It does not demonstrate that the target case achieves the pilot workload deploys within approved network, identity, and policy boundaries.

C would instead validate the other design where an enterprise wants to decide which applications to retire, rehost, refactor, or replace before moving infrastructure. That evidence is consistent with perform workload rationalization and migration-wave planning under the Cloud Adoption Framework, because cAF planning aligns business value, dependencies, and modernization choices before execution. It does not demonstrate that the target case achieves the pilot workload deploys within approved network, identity, and policy boundaries.

D 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 the pilot workload deploys within approved network, identity, and policy boundaries.

“Configure Azure File Sync with supported server endpoints and cloud endpoint” relies on this Azure capability: file Sync synchronizes file-share data while retaining local cache capabilities. This workload involves A multi-year migration program needs ownership, risks, and measurable business outcomes for each migration phase. The decisive Azure design mechanism is: cAF ties technical migration work to organizational adoption outcomes. Thus “Apply CAF strategy and governance practices with documented responsibilities and success metrics” 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 A multi-year migration program needs ownership, risks, and measurable business outcomes for each migration phase. The decisive Azure design mechanism is: cAF ties technical migration work to organizational adoption outcomes. Thus “Apply CAF strategy and governance practices with documented responsibilities and success metrics” is the applicable architecture choice.

“Perform workload rationalization and migration-wave planning under the Cloud Adoption Framework” relies on this Azure capability: cAF planning aligns business value, dependencies, and modernization choices before execution. This workload involves A multi-year migration program needs ownership, risks, and measurable business outcomes for each migration phase. The decisive Azure design mechanism is: cAF ties technical migration work to organizational adoption outcomes. Thus “Apply CAF strategy and governance practices with documented responsibilities and success metrics” is the applicable architecture choice.

A would instead validate the other design where a business wants on-premises Windows file servers to cache and synchronize with an Azure Files share. That evidence is consistent with configure Azure File Sync with supported server endpoints and cloud endpoint, because file Sync synchronizes file-share data while retaining local cache capabilities. It does not demonstrate that the target case achieves each migration wave has documented decision owners, readiness evidence, and success measurements.

B would instead validate the other design where an enterprise wants to decide which applications to retire, rehost, refactor, or replace before moving infrastructure. That evidence is consistent with perform workload rationalization and migration-wave planning under the Cloud Adoption Framework, because cAF planning aligns business value, dependencies, and modernization choices before execution. It does not demonstrate that the target case achieves each migration wave has documented decision owners, readiness evidence, and success measurements.

C 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 migration wave has documented decision owners, readiness evidence, and success measurements.

“Assess and migrate the web application to Azure App Service” relies on this Azure capability: paaS hosting reduces ongoing operating-system administration for compatible web apps. This workload involves A proposed Azure workload has the following constraint: A data-center company cannot accurately inventory its existing servers before an Azure move. The decisive Azure design mechanism is: discovery identifies workloads, guest details, and migration candidates. Thus “Use Azure Migrate discovery for supported on-premises server estates” is the applicable architecture choice.

“Run Azure Migrate assessments with performance-based sizing where supported” relies on this Azure capability: performance assessments inform right-sized target capacity and cost estimates. This workload involves A proposed Azure workload has the following constraint: A data-center company cannot accurately inventory its existing servers before an Azure move. The decisive Azure design mechanism is: discovery identifies workloads, guest details, and migration candidates. Thus “Use Azure Migrate discovery for supported on-premises server estates” is the applicable architecture choice.

“Analyze discovered application dependencies before creating migration waves” relies on this Azure capability: dependency mapping reveals communications that affect wave order and downtime. This workload involves A proposed Azure workload has the following constraint: A data-center company cannot accurately inventory its existing servers before an Azure move. The decisive Azure design mechanism is: discovery identifies workloads, guest details, and migration candidates. Thus “Use Azure Migrate discovery for supported on-premises server estates” is the applicable architecture choice.

A would instead validate the other design where a migration architect wants Azure VM sizing estimates based on actual source resource consumption. That evidence is consistent with run Azure Migrate assessments with performance-based sizing where supported, because performance assessments inform right-sized target capacity and cost estimates. It does not demonstrate that the target case achieves the discovered inventory matches known servers and application owners.

C would instead validate the other design where an enterprise must sequence dependent application tiers so a database is not moved after its consumers are cut over. That evidence is consistent with analyze discovered application dependencies before creating migration waves, because dependency mapping reveals communications that affect wave order and downtime. It does not demonstrate that the target case achieves the discovered inventory matches known servers and application owners.

D would instead validate the other design where a company owns an eligible IIS web app that can run on the Azure App Service platform. That evidence is consistent with assess and migrate the web application to Azure App Service, because paaS hosting reduces ongoing operating-system administration for compatible web apps. It does not demonstrate that the target case achieves the discovered inventory matches known servers and application owners.

“Analyze discovered application dependencies before creating migration waves” relies on this Azure capability: dependency mapping reveals communications that affect wave order and downtime. This workload involves During a cloud design workshop, stakeholders explain: A migration architect wants Azure VM sizing estimates based on actual source resource consumption. The decisive Azure design mechanism is: performance assessments inform right-sized target capacity and cost estimates. Thus “Run Azure Migrate assessments with performance-based sizing where supported” is the applicable architecture choice.

“Use Azure Migrate discovery for supported on-premises server estates” relies on this Azure capability: discovery identifies workloads, guest details, and migration candidates. This workload involves During a cloud design workshop, stakeholders explain: A migration architect wants Azure VM sizing estimates based on actual source resource consumption. The decisive Azure design mechanism is: performance assessments inform right-sized target capacity and cost estimates. Thus “Run Azure Migrate assessments with performance-based sizing where supported” is the applicable architecture choice.

“Perform workload rationalization and migration-wave planning under the Cloud Adoption Framework” relies on this Azure capability: cAF planning aligns business value, dependencies, and modernization choices before execution. This workload involves During a cloud design workshop, stakeholders explain: A migration architect wants Azure VM sizing estimates based on actual source resource consumption. The decisive Azure design mechanism is: performance assessments inform right-sized target capacity and cost estimates. Thus “Run Azure Migrate assessments with performance-based sizing where supported” is the applicable architecture choice.

A would instead validate the other design where an enterprise wants to decide which applications to retire, rehost, refactor, or replace before moving infrastructure. That evidence is consistent with perform workload rationalization and migration-wave planning under the Cloud Adoption Framework, because cAF planning aligns business value, dependencies, and modernization choices before execution. It does not demonstrate that the target case achieves the migration assessment proposes capacity using observed utilization windows.

B would instead validate the other design where an enterprise must sequence dependent application tiers so a database is not moved after its consumers are cut over. That evidence is consistent with analyze discovered application dependencies before creating migration waves, because dependency mapping reveals communications that affect wave order and downtime. It does not demonstrate that the target case achieves the migration assessment proposes capacity using observed utilization windows.

C would instead validate the other design where a data-center company cannot accurately inventory its existing servers before an Azure move. That evidence is consistent with use Azure Migrate discovery for supported on-premises server estates, because discovery identifies workloads, guest details, and migration candidates. It does not demonstrate that the target case achieves the migration assessment proposes capacity using observed utilization windows.

“Use Azure Migrate discovery for supported on-premises server estates” relies on this Azure capability: discovery identifies workloads, guest details, and migration candidates. This workload involves An enterprise must sequence dependent application tiers so a database is not moved after its consumers are cut over. The decisive Azure design mechanism is: dependency mapping reveals communications that affect wave order and downtime. Thus “Analyze discovered application dependencies before creating migration waves” is the applicable architecture choice.

“Run Azure Migrate assessments with performance-based sizing where supported” relies on this Azure capability: performance assessments inform right-sized target capacity and cost estimates. This workload involves An enterprise must sequence dependent application tiers so a database is not moved after its consumers are cut over. The decisive Azure design mechanism is: dependency mapping reveals communications that affect wave order and downtime. Thus “Analyze discovered application dependencies before creating migration waves” is the applicable architecture choice.

“Use the supported Azure Database for PostgreSQL migration service or documented migration method” relies on this Azure capability: postgreSQL-targeted migration tools handle data transfer and cutover planning. This workload involves An enterprise must sequence dependent application tiers so a database is not moved after its consumers are cut over. The decisive Azure design mechanism is: dependency mapping reveals communications that affect wave order and downtime. Thus “Analyze discovered application dependencies before creating migration waves” is the applicable architecture choice.

A would instead validate the other design where a migration architect wants Azure VM sizing estimates based on actual source resource consumption. That evidence is consistent with run Azure Migrate assessments with performance-based sizing where supported, because performance assessments inform right-sized target capacity and cost estimates. It does not demonstrate that the target case achieves the dependency report identifies critical tier-to-tier connections used to plan waves.

C would instead validate the other design where a data-center company cannot accurately inventory its existing servers before an Azure move. That evidence is consistent with use Azure Migrate discovery for supported on-premises server estates, because discovery identifies workloads, guest details, and migration candidates. It does not demonstrate that the target case achieves the dependency report identifies critical tier-to-tier connections used to plan waves.

D would instead validate the other design where a company hosts PostgreSQL on-premises and needs a managed Azure PostgreSQL target with a controlled data cutover. That evidence is consistent with use the supported Azure Database for PostgreSQL migration service or documented migration method, because postgreSQL-targeted migration tools handle data transfer and cutover planning. It does not demonstrate that the target case achieves the dependency report identifies critical tier-to-tier connections used to plan waves.