A would instead validate the other design where an IoT business ingests millions of time-ordered device events from producers in real time. That evidence is consistent with use Azure Event Hubs as the streaming event-ingestion layer, because event Hubs provides high-throughput event streaming with consumer groups. It does not demonstrate that the target case achieves transformed lakehouse tables are queryable through the configured SQL endpoint.

B would instead validate the other design where an observability team needs fast analytics over very high-volume timestamped logs and telemetry. That evidence is consistent with use Azure Data Explorer for indexed time-series and log analytics, because azure Data Explorer is optimized for interactive exploration of large event datasets. It does not demonstrate that the target case achieves transformed lakehouse tables are queryable through the configured SQL endpoint.

C would instead validate the other design where an analyst must query Parquet files directly in a data lake using SQL without operating a dedicated cluster. That evidence is consistent with use an Azure Synapse serverless SQL pool for eligible file formats, because serverless SQL enables on-demand SQL queries over supported lake files. It does not demonstrate that the target case achieves transformed lakehouse tables are queryable through the configured SQL endpoint.

“Use policy-based Azure Backup and document its achievable RPO/RTO” relies on this Azure capability: backup restore is suitable when recovery objectives allow restore time and recovery-point age. This workload involves A manufacturing plant needs orchestrated failover of supported on-premises virtual machines into Azure during a site outage. The decisive Azure design mechanism is: aSR coordinates ongoing VM replication, test failover, and disaster recovery orchestration. Thus “Design an Azure Site Recovery replication and recovery plan for the supported hybrid VMs” is the applicable architecture choice.

“Design active-active multi-region application deployment with global routing” relies on this Azure capability: predeployed regional capacity reduces reliance on cold recovery for low RTO. This workload involves A manufacturing plant needs orchestrated failover of supported on-premises virtual machines into Azure during a site outage. The decisive Azure design mechanism is: aSR coordinates ongoing VM replication, test failover, and disaster recovery orchestration. Thus “Design an Azure Site Recovery replication and recovery plan for the supported hybrid VMs” is the applicable architecture choice.

“Enable blob versioning on the eligible storage account” relies on this Azure capability: versioning preserves prior object versions when a blob is changed. This workload involves A manufacturing plant needs orchestrated failover of supported on-premises virtual machines into Azure during a site outage. The decisive Azure design mechanism is: aSR coordinates ongoing VM replication, test failover, and disaster recovery orchestration. Thus “Design an Azure Site Recovery replication and recovery plan for the supported hybrid VMs” is the applicable architecture choice.

A would instead validate the other design where an online business cannot tolerate a regional outage and needs its web tier already running in two regions. That evidence is consistent with design active-active multi-region application deployment with global routing, because predeployed regional capacity reduces reliance on cold recovery for low RTO. It does not demonstrate that the target case achieves a non-disruptive test failover brings the designated machines up in Azure in the right sequence.

C would instead validate the other design where a small internal VM workload can tolerate several hours of recovery and a restore from a nightly recovery point. That evidence is consistent with use policy-based Azure Backup and document its achievable RPO/RTO, because backup restore is suitable when recovery objectives allow restore time and recovery-point age. It does not demonstrate that the target case achieves a non-disruptive test failover brings the designated machines up in Azure in the right sequence.

D would instead validate the other design where a design agency must recover a previously overwritten version of a supported Azure blob. That evidence is consistent with enable blob versioning on the eligible storage account, because versioning preserves prior object versions when a blob is changed. It does not demonstrate that the target case achieves a non-disruptive test failover brings the designated machines up in Azure in the right sequence.

“Perform a supported alternate-location Azure VM restore into a recovery test environment” relies on this Azure capability: alternate restore enables validation without overwriting the running production VM. This workload involves An online business cannot tolerate a regional outage and needs its web tier already running in two regions. The decisive Azure design mechanism is: predeployed regional capacity reduces reliance on cold recovery for low RTO. Thus “Design active-active multi-region application deployment with global routing” is the applicable architecture choice.

“Use Azure SQL failover groups with geo-replicated databases where appropriate” relies on this Azure capability: a failover group enables managed connection routing across geo-secondary databases. This workload involves An online business cannot tolerate a regional outage and needs its web tier already running in two regions. The decisive Azure design mechanism is: predeployed regional capacity reduces reliance on cold recovery for low RTO. Thus “Design active-active multi-region application deployment with global routing” is the applicable architecture choice.

“Use policy-based Azure Backup and document its achievable RPO/RTO” relies on this Azure capability: backup restore is suitable when recovery objectives allow restore time and recovery-point age. This workload involves An online business cannot tolerate a regional outage and needs its web tier already running in two regions. The decisive Azure design mechanism is: predeployed regional capacity reduces reliance on cold recovery for low RTO. Thus “Design active-active multi-region application deployment with global routing” is the applicable architecture choice.

A would instead validate the other design where a small internal VM workload can tolerate several hours of recovery and a restore from a nightly recovery point. That evidence is consistent with use policy-based Azure Backup and document its achievable RPO/RTO, because backup restore is suitable when recovery objectives allow restore time and recovery-point age. It does not demonstrate that the target case achieves traffic is routed to a healthy second-region application when the first is unavailable.

B would instead validate the other design where a company requires recovery of an Azure SQL database after a region-level failure with a predefined failover mechanism. That evidence is consistent with use Azure SQL failover groups with geo-replicated databases where appropriate, because a failover group enables managed connection routing across geo-secondary databases. It does not demonstrate that the target case achieves traffic is routed to a healthy second-region application when the first is unavailable.

C would instead validate the other design where a platform engineer must recover a VM into a separate isolated network for periodic evidence collection. That evidence is consistent with perform a supported alternate-location Azure VM restore into a recovery test environment, because alternate restore enables validation without overwriting the running production VM. It does not demonstrate that the target case achieves traffic is routed to a healthy second-region application when the first is unavailable.

“Use Azure SQL failover groups with geo-replicated databases where appropriate” relies on this Azure capability: a failover group enables managed connection routing across geo-secondary databases. This workload involves A proposed Azure workload has the following constraint: A small internal VM workload can tolerate several hours of recovery and a restore from a nightly recovery point. The decisive Azure design mechanism is: backup restore is suitable when recovery objectives allow restore time and recovery-point age. Thus “Use policy-based Azure Backup and document its achievable RPO/RTO” is the applicable architecture choice.

“Enable blob soft delete with the required retention” relies on this Azure capability: soft delete retains recoverable deleted blob data during the configured window. This workload involves A proposed Azure workload has the following constraint: A small internal VM workload can tolerate several hours of recovery and a restore from a nightly recovery point. The decisive Azure design mechanism is: backup restore is suitable when recovery objectives allow restore time and recovery-point age. Thus “Use policy-based Azure Backup and document its achievable RPO/RTO” is the applicable architecture choice.

“Select the DR architecture by quantifying application RTO and RPO before choosing the technology” relies on this Azure capability: recovery time and data-loss tolerance determine whether standby replication or backups fit. This workload involves A proposed Azure workload has the following constraint: A small internal VM workload can tolerate several hours of recovery and a restore from a nightly recovery point. The decisive Azure design mechanism is: backup restore is suitable when recovery objectives allow restore time and recovery-point age. Thus “Use policy-based Azure Backup and document its achievable RPO/RTO” is the applicable architecture choice.

A would instead validate the other design where a company requires recovery of an Azure SQL database after a region-level failure with a predefined failover mechanism. That evidence is consistent with use Azure SQL failover groups with geo-replicated databases where appropriate, because a failover group enables managed connection routing across geo-secondary databases. It does not demonstrate that the target case achieves a timed restore test meets the stated hours-long recovery objective.

C would instead validate the other design where a warehouse system needs a consistent design decision between near-continuous replication and once-a-day restore. That evidence is consistent with select the DR architecture by quantifying application RTO and RPO before choosing the technology, because recovery time and data-loss tolerance determine whether standby replication or backups fit. It does not demonstrate that the target case achieves a timed restore test meets the stated hours-long recovery objective.

D would instead validate the other design where a company needs a recovery window for accidentally deleted objects in its blob container. That evidence is consistent with enable blob soft delete with the required retention, because soft delete retains recoverable deleted blob data during the configured window. It does not demonstrate that the target case achieves a timed restore test meets the stated hours-long recovery objective.

“Select the DR architecture by quantifying application RTO and RPO before choosing the technology” relies on this Azure capability: recovery time and data-loss tolerance determine whether standby replication or backups fit. This workload involves During a cloud design workshop, stakeholders explain: A company requires recovery of an Azure SQL database after a region-level failure with a predefined failover mechanism. The decisive Azure design mechanism is: a failover group enables managed connection routing across geo-secondary databases. Thus “Use Azure SQL failover groups with geo-replicated databases where appropriate” is the applicable architecture choice.

“Configure Azure VM backup for application-consistent recovery points where supported” relies on this Azure capability: application-aware snapshots coordinate guest state for recoverability. This workload involves During a cloud design workshop, stakeholders explain: A company requires recovery of an Azure SQL database after a region-level failure with a predefined failover mechanism. The decisive Azure design mechanism is: a failover group enables managed connection routing across geo-secondary databases. Thus “Use Azure SQL failover groups with geo-replicated databases where appropriate” is the applicable architecture choice.

“Design an Azure Site Recovery replication and recovery plan for the supported hybrid VMs” relies on this Azure capability: aSR coordinates ongoing VM replication, test failover, and disaster recovery orchestration. This workload involves During a cloud design workshop, stakeholders explain: A company requires recovery of an Azure SQL database after a region-level failure with a predefined failover mechanism. The decisive Azure design mechanism is: a failover group enables managed connection routing across geo-secondary databases. Thus “Use Azure SQL failover groups with geo-replicated databases where appropriate” is the applicable architecture choice.

A would instead validate the other design where a manufacturing plant needs orchestrated failover of supported on-premises virtual machines into Azure during a site outage. That evidence is consistent with design an Azure Site Recovery replication and recovery plan for the supported hybrid VMs, because aSR coordinates ongoing VM replication, test failover, and disaster recovery orchestration. It does not demonstrate that the target case achieves the failover endpoint connects to the secondary after a controlled failover drill.

B would instead validate the other design where a company has application-consistent database-backed VM workloads and needs appropriately timed backup snapshots. That evidence is consistent with configure Azure VM backup for application-consistent recovery points where supported, because application-aware snapshots coordinate guest state for recoverability. It does not demonstrate that the target case achieves the failover endpoint connects to the secondary after a controlled failover drill.

C would instead validate the other design where a warehouse system needs a consistent design decision between near-continuous replication and once-a-day restore. That evidence is consistent with select the DR architecture by quantifying application RTO and RPO before choosing the technology, because recovery time and data-loss tolerance determine whether standby replication or backups fit. It does not demonstrate that the target case achieves the failover endpoint connects to the secondary after a controlled failover drill.

“Design active-active multi-region application deployment with global routing” relies on this Azure capability: predeployed regional capacity reduces reliance on cold recovery for low RTO. This workload involves A warehouse system needs a consistent design decision between near-continuous replication and once-a-day restore. The decisive Azure design mechanism is: recovery time and data-loss tolerance determine whether standby replication or backups fit. Thus “Select the DR architecture by quantifying application RTO and RPO before choosing the technology” is the applicable architecture choice.

“Design Azure Blob point-in-time restore with the required change feed and versioning prerequisites” relies on this Azure capability: point-in-time restore reverses supported blob operations to an earlier state. This workload involves A warehouse system needs a consistent design decision between near-continuous replication and once-a-day restore. The decisive Azure design mechanism is: recovery time and data-loss tolerance determine whether standby replication or backups fit. Thus “Select the DR architecture by quantifying application RTO and RPO before choosing the technology” is the applicable architecture choice.

“Design an Azure Site Recovery replication and recovery plan for the supported hybrid VMs” relies on this Azure capability: aSR coordinates ongoing VM replication, test failover, and disaster recovery orchestration. This workload involves A warehouse system needs a consistent design decision between near-continuous replication and once-a-day restore. The decisive Azure design mechanism is: recovery time and data-loss tolerance determine whether standby replication or backups fit. Thus “Select the DR architecture by quantifying application RTO and RPO before choosing the technology” is the applicable architecture choice.

A would instead validate the other design where a finance department wants a storage-account-level rollback of eligible block blobs to a prior timestamp. That evidence is consistent with design Azure Blob point-in-time restore with the required change feed and versioning prerequisites, because point-in-time restore reverses supported blob operations to an earlier state. It does not demonstrate that the target case achieves a recovery rehearsal demonstrates both the time to restore service and the age of restored data.

C would instead validate the other design where an online business cannot tolerate a regional outage and needs its web tier already running in two regions. That evidence is consistent with design active-active multi-region application deployment with global routing, because predeployed regional capacity reduces reliance on cold recovery for low RTO. It does not demonstrate that the target case achieves a recovery rehearsal demonstrates both the time to restore service and the age of restored data.

D would instead validate the other design where a manufacturing plant needs orchestrated failover of supported on-premises virtual machines into Azure during a site outage. That evidence is consistent with design an Azure Site Recovery replication and recovery plan for the supported hybrid VMs, because aSR coordinates ongoing VM replication, test failover, and disaster recovery orchestration. It does not demonstrate that the target case achieves a recovery rehearsal demonstrates both the time to restore service and the age of restored data.