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Measure vCore pressure, memory grants, data and log latency, tempdb contention, IOPS ceilings, disk caching, VM SKU caps and scaling windows. These checks primarily test Managed Instance and SQL VM compute storage scaling instead of the critical system behavior in this case. Examine: Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs.

Inventory SQL Agent, cross-database dependencies, instance features, latency, licensing, OS access and application connection requirements before choosing a target. These checks primarily test Choosing Azure SQL Database versus Managed Instance versus SQL VM instead of the critical system behavior in this case. Examine: Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs.

Inspect migration replication health, source change rate, lag, database compatibility, authentication, connection-string cutover and residual transaction state. These checks primarily test Online migration with controlled cutover and validation instead of the critical system behavior in this case. Examine: Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs.

Inspect monitor-server history, last backup/copied/restored times, log chain continuity, share access, standby mode and restore delay. This choice pertains to Log shipping health and recovery orchestration, not the needed response/evidence pair. Use: Use supported SQL Server failover cluster instances on Azure virtual machines with compatible shared storage and cluster quorum instead of treating FCIs as data-replication AGs. Check: Inspect shared disk or supported file-share topology, Windows cluster validation, storage permissions, listener name, resource ownership and node failover tests.

Build and monitor supported SQL Server Always On availability groups on Azure VMs and understand SQL MI link behavior, verifying cluster quorum, replica health, endpoint access and synchronization mode. This choice pertains to Always On availability groups on SQL Server VMs and SQL MI links, not the needed response/evidence pair. Use: Use supported SQL Server failover cluster instances on Azure virtual machines with compatible shared storage and cluster quorum instead of treating FCIs as data-replication AGs. Check: Inspect shared disk or supported file-share topology, Windows cluster validation, storage permissions, listener name, resource ownership and node failover tests.

A recoverable backup is stored under controlled access and a real restore exercise proves the chain, keys and application consistency requirements. This choice pertains to Backup to Azure Blob URL and tested cloud restore, not the needed response/evidence pair. Use: Use supported SQL Server failover cluster instances on Azure virtual machines with compatible shared storage and cluster quorum instead of treating FCIs as data-replication AGs. Check: Inspect shared disk or supported file-share topology, Windows cluster validation, storage permissions, listener name, resource ownership and node failover tests.

The intended app authenticates through the managed identity or Entra principal without shared SQL passwords and can perform only its permitted queries. This would validate Microsoft Entra authentication with contained service identity, but it does not establish the required outcome here. Verify instead: Approved users can execute only intended tables, views and procedures, while unauthorized writes and administrative operations fail reproducibly.

Low-trust users receive suitably masked results while sensitive storage and access controls still enforce separately tested privacy requirements. This would validate Dynamic data masking classification and permission differences, but it does not establish the required outcome here. Verify instead: Approved users can execute only intended tables, views and procedures, while unauthorized writes and administrative operations fail reproducibly.

Each authorized tenant sees only its records through SQL and API queries, while unauthorized predicate bypass attempts return no rows. This would validate Row-level security and ownership predicates, but it does not establish the required outcome here. Verify instead: Approved users can execute only intended tables, views and procedures, while unauthorized writes and administrative operations fail reproducibly.

Use ARM or Bicep to define Azure SQL servers, databases, identity, networking and policy settings in source-controlled templates with environment parameters and gated deployment checks. This addresses ARM and Bicep declarative Azure SQL provisioning, not the controlling requirement here. A valid ARM deployment may still produce unsafe security settings if templates use broad defaults or incorrect resource group/subscription targets. Prefer: Monitor Azure resource deployments and resolve failed dependencies or policy denials using logs and change review, with safe rollback plans for data-bearing SQL resources.

Use Azure SQL elastic jobs for supported scheduled T-SQL maintenance across Azure SQL Database targets, managing target groups, credentials and job execution state. This addresses Azure SQL Database elastic jobs for fleet maintenance, not the controlling requirement here. Elastic jobs are a separate managed job-agent service and should not be confused with SQL Server Agent jobs inside Azure SQL Database. Prefer: Monitor Azure resource deployments and resolve failed dependencies or policy denials using logs and change review, with safe rollback plans for data-bearing SQL resources.

Configure SQL Server Agent schedules and job-step security on supported SQL Server or Managed Instance platforms, separating service credentials from the database data permissions they need. This addresses SQL Server Agent schedules and job ownership, not the controlling requirement here. SQL Server Agent is supported on relevant instance-level offerings but is not the native scheduling mechanism inside a standalone Azure SQL Database. Prefer: Monitor Azure resource deployments and resolve failed dependencies or policy denials using logs and change review, with safe rollback plans for data-bearing SQL resources.

Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits. These checks primarily test Azure SQL Database service tiers and elastic pools instead of the critical system behavior in this case. Examine: Compare database offering capabilities, deployment parameters, identity, networking, Fabric integration support, source control and tenant governance before provisioning.

Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs. These checks primarily test Azure Arc-enabled SQL and hybrid fleet governance instead of the critical system behavior in this case. Examine: Compare database offering capabilities, deployment parameters, identity, networking, Fabric integration support, source control and tenant governance before provisioning.

Inventory SQL Agent, cross-database dependencies, instance features, latency, licensing, OS access and application connection requirements before choosing a target. These checks primarily test Choosing Azure SQL Database versus Managed Instance versus SQL VM instead of the critical system behavior in this case. Examine: Compare database offering capabilities, deployment parameters, identity, networking, Fabric integration support, source control and tenant governance before provisioning.

Configure database auditing for activity accountability, select Change Tracking or Change Data Capture for supported data-change synchronization, and use ledger only when tamper-evidence requirements justify it. This addresses Azure SQL auditing ledger and change tracking distinctions, not the controlling requirement here. SQL auditing, change tracking, CDC and ledger answer different questions; enabling one does not guarantee identical transaction history or immutable external evidence. Prefer: Configure supported Microsoft Entra authentication and database users or contained identities for Azure SQL, ensuring the application principal receives only required permissions.

Select Entra or SQL authentication deliberately for Azure SQL Database, Managed Instance and SQL Server VMs based on platform support, application constraints and security controls. This addresses SQL authentication versus Entra and managed-instance principals, not the controlling requirement here. A login defined in one SQL Server instance is not automatically a contained user in another database or a globally valid Entra identity. Prefer: Configure supported Microsoft Entra authentication and database users or contained identities for Azure SQL, ensuring the application principal receives only required permissions.

Implement Row-Level Security with a carefully tested predicate function and security policy for each tenant or business boundary, independent of form-level filtering. This addresses Row-level security and ownership predicates, not the controlling requirement here. Client-side WHERE filters are not a database security boundary, and incorrectly scoped predicates can leak cross-tenant data. Prefer: Configure supported Microsoft Entra authentication and database users or contained identities for Azure SQL, ensuring the application principal receives only required permissions.

The job runs on intended databases with controlled concurrency and job history that identifies any failed or skipped target for remediation. This choice pertains to Azure SQL Database elastic jobs for fleet maintenance, not the needed response/evidence pair. Use: Investigate SQL Agent job-step execution context, timing and dependencies before retrying, and make destructive maintenance operations safe for partial reruns. Check: Inspect step command, proxy token, connection defaults, deadlocks, Agent history output, last success timestamp and job rerun behavior.

Configure SQL Server Agent schedules and job-step security on supported SQL Server or Managed Instance platforms, separating service credentials from the database data permissions they need. This choice pertains to SQL Server Agent schedules and job ownership, not the needed response/evidence pair. Use: Investigate SQL Agent job-step execution context, timing and dependencies before retrying, and make destructive maintenance operations safe for partial reruns. Check: Inspect step command, proxy token, connection defaults, deadlocks, Agent history output, last success timestamp and job rerun behavior.

Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health. This choice pertains to Deployment monitoring rollback and drift reconciliation, not the needed response/evidence pair. Use: Investigate SQL Agent job-step execution context, timing and dependencies before retrying, and make destructive maintenance operations safe for partial reruns. Check: Inspect step command, proxy token, connection defaults, deadlocks, Agent history output, last success timestamp and job rerun behavior.

New environment deployments match approved SKU, networking and identities while Fabric access follows supported synchronization and permission boundaries. This would validate Azure SQL deployment automation and Fabric SQL integration boundary, but it does not establish the required outcome here. Verify instead: The chosen tier or pool absorbs expected peaks with measured response times while avoiding waste from persistently idle dedicated allocations.

Queries and retention maintenance operate on intended partitions with predictable execution plans, storage growth and acceptable CPU overhead. This would validate Table partitioning compression and scale-out tradeoffs, but it does not establish the required outcome here. Verify instead: The chosen tier or pool absorbs expected peaks with measured response times while avoiding waste from persistently idle dedicated allocations.

The deployed database meets compatibility and service-management requirements without unnecessary virtual-machine maintenance or unsupported instance features. This would validate Choosing Azure SQL Database versus Managed Instance versus SQL VM, but it does not establish the required outcome here. Verify instead: The chosen tier or pool absorbs expected peaks with measured response times while avoiding waste from persistently idle dedicated allocations.

Monitor Azure resource deployments and resolve failed dependencies or policy denials using logs and change review, with safe rollback plans for data-bearing SQL resources. This choice pertains to Deployment monitoring rollback and drift reconciliation, not the needed response/evidence pair. Use: Use ARM or Bicep to define Azure SQL servers, databases, identity, networking and policy settings in source-controlled templates with environment parameters and gated deployment checks. Check: Inspect template parameters, deployment scope, what-if output, module dependencies, identity role assignments and post-deploy effective configuration.

Inspect automation account managed identity, runbook schedule, Azure Monitor action group, job output, retry policy, resource locks and target database state. This choice pertains to Azure database task automation alerts and retries, not the needed response/evidence pair. Use: Use ARM or Bicep to define Azure SQL servers, databases, identity, networking and policy settings in source-controlled templates with environment parameters and gated deployment checks. Check: Inspect template parameters, deployment scope, what-if output, module dependencies, identity role assignments and post-deploy effective configuration.

The job runs on intended databases with controlled concurrency and job history that identifies any failed or skipped target for remediation. This choice pertains to Azure SQL Database elastic jobs for fleet maintenance, not the needed response/evidence pair. Use: Use ARM or Bicep to define Azure SQL servers, databases, identity, networking and policy settings in source-controlled templates with environment parameters and gated deployment checks. Check: Inspect template parameters, deployment scope, what-if output, module dependencies, identity role assignments and post-deploy effective configuration.

The automation updates only intended database resources and produces repeatable audit evidence of changes and failures without storing plaintext passwords. This would validate Azure PowerShell and CLI operations with scoped automation identity, but it does not establish the required outcome here. Verify instead: Repeatable deployments create the intended SQL resources in each subscription without silently broadening public network access or replacing production data.

The maintenance job executes on the approved cadence with least-privilege identity and traceable execution logs without depending on a personal admin login. This would validate SQL Server Agent schedules and job ownership, but it does not establish the required outcome here. Verify instead: Repeatable deployments create the intended SQL resources in each subscription without silently broadening public network access or replacing production data.

The job runs on intended databases with controlled concurrency and job history that identifies any failed or skipped target for remediation. This would validate Azure SQL Database elastic jobs for fleet maintenance, but it does not establish the required outcome here. Verify instead: Repeatable deployments create the intended SQL resources in each subscription without silently broadening public network access or replacing production data.

Compare database offering capabilities, deployment parameters, identity, networking, Fabric integration support, source control and tenant governance before provisioning. These checks primarily test Azure SQL deployment automation and Fabric SQL integration boundary instead of the critical system behavior in this case. Examine: Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits.

Measure vCore pressure, memory grants, data and log latency, tempdb contention, IOPS ceilings, disk caching, VM SKU caps and scaling windows. These checks primarily test Managed Instance and SQL VM compute storage scaling instead of the critical system behavior in this case. Examine: Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits.

Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs. These checks primarily test Azure Arc-enabled SQL and hybrid fleet governance instead of the critical system behavior in this case. Examine: Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits.