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Queries use appropriate access paths under representative parameters while maintenance and write overhead remain acceptable. This choice pertains to Execution plans cardinality and indexing improvements, not the needed response/evidence pair. Use: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time. Check: Review database watcher configuration and targets, query and instance samples, CPU, data/log IO, wait statistics, alert scope and time-series retention.

Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows. This choice pertains to Index and statistics maintenance with targeted cadence, not the needed response/evidence pair. Use: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time. Check: Review database watcher configuration and targets, query and instance samples, CPU, data/log IO, wait statistics, alert scope and time-series retention.

Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources. This choice pertains to SQL operational baseline across waits locks and storage, not the needed response/evidence pair. Use: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time. Check: Review database watcher configuration and targets, query and instance samples, CPU, data/log IO, wait statistics, alert scope and time-series retention.

Correlate Azure SQL Intelligent Insights or Query Store evidence with parameter sensitivity and memory grant patterns, selecting documented plan or query changes instead of blindly upgrading SKU. This addresses Intelligent Insights and parameter-sensitive query analysis, not the controlling requirement here. Intelligent recommendations require contextual validation; SQL intelligent query processing features depend on engine version, database compatibility and workload shape. Prefer: Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources.

Configure supported metrics, diagnostic settings and alerts for Azure SQL resources to detect real performance or availability regressions with correct thresholds and missing-data treatment. This addresses Azure Monitor alerts diagnostics and resource metrics, not the controlling requirement here. A low CPU value does not mean an app is healthy; incorrect aggregation or missing telemetry can hide blocked sessions and failures. Prefer: Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources.

Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night. This addresses Index and statistics maintenance with targeted cadence, not the controlling requirement here. Small index fragmentation percentages do not automatically justify full rebuild; large rebuilds consume log space and can block workloads. Prefer: Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources.

Operators detect regressions against representative baseline periods and can link database symptoms to trustworthy collected performance evidence. This would validate Database watcher and Azure SQL monitoring baseline, but it does not establish the required outcome here. Verify instead: Alerts fire for meaningful sustained database risk while log delivery and missing-metric gaps are independently observable.

Performance improvements persist under measured production workloads and failed recommendations are reversed or disabled according to policy. This would validate Automatic tuning and intelligent query processing guardrails, but it does not establish the required outcome here. Verify instead: Alerts fire for meaningful sustained database risk while log delivery and missing-metric gaps are independently observable.

A problematic query recovers representative execution time without forcing a plan that fails under other parameter or schema conditions. This would validate Query Store plan regressions and forced plans, but it does not establish the required outcome here. Verify instead: Alerts fire for meaningful sustained database risk while log delivery and missing-metric gaps are independently observable.

Use Azure Arc-enabled SQL Server to inventory and govern supported SQL Server instances outside Azure without assuming Arc moves their databases or turns them into PaaS. This addresses Azure Arc-enabled SQL and hybrid fleet governance, not the controlling requirement here. Azure Arc projects management and governance into Azure; Arc onboarding is not equivalent to migration or replacing SQL Server backups and patching. Prefer: Use a current migration assessment to inventory database dependencies, unsupported features, downtime constraints and target platform compatibility before moving data.

Choose a supported online migration path with ongoing data synchronization when a tight outage window requires it, and plan explicit final cutover, validation and rollback checkpoints. This addresses Online migration with controlled cutover and validation, not the controlling requirement here. Online migration reduces downtime but is not automatically zero-downtime; final synchronization, traffic redirection and application tests still matter. Prefer: Use a current migration assessment to inventory database dependencies, unsupported features, downtime constraints and target platform compatibility before moving data.

Implement supported partition functions and aligned partition schemes for lifecycle-aware large tables, and choose row, page or columnstore compression only after measuring query and maintenance effects. This addresses Table partitioning compression and scale-out tradeoffs, not the controlling requirement here. Partitioning alone does not make every query faster; indexes and predicate alignment determine pruning and compression may trade CPU for IO. Prefer: Use a current migration assessment to inventory database dependencies, unsupported features, downtime constraints and target platform compatibility before moving data.

Use supported backup/restore, BACPAC or database-copy methods based on source/target compatibility and offline downtime budget, then validate security, consistency and performance after migration. This choice pertains to Offline migration and post-migration verification, not the needed response/evidence pair. Use: Use a current migration assessment to inventory database dependencies, unsupported features, downtime constraints and target platform compatibility before moving data. Check: Collect source version, compatibility level, blockers, logins, SQL Agent jobs, linked servers, TDE key dependencies, storage size and application integration map.

After resizing or tuning storage, relevant workload bottlenecks improve without breaching instance compatibility, availability or maintenance requirements. This choice pertains to Managed Instance and SQL VM compute storage scaling, not the needed response/evidence pair. Use: Use a current migration assessment to inventory database dependencies, unsupported features, downtime constraints and target platform compatibility before moving data. Check: Collect source version, compatibility level, blockers, logins, SQL Agent jobs, linked servers, TDE key dependencies, storage size and application integration map.

Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs. This choice pertains to Azure Arc-enabled SQL and hybrid fleet governance, not the needed response/evidence pair. Use: Use a current migration assessment to inventory database dependencies, unsupported features, downtime constraints and target platform compatibility before moving data. Check: Collect source version, compatibility level, blockers, logins, SQL Agent jobs, linked servers, TDE key dependencies, storage size and application integration map.

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: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required.

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: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required.

Configure supported Microsoft Entra authentication and database users or contained identities for Azure SQL, ensuring the application principal receives only required permissions. This addresses Microsoft Entra authentication with contained service identity, not the controlling requirement here. A successful token request is not sufficient if the database principal, scope or permissions are missing or the token targets the wrong resource. Prefer: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required.

Review database watcher configuration and targets, query and instance samples, CPU, data/log IO, wait statistics, alert scope and time-series retention. This choice pertains to Database watcher and Azure SQL monitoring baseline, not the needed response/evidence pair. Use: Configure supported metrics, diagnostic settings and alerts for Azure SQL resources to detect real performance or availability regressions with correct thresholds and missing-data treatment. Check: Inspect metric namespace, resource dimensions, aggregation periods, diagnostic destinations, alert scope, delivery permissions, action groups and sample timestamps.

Long blocking chains shorten and transaction correctness remains valid after safe tuning, with deadlock retry policy and consistency requirements addressed. This choice pertains to Blocking deadlocks and isolation strategy, not the needed response/evidence pair. Use: Configure supported metrics, diagnostic settings and alerts for Azure SQL resources to detect real performance or availability regressions with correct thresholds and missing-data treatment. Check: Inspect metric namespace, resource dimensions, aggregation periods, diagnostic destinations, alert scope, delivery permissions, action groups and sample timestamps.

Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources. This choice pertains to SQL operational baseline across waits locks and storage, not the needed response/evidence pair. Use: Configure supported metrics, diagnostic settings and alerts for Azure SQL resources to detect real performance or availability regressions with correct thresholds and missing-data treatment. Check: Inspect metric namespace, resource dimensions, aggregation periods, diagnostic destinations, alert scope, delivery permissions, action groups and sample timestamps.

A recoverable backup is stored under controlled access and a real restore exercise proves the chain, keys and application consistency requirements. This would validate Backup to Azure Blob URL and tested cloud restore, but it does not establish the required outcome here. Verify instead: Measured failover and restoration exercises meet agreed data loss and recovery time limits without confusing uptime redundancy with recoverability.

The application reconnects through the intended endpoint after a tested supported failover, with documented possible data loss and read/write behavior. This would validate Azure SQL failover groups versus active geo replication decisions, but it does not establish the required outcome here. Verify instead: Measured failover and restoration exercises meet agreed data loss and recovery time limits without confusing uptime redundancy with recoverability.

A documented exercise demonstrates recovery within measured time and data-loss objectives and reveals unmet application dependencies before production emergencies. This would validate HA DR tabletop and destructive failure testing, but it does not establish the required outcome here. Verify instead: Measured failover and restoration exercises meet agreed data loss and recovery time limits without confusing uptime redundancy with recoverability.

After a planned or forced failover the intended database endpoint serves valid writes and read-only workloads under documented consistency limits. This would validate Azure SQL Database active geo replication and failover groups, but it does not establish the required outcome here. Verify instead: The secondary replays the intended log sequence and can be recovered with documented RPO/RTO and acceptable data loss when primary fails.

The intended database replicas transition primary role as designed and application connections reach the listener without unsupported database or instance assumptions. This would validate Always On availability groups on SQL Server VMs and SQL MI links, but it does not establish the required outcome here. Verify instead: The secondary replays the intended log sequence and can be recovered with documented RPO/RTO and acceptable data loss when primary fails.

A complete chain can be restored to the required point using valid full, differential and sequential log backups with proven integrity. This would validate SQL Server native backup chain and recovery model, but it does not establish the required outcome here. Verify instead: The secondary replays the intended log sequence and can be recovered with documented RPO/RTO and acceptable data loss when primary fails.

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: The intended app authenticates through the managed identity or Entra principal without shared SQL passwords and can perform only its permitted queries.

Authorized workloads resolve the intended endpoint and connect over the required private or permitted path while unauthorized public sources are blocked. This would validate Azure SQL private endpoints firewall and TLS boundary, but it does not establish the required outcome here. Verify instead: The intended app authenticates through the managed identity or Entra principal without shared SQL passwords and can perform only its permitted queries.

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

The application reconnects through the intended endpoint after a tested supported failover, with documented possible data loss and read/write behavior. This would validate Azure SQL failover groups versus active geo replication decisions, but it does not establish the required outcome here. Verify instead: The SQL Server instance restarts on an eligible node using accessible shared data and log storage while the application resolves the cluster virtual network name.

The intended database replicas transition primary role as designed and application connections reach the listener without unsupported database or instance assumptions. This would validate Always On availability groups on SQL Server VMs and SQL MI links, but it does not establish the required outcome here. Verify instead: The SQL Server instance restarts on an eligible node using accessible shared data and log storage while the application resolves the cluster virtual network name.

The secondary replays the intended log sequence and can be recovered with documented RPO/RTO and acceptable data loss when primary fails. This would validate Log shipping health and recovery orchestration, but it does not establish the required outcome here. Verify instead: The SQL Server instance restarts on an eligible node using accessible shared data and log storage while the application resolves the cluster virtual network name.