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Check automatic tuning mode, automatic plan correction state, index create/drop recommendations, plan verification, workload compatibility level and rollback events. These checks primarily test Automatic tuning and intelligent query processing guardrails instead of the critical system behavior in this case. Examine: Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows.

Inspect Query Store capture mode, runtime statistics, plan IDs, compatibility level, force-plan failure reasons and workload parameter distributions. These checks primarily test Query Store plan regressions and forced plans instead of the critical system behavior in this case. Examine: Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows.

Inspect scans/seeks, key lookup counts, implicit conversions, estimated-versus-actual rows, missing-index recommendations and write amplification. These checks primarily test Execution plans cardinality and indexing improvements instead of the critical system behavior in this case. Examine: Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows.

Measured failover and restoration exercises meet agreed data loss and recovery time limits without confusing uptime redundancy with recoverability. This would validate RPO RTO strategy and availability versus recovery, but it does not establish the required outcome here. Verify instead: Approved managed-instance failover or hybrid transition works after controlled traffic redirection, with required instance features and logins present.

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. This would validate SQL Server failover cluster instances shared storage constraints, but it does not establish the required outcome here. Verify instead: Approved managed-instance failover or hybrid transition works after controlled traffic redirection, with required instance features and logins present.

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: Approved managed-instance failover or hybrid transition works after controlled traffic redirection, with required instance features and logins present.

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 destination is consistent at an accepted cutover point, applications use the intended endpoint, and rollback criteria are documented before retirement of source.

After resizing or tuning storage, relevant workload bottlenecks improve without breaching instance compatibility, availability or maintenance requirements. This would validate Managed Instance and SQL VM compute storage scaling, but it does not establish the required outcome here. Verify instead: The destination is consistent at an accepted cutover point, applications use the intended endpoint, and rollback criteria are documented before retirement of source.

The migrated service opens with consistent data, working identities and restored application workflows within the agreed maintenance period. This would validate Offline migration and post-migration verification, but it does not establish the required outcome here. Verify instead: The destination is consistent at an accepted cutover point, applications use the intended endpoint, and rollback criteria are documented before retirement of source.

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. This choice pertains to SQL Server failover cluster instances shared storage constraints, not the needed response/evidence pair. Use: 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. Check: Inspect Windows Server failover cluster or supported Linux HA configuration, availability replica state, listener IP/DNS, synchronization health, AG endpoint port and failover readiness.

Inspect replica role, readable secondary capacity, asynchronous replication lag, failover-group listener endpoint, DNS TTL and connection retry handling. This choice pertains to Azure SQL failover groups versus active geo replication decisions, not the needed response/evidence pair. Use: 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. Check: Inspect Windows Server failover cluster or supported Linux HA configuration, availability replica state, listener IP/DNS, synchronization health, AG endpoint port and failover readiness.

A documented exercise demonstrates recovery within measured time and data-loss objectives and reveals unmet application dependencies before production emergencies. This choice pertains to HA DR tabletop and destructive failure testing, not the needed response/evidence pair. Use: 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. Check: Inspect Windows Server failover cluster or supported Linux HA configuration, availability replica state, listener IP/DNS, synchronization health, AG endpoint port and failover readiness.

The chosen tier or pool absorbs expected peaks with measured response times while avoiding waste from persistently idle dedicated allocations. This choice pertains to Azure SQL Database service tiers and elastic pools, 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.

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 choice pertains to Online migration with controlled cutover and validation, 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.

The repaired scheduled job completes reliably and re-executing a partially completed workflow does not double-post or delete unintended data. This choice pertains to SQL Agent job troubleshooting and idempotent maintenance, not the needed response/evidence pair. Use: 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. Check: Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health.

Inspect SQL Agent service state, job owner, proxy or credential, step subsystem, schedules, permissions, execution history and target database. This choice pertains to SQL Server Agent schedules and job ownership, not the needed response/evidence pair. Use: 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. Check: Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health.

Use Az PowerShell or Azure CLI in authenticated deployment workflows with the minimum Azure RBAC rights and explicit resource/subscription selection to manage approved SQL resources. This choice pertains to Azure PowerShell and CLI operations with scoped automation identity, not the needed response/evidence pair. Use: 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. Check: Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health.

Competing workloads respect their configured limits and critical requests retain service within documented platform and resource constraints. This choice pertains to Resource Governor and scoped compute configuration, 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.

Inspect blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration. This choice pertains to Blocking deadlocks and isolation strategy, 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.

The deployed database meets compatibility and service-management requirements without unnecessary virtual-machine maintenance or unsupported instance features. This choice pertains to Choosing Azure SQL Database versus Managed Instance versus SQL VM, not the needed response/evidence pair. Use: Choose database vCore or DTU purchasing models, compute tier and elastic-pool membership from measured resource patterns, isolation needs and predictable workload peaks. Check: Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits.

Inspect backup format, target offering limits, export size, authentication mapping, integrity checks, DBCC findings, workload baselines and cutover test results. This choice pertains to Offline migration and post-migration verification, not the needed response/evidence pair. Use: Choose database vCore or DTU purchasing models, compute tier and elastic-pool membership from measured resource patterns, isolation needs and predictable workload peaks. Check: Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits.

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 choice pertains to Table partitioning compression and scale-out tradeoffs, not the needed response/evidence pair. Use: Choose database vCore or DTU purchasing models, compute tier and elastic-pool membership from measured resource patterns, isolation needs and predictable workload peaks. Check: Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits.

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: Investigators obtain the intended activity or row-change evidence, while tampering or synchronization gaps are detected by the correct feature-specific controls.

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: Investigators obtain the intended activity or row-change evidence, while tampering or synchronization gaps are detected by the correct feature-specific controls.

Only approved SQL principals or application clients decrypt the intended data and key rotation retains tested recovery capability. This would validate Object-level encryption and database cryptographic controls, but it does not establish the required outcome here. Verify instead: Investigators obtain the intended activity or row-change evidence, while tampering or synchronization gaps are detected by the correct feature-specific controls.

Inspect replica role, readable secondary capacity, asynchronous replication lag, failover-group listener endpoint, DNS TTL and connection retry handling. These checks primarily test Azure SQL failover groups versus active geo replication decisions instead of the critical system behavior in this case. Examine: Inspect replication state, endpoint listeners, failover policy, allowed data loss, application reconnect errors, secondary region capacity and DNS cache behavior.

Inspect monitor-server history, last backup/copied/restored times, log chain continuity, share access, standby mode and restore delay. These checks primarily test Log shipping health and recovery orchestration instead of the critical system behavior in this case. Examine: Inspect replication state, endpoint listeners, failover policy, allowed data loss, application reconnect errors, secondary region capacity and DNS cache behavior.

Check service tier, available restore points, retention setting, logical server and database permissions, LTR vault policy and restore destination. These checks primarily test Azure SQL automatic PITR and long-term retention instead of the critical system behavior in this case. Examine: Inspect replication state, endpoint listeners, failover policy, allowed data loss, application reconnect errors, secondary region capacity and DNS cache behavior.

Competing workloads respect their configured limits and critical requests retain service within documented platform and resource constraints. This choice pertains to Resource Governor and scoped compute configuration, 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.

Check automatic tuning mode, automatic plan correction state, index create/drop recommendations, plan verification, workload compatibility level and rollback events. This choice pertains to Automatic tuning and intelligent query processing guardrails, 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.