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Inspect database recovery model, complete backup-chain LSNs, log backup cadence, backup checksums, database state and recovery target. These checks primarily test SQL Server native backup chain and recovery model instead of the critical system behavior in this case. Examine: Examine failover runbook owner, replica health, DNS and application retry time, last confirmed transaction, validation queries and restore evidence.

Inspect recovery objectives, replication modes, failure domains, backup frequencies, availability SLA, network and DNS cutover plus application dependency map. These checks primarily test RPO RTO strategy and availability versus recovery instead of the critical system behavior in this case. Examine: Examine failover runbook owner, replica health, DNS and application retry time, last confirmed transaction, validation queries and restore evidence.

Check Managed Instance region pairing, VNet/DNS dependencies, SQL MI link prerequisites where used, migration and failover sequence, replication health and authentication continuity. These checks primarily test Managed Instance hybrid recovery and operational readiness instead of the critical system behavior in this case. Examine: Examine failover runbook owner, replica health, DNS and application retry time, last confirmed transaction, validation queries and restore evidence.

Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources. This addresses SQL operational baseline across waits locks and storage, not the controlling requirement here. One wait type alone is not a universal diagnosis; waits, plan behavior, request duration and resource data must be read together. Prefer: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night.

Use Resource Governor on supported SQL Server platforms for workload classification and resource controls, while choosing Azure SQL compute tier and database-scoped settings appropriately for PaaS. This addresses Resource Governor and scoped compute configuration, not the controlling requirement here. Resource Governor is not uniformly offered on every Azure SQL service, and workload isolation cannot be achieved simply by setting an unrelated database-scoped knob. Prefer: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night.

Diagnose blocked and deadlocked sessions using live DMVs and deadlock graphs, then fix transaction scope, indexing or isolation behavior with concurrency tests. This addresses Blocking deadlocks and isolation strategy, not the controlling requirement here. Raising an app timeout does not remove a blocking root cause; snapshot isolation can reduce some reader/writer contention but changes version-store behavior. Prefer: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night.

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: A documented exercise demonstrates recovery within measured time and data-loss objectives and reveals unmet application dependencies before production emergencies.

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: A documented exercise demonstrates recovery within measured time and data-loss objectives and reveals unmet application dependencies before production emergencies.

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: A documented exercise demonstrates recovery within measured time and data-loss objectives and reveals unmet application dependencies before production emergencies.

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: Queries and retention maintenance operate on intended partitions with predictable execution plans, storage growth and acceptable CPU overhead.

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: Queries and retention maintenance operate on intended partitions with predictable execution plans, storage growth and acceptable CPU overhead.

The chosen tier or pool absorbs expected peaks with measured response times while avoiding waste from persistently idle dedicated allocations. This would validate Azure SQL Database service tiers and elastic pools, but it does not establish the required outcome here. Verify instead: Queries and retention maintenance operate on intended partitions with predictable execution plans, storage growth and acceptable CPU overhead.

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.

Approved managed-instance failover or hybrid transition works after controlled traffic redirection, with required instance features and logins present. This would validate Managed Instance hybrid recovery and operational readiness, 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 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: Measured failover and restoration exercises meet agreed data loss and recovery time limits without confusing uptime redundancy with recoverability.

Configure SQL Server log shipping across supported instances with reliable log backup, copy and restore schedules, delay and standby policies, then test manual recovery and ownership of jobs. This addresses Log shipping health and recovery orchestration, not the controlling requirement here. Log shipping is backup-copy-restore orchestration, not synchronous automatic database failover or a substitute for manual application cutover. Prefer: Use Azure SQL automated backups and configured point-in-time retention or long-term retention policy for supported managed offerings, choosing the correct restore workflow and retention costs.

Back up supported SQL Server databases to Azure Blob storage through approved credential or managed identity patterns, then validate access, checksums and test restores from cloud targets. This addresses Backup to Azure Blob URL and tested cloud restore, not the controlling requirement here. A backup file's existence or a VERIFYONLY success does not replace a tested full restoration and end-to-end workload check. Prefer: Use Azure SQL automated backups and configured point-in-time retention or long-term retention policy for supported managed offerings, choosing the correct restore workflow and retention costs.

Select a supported HA and disaster recovery architecture from business-defined RPO, RTO, region failure assumptions, planned failover steps and service-specific capabilities. This addresses RPO RTO strategy and availability versus recovery, not the controlling requirement here. High availability reduces some outages but cannot substitute for recoverable backups after corruption or human deletion; RPO and RTO are separate targets. Prefer: Use Azure SQL automated backups and configured point-in-time retention or long-term retention policy for supported managed offerings, choosing the correct restore workflow and retention costs.

Examine audit destinations, retention and access, captured event classes, Change Tracking version retention, CDC capture jobs, ledger digest verification and application expectations. This choice pertains to Azure SQL auditing ledger and change tracking distinctions, not the needed response/evidence pair. Use: Distinguish authentication from authorization using SQL error context, Entra membership and database principal mapping before changing firewall or granting elevated roles. Check: Review sign-in error number and state, selected database, Entra token principal, group claims, CREATE USER scope, role membership and firewall reachability.

Grant database and object rights to explicit roles, distinguish server-level from database permissions, and avoid applying sysadmin or db_owner as a workaround for every access error. This choice pertains to Least-privilege SQL roles and ownership chaining, not the needed response/evidence pair. Use: Distinguish authentication from authorization using SQL error context, Entra membership and database principal mapping before changing firewall or granting elevated roles. Check: Review sign-in error number and state, selected database, Entra token principal, group claims, CREATE USER scope, role membership and firewall reachability.

Low-trust users receive suitably masked results while sensitive storage and access controls still enforce separately tested privacy requirements. This choice pertains to Dynamic data masking classification and permission differences, not the needed response/evidence pair. Use: Distinguish authentication from authorization using SQL error context, Entra membership and database principal mapping before changing firewall or granting elevated roles. Check: Review sign-in error number and state, selected database, Entra token principal, group claims, CREATE USER scope, role membership and firewall reachability.

Inspect job history return status, Database Mail profile, operator assignment, alert severity, retry steps, failure branching and delivery errors. These checks primarily test SQL Agent alerts operators and failure notifications instead of the critical system behavior in this case. Examine: Inspect SQL Agent service state, job owner, proxy or credential, step subsystem, schedules, permissions, execution history and target database.

Inspect automation account managed identity, runbook schedule, Azure Monitor action group, job output, retry policy, resource locks and target database state. These checks primarily test Azure database task automation alerts and retries instead of the critical system behavior in this case. Examine: Inspect SQL Agent service state, job owner, proxy or credential, step subsystem, schedules, permissions, execution history and target database.

Review subscription context, service principal or managed identity, Azure RBAC role, API response, database resource state and script error handling. These checks primarily test Azure PowerShell and CLI operations with scoped automation identity instead of the critical system behavior in this case. Examine: Inspect SQL Agent service state, job owner, proxy or credential, step subsystem, schedules, permissions, execution history and target database.

Use a current migration assessment to inventory database dependencies, unsupported features, downtime constraints and target platform compatibility before moving data. This addresses Azure SQL migration assessment and feature compatibility, not the controlling requirement here. Successful schema import does not prove that instance-level jobs, security identities and linked dependencies survive a migration. Prefer: 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.

Deploy governed Azure SQL resources with repeatable infrastructure templates and evaluate Azure SQL Database in Fabric for supported analytics-connected workflows rather than assuming all workloads are equivalent. This addresses Azure SQL deployment automation and Fabric SQL integration boundary, not the controlling requirement here. Using Fabric with Azure SQL does not remove operational security responsibilities or grant unsupported cross-service transactional semantics. Prefer: 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.

Size Managed Instance and SQL Server VM compute, storage latency and throughput using performance evidence, including correct storage-layout and platform support limits. This addresses Managed Instance and SQL VM compute storage scaling, not the controlling requirement here. Increasing CPU cannot fix a saturated log throughput budget or a storage path with high sustained write latency. Prefer: 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.

Inspect TDE state, Azure Key Vault or Managed HSM key permissions, protector version, key availability, rotation policy and restore dependencies. These checks primarily test Transparent data encryption keys and protector lifecycle instead of the critical system behavior in this case. Examine: Inspect login/user mapping, server principals, password policy, Entra admin configuration, directory identity, database containment and connection string authentication mode.

Inspect tenant and server Entra administrator settings, token audience, user mapping, group membership, service principal, role grants and sign-in failures. These checks primarily test Microsoft Entra authentication with contained service identity instead of the critical system behavior in this case. Examine: Inspect login/user mapping, server principals, password policy, Entra admin configuration, directory identity, database containment and connection string authentication mode.

Review key hierarchy, certificate availability, encrypted payload format, authorized decryption path, T-SQL function usage, rotation and backup procedures. These checks primarily test Object-level encryption and database cryptographic controls instead of the critical system behavior in this case. Examine: Inspect login/user mapping, server principals, password policy, Entra admin configuration, directory identity, database containment and connection string authentication mode.

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: A representative mix of small and large parameter executions achieves stable resource use, with intelligent feature effects monitored over time.

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: A representative mix of small and large parameter executions achieves stable resource use, with intelligent feature effects monitored over time.

Access paths and estimates remain healthy with acceptable maintenance duration, transaction log growth and production concurrency. This would validate Index and statistics maintenance with targeted cadence, but it does not establish the required outcome here. Verify instead: A representative mix of small and large parameter executions achieves stable resource use, with intelligent feature effects monitored over time.