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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 blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration.

Inspect metric namespace, resource dimensions, aggregation periods, diagnostic destinations, alert scope, delivery permissions, action groups and sample timestamps. These checks primarily test Azure Monitor alerts diagnostics and resource metrics instead of the critical system behavior in this case. Examine: Inspect blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration.

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 blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration.

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 addresses Azure PowerShell and CLI operations with scoped automation identity, not the controlling requirement here. Azure RBAC permissions to create an Azure SQL server do not imply arbitrary T-SQL data access to every user database. Prefer: 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.

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: 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.

Automate supported Azure SQL operational tasks with an appropriately scoped schedule or runbook and route failures into measurable alert channels while handling retries idempotently. This addresses Azure database task automation alerts and retries, not the controlling requirement here. An Azure automation runbook reporting successful execution does not prove its database command succeeded or that an alert was delivered. Prefer: 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.

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: 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.

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: 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.

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: 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.

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 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.

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 addresses Always On availability groups on SQL Server VMs and SQL MI links, not the controlling requirement here. An AG protects replicated user databases and requires cluster/listener configuration; it does not automatically synchronize every SQL Agent job or server-level login. Prefer: 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.

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. This addresses Azure SQL automatic PITR and long-term retention, not the controlling requirement here. Point-in-time restore usually creates a separate database and does not undo production writes in place; retention is constrained by service-specific limits. Prefer: 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.

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 choice pertains to Row-level security and ownership predicates, not the needed response/evidence pair. Use: 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. Check: Examine audit destinations, retention and access, captured event classes, Change Tracking version retention, CDC capture jobs, ledger digest verification and application expectations.

A previously failing user connects with the correct principal and performs only approved operations after a narrowly scoped corrective change. This choice pertains to Diagnosing login failure and group-based access, not the needed response/evidence pair. Use: 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. Check: Examine audit destinations, retention and access, captured event classes, Change Tracking version retention, CDC capture jobs, ledger digest verification and application expectations.

Inspect sensitivity labels, masking expressions, UNMASK permissions, privileged role membership and direct query behavior across application identities. This choice pertains to Dynamic data masking classification and permission differences, not the needed response/evidence pair. Use: 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. Check: Examine audit destinations, retention and access, captured event classes, Change Tracking version retention, CDC capture jobs, ledger digest verification and application expectations.

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 complete chain can be restored to the required point using valid full, differential and sequential log backups with proven integrity.

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: A complete chain can be restored to the required point using valid full, differential and sequential log backups with proven integrity.

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 complete chain can be restored to the required point using valid full, differential and sequential log backups with proven integrity.

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: Inspect backup URL, SAS or managed identity support by SQL version, container permissions, encryption/certificate material, backup headers and RESTORE VERIFYONLY caveats.

Inspect replication state, endpoint listeners, failover policy, allowed data loss, application reconnect errors, secondary region capacity and DNS cache behavior. These checks primarily test Azure SQL Database active geo replication and failover groups instead of the critical system behavior in this case. Examine: Inspect backup URL, SAS or managed identity support by SQL version, container permissions, encryption/certificate material, backup headers and RESTORE VERIFYONLY caveats.

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: Inspect backup URL, SAS or managed identity support by SQL version, container permissions, encryption/certificate material, backup headers and RESTORE VERIFYONLY caveats.

New environment deployments match approved SKU, networking and identities while Fabric access follows supported synchronization and permission boundaries. This choice pertains to Azure SQL deployment automation and Fabric SQL integration boundary, not the needed response/evidence pair. Use: 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. Check: Inspect migration replication health, source change rate, lag, database compatibility, authentication, connection-string cutover and residual transaction state.

Collect source version, compatibility level, blockers, logins, SQL Agent jobs, linked servers, TDE key dependencies, storage size and application integration map. This choice pertains to Azure SQL migration assessment and feature compatibility, not the needed response/evidence pair. Use: 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. Check: Inspect migration replication health, source change rate, lag, database compatibility, authentication, connection-string cutover and residual transaction state.

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: 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. Check: Inspect migration replication health, source change rate, lag, database compatibility, authentication, connection-string cutover and residual transaction state.

Enable supported Transparent Data Encryption and choose managed or customer-managed protectors based on compliance, rotating keys without losing the ability to decrypt database backups. This addresses Transparent data encryption keys and protector lifecycle, not the controlling requirement here. TDE encrypts storage at rest but does not hide column values from authorized SQL queries or replace end-to-end TLS. Prefer: Use Always Encrypted to protect selected columns with client-side cryptography and evaluate secure enclaves for supported confidential computations and richer query operations.

Design Azure SQL firewall policies and Private Link or approved service endpoints at the required scope, enforcing TLS and minimizing public endpoint exposure. This addresses Azure SQL private endpoints firewall and TLS boundary, not the controlling requirement here. A Private Endpoint does not provide correct DNS records automatically in every custom network; name resolution and access policy require separate verification. Prefer: Use Always Encrypted to protect selected columns with client-side cryptography and evaluate secure enclaves for supported confidential computations and richer query operations.

Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required. This addresses Dynamic data masking classification and permission differences, not the controlling requirement here. Dynamic Data Masking is not encryption or a complete defense against inference; administrators and UNMASK principals can still access original values. Prefer: Use Always Encrypted to protect selected columns with client-side cryptography and evaluate secure enclaves for supported confidential computations and richer query operations.

Operators detect regressions against representative baseline periods and can link database symptoms to trustworthy collected performance evidence. This choice pertains to Database watcher and Azure SQL monitoring baseline, not the needed response/evidence pair. Use: 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. Check: Inspect supported platform, classifier function, workload groups, CPU and memory caps, connection routing, database-scoped settings and compute metrics.

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: 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. Check: Inspect supported platform, classifier function, workload groups, CPU and memory caps, connection routing, database-scoped settings and compute metrics.

Use supported Azure SQL automatic tuning or IQP features with monitoring and safety controls; verify supported recommendations and their observed effect before treating them as universal fixes. This choice pertains to Automatic tuning and intelligent query processing guardrails, not the needed response/evidence pair. Use: 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. Check: Inspect supported platform, classifier function, workload groups, CPU and memory caps, connection routing, database-scoped settings and compute metrics.

Long blocking chains shorten and transaction correctness remains valid after safe tuning, with deadlock retry policy and consistency requirements addressed. This would validate Blocking deadlocks and isolation strategy, but it does not establish the required outcome here. Verify instead: Performance improvements persist under measured production workloads and failed recommendations are reversed or disabled according to policy.

A representative mix of small and large parameter executions achieves stable resource use, with intelligent feature effects monitored over time. This would validate Intelligent Insights and parameter-sensitive query analysis, but it does not establish the required outcome here. Verify instead: Performance improvements persist under measured production workloads and failed recommendations are reversed or disabled according to policy.

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: Performance improvements persist under measured production workloads and failed recommendations are reversed or disabled according to policy.