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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: 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.

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 addresses Offline migration and post-migration verification, not the controlling requirement here. A BACPAC export captures schema and data for applicable databases but is not an instance backup that automatically recreates all server features and jobs. Prefer: 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.

Choose database vCore or DTU purchasing models, compute tier and elastic-pool membership from measured resource patterns, isolation needs and predictable workload peaks. This addresses Azure SQL Database service tiers and elastic pools, not the controlling requirement here. Elastic pools share resources across eligible Azure SQL databases; adding a pool cannot eliminate an individual database's workload or resource configuration limits. Prefer: 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 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 monitor-server history, last backup/copied/restored times, log chain continuity, share access, standby mode and restore delay.

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 monitor-server history, last backup/copied/restored times, log chain continuity, share access, standby mode and restore delay.

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 monitor-server history, last backup/copied/restored times, log chain continuity, share access, standby mode and restore delay.

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: Use Query Store to compare query runtime and plan history, identify a genuine plan regression, and force a known suitable plan only with ongoing validation and rollback.

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: Use Query Store to compare query runtime and plan history, identify a genuine plan regression, and force a known suitable plan only with ongoing validation and rollback.

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: Use Query Store to compare query runtime and plan history, identify a genuine plan regression, and force a known suitable plan only with ongoing validation and rollback.

Scheduled maintenance produces validated database results and on-call teams receive accurate failure notifications with safe recovery playbooks. This would validate Azure database task automation alerts and retries, but it does not establish the required outcome here. Verify instead: The maintenance job executes on the approved cadence with least-privilege identity and traceable execution logs without depending on a personal admin login.

The repaired scheduled job completes reliably and re-executing a partially completed workflow does not double-post or delete unintended data. This would validate SQL Agent job troubleshooting and idempotent maintenance, but it does not establish the required outcome here. Verify instead: The maintenance job executes on the approved cadence with least-privilege identity and traceable execution logs without depending on a personal admin login.

A failed deployment is diagnosed without deleting productive data, and the corrected infrastructure remains consistent with reviewed desired state. This would validate Deployment monitoring rollback and drift reconciliation, but it does not establish the required outcome here. Verify instead: The maintenance job executes on the approved cadence with least-privilege identity and traceable execution logs without depending on a personal admin login.

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: Measure vCore pressure, memory grants, data and log latency, tempdb contention, IOPS ceilings, disk caching, VM SKU caps and scaling windows.

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: Measure vCore pressure, memory grants, data and log latency, tempdb contention, IOPS ceilings, disk caching, VM SKU caps and scaling windows.

Inspect backup format, target offering limits, export size, authentication mapping, integrity checks, DBCC findings, workload baselines and cutover test results. These checks primarily test Offline migration and post-migration verification instead of the critical system behavior in this case. Examine: Measure vCore pressure, memory grants, data and log latency, tempdb contention, IOPS ceilings, disk caching, VM SKU caps and scaling windows.

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: 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.

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: 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.

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: 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.

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: Automate supported Azure SQL operational tasks with an appropriately scoped schedule or runbook and route failures into measurable alert channels while handling retries idempotently.

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: Automate supported Azure SQL operational tasks with an appropriately scoped schedule or runbook and route failures into measurable alert channels while handling retries idempotently.

Investigate SQL Agent job-step execution context, timing and dependencies before retrying, and make destructive maintenance operations safe for partial reruns. This addresses SQL Agent job troubleshooting and idempotent maintenance, not the controlling requirement here. Retrying a failed job without tracking committed work can create duplicate actions; application and SQL job state must be reconciled. Prefer: Automate supported Azure SQL operational tasks with an appropriately scoped schedule or runbook and route failures into measurable alert channels while handling retries idempotently.

The migration plan explicitly maps each source feature to a supported destination behavior and documented remediation before cutover. This would validate Azure SQL migration assessment and feature compatibility, but it does not establish the required outcome here. Verify instead: The deployed database meets compatibility and service-management requirements without unnecessary virtual-machine maintenance or unsupported instance features.

Administrators can view authorized hybrid SQL inventory and policy findings while database files remain in their existing managed locations. This would validate Azure Arc-enabled SQL and hybrid fleet governance, but it does not establish the required outcome here. Verify instead: The deployed database meets compatibility and service-management requirements without unnecessary virtual-machine maintenance or unsupported instance features.

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 deployed database meets compatibility and service-management requirements without unnecessary virtual-machine maintenance or unsupported instance features.

Choose database vCore or DTU purchasing models, compute tier and elastic-pool membership from measured resource patterns, isolation needs and predictable workload peaks. This choice pertains to Azure SQL Database service tiers and elastic pools, not the needed response/evidence pair. Use: 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. Check: Inspect predicate selectivity, partition elimination, split/merge plans, aligned indexes, compression CPU overhead and real data size reduction.

Administrators can view authorized hybrid SQL inventory and policy findings while database files remain in their existing managed locations. This choice pertains to Azure Arc-enabled SQL and hybrid fleet governance, not the needed response/evidence pair. Use: 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. Check: Inspect predicate selectivity, partition elimination, split/merge plans, aligned indexes, compression CPU overhead and real data size reduction.

Inventory SQL Agent, cross-database dependencies, instance features, latency, licensing, OS access and application connection requirements before choosing a target. This choice pertains to Choosing Azure SQL Database versus Managed Instance versus SQL VM, not the needed response/evidence pair. Use: 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. Check: Inspect predicate selectivity, partition elimination, split/merge plans, aligned indexes, compression CPU overhead and real data size reduction.

Inspect sensitivity labels, masking expressions, UNMASK permissions, privileged role membership and direct query behavior across application identities. These checks primarily test Dynamic data masking classification and permission differences instead of the critical system behavior in this case. Examine: Inspect server-level and database-level firewall rules, private endpoint approval and DNS, VNet/subnet routing, public network access status and certificate validation.

Inspect encrypted column metadata, column master and encryption keys, driver feature support, enclave attestation where applicable and key store permissions. These checks primarily test Always Encrypted client-side protection and enclaves instead of the critical system behavior in this case. Examine: Inspect server-level and database-level firewall rules, private endpoint approval and DNS, VNet/subnet routing, public network access status and certificate validation.

Inspect SESSION_CONTEXT usage, predicate function logic, policy state, object permissions, execution plans and cross-tenant test accounts. These checks primarily test Row-level security and ownership predicates instead of the critical system behavior in this case. Examine: Inspect server-level and database-level firewall rules, private endpoint approval and DNS, VNet/subnet routing, public network access status and certificate validation.

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.