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Review sign-in error number and state, selected database, Entra token principal, group claims, CREATE USER scope, role membership and firewall reachability. This choice pertains to Diagnosing login failure and group-based access, not the needed response/evidence pair. Use: Configure supported Microsoft Entra authentication and database users or contained identities for Azure SQL, ensuring the application principal receives only required permissions. Check: Inspect tenant and server Entra administrator settings, token audience, user mapping, group membership, service principal, role grants and sign-in failures.

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 choice pertains to SQL authentication versus Entra and managed-instance principals, not the needed response/evidence pair. Use: Configure supported Microsoft Entra authentication and database users or contained identities for Azure SQL, ensuring the application principal receives only required permissions. Check: Inspect tenant and server Entra administrator settings, token audience, user mapping, group membership, service principal, role grants and sign-in failures.

Authorized workloads resolve the intended endpoint and connect over the required private or permitted path while unauthorized public sources are blocked. This choice pertains to Azure SQL private endpoints firewall and TLS boundary, not the needed response/evidence pair. Use: Configure supported Microsoft Entra authentication and database users or contained identities for Azure SQL, ensuring the application principal receives only required permissions. Check: Inspect tenant and server Entra administrator settings, token audience, user mapping, group membership, service principal, role grants and sign-in failures.

New environment deployments match approved SKU, networking and identities while Fabric access follows supported synchronization and permission boundaries. This would validate Azure SQL deployment automation and Fabric SQL integration boundary, but it does not establish the required outcome here. Verify instead: After resizing or tuning storage, relevant workload bottlenecks improve without breaching instance compatibility, availability or maintenance requirements.

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: After resizing or tuning storage, relevant workload bottlenecks improve without breaching instance compatibility, availability or maintenance requirements.

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: After resizing or tuning storage, relevant workload bottlenecks improve without breaching instance compatibility, availability or maintenance requirements.

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

New environment deployments match approved SKU, networking and identities while Fabric access follows supported synchronization and permission boundaries. This would validate Azure SQL deployment automation and Fabric SQL integration boundary, but it does not establish the required outcome here. Verify instead: The migrated service opens with consistent data, working identities and restored application workflows within the agreed maintenance period.

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: The migrated service opens with consistent data, working identities and restored application workflows within the agreed maintenance period.

Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs. These checks primarily test Azure Arc-enabled SQL and hybrid fleet governance instead of the critical system behavior in this case. Examine: Collect source version, compatibility level, blockers, logins, SQL Agent jobs, linked servers, TDE key dependencies, storage size and application integration map.

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: Collect source version, compatibility level, blockers, logins, SQL Agent jobs, linked servers, TDE key dependencies, storage size and application integration map.

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: Collect source version, compatibility level, blockers, logins, SQL Agent jobs, linked servers, TDE key dependencies, storage size and application integration map.

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: Repeatable deployments create the intended SQL resources in each subscription without silently broadening public network access or replacing production data.

A real job failure triggers one useful notification to the right team and its history records sufficient context for a safe repair or retry. This would validate SQL Agent alerts operators and failure notifications, but it does not establish the required outcome here. Verify instead: Repeatable deployments create the intended SQL resources in each subscription without silently broadening public network access or replacing production data.

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: Repeatable deployments create the intended SQL resources in each subscription without silently broadening public network access or replacing production data.

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 elastic job agent setup, target server groups, database-scoped credentials, schedule, job version, execution history and permissions.

Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health. These checks primarily test Deployment monitoring rollback and drift reconciliation instead of the critical system behavior in this case. Examine: Inspect elastic job agent setup, target server groups, database-scoped credentials, schedule, job version, execution history and permissions.

Inspect SQL Agent service state, job owner, proxy or credential, step subsystem, schedules, permissions, execution history and target database. These checks primarily test SQL Server Agent schedules and job ownership instead of the critical system behavior in this case. Examine: Inspect elastic job agent setup, target server groups, database-scoped credentials, schedule, job version, execution history and permissions.

Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time. This addresses Database watcher and Azure SQL monitoring baseline, not the controlling requirement here. Database watcher and Azure Monitor observe resource activity but do not by themselves repair a bad execution plan or guarantee metrics are complete. Prefer: 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.

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. This addresses Query Store plan regressions and forced plans, not the controlling requirement here. Forcing a historical plan can mitigate a regression but does not make poor statistics or parameter sensitivity disappear and may fail after schema changes. Prefer: 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.

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: 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.

Inspect Windows Server failover cluster or supported Linux HA configuration, availability replica state, listener IP/DNS, synchronization health, AG endpoint port and failover readiness. These checks primarily test Always On availability groups on SQL Server VMs and SQL MI links instead of the critical system behavior in this case. Examine: Inspect database recovery model, complete backup-chain LSNs, log backup cadence, backup checksums, database state and recovery target.

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 database recovery model, complete backup-chain LSNs, log backup cadence, backup checksums, database state and recovery target.

Inspect backup URL, SAS or managed identity support by SQL version, container permissions, encryption/certificate material, backup headers and RESTORE VERIFYONLY caveats. These checks primarily test Backup to Azure Blob URL and tested cloud restore instead of the critical system behavior in this case. Examine: Inspect database recovery model, complete backup-chain LSNs, log backup cadence, backup checksums, database state and recovery target.

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: After resizing or tuning storage, relevant workload bottlenecks improve without breaching instance compatibility, availability or maintenance requirements.

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: After resizing or tuning storage, relevant workload bottlenecks improve without breaching instance compatibility, availability or maintenance requirements.

The deployed database meets compatibility and service-management requirements without unnecessary virtual-machine maintenance or unsupported instance features. This would validate Choosing Azure SQL Database versus Managed Instance versus SQL VM, but it does not establish the required outcome here. Verify instead: After resizing or tuning storage, relevant workload bottlenecks improve without breaching instance compatibility, availability or maintenance requirements.

Inspect supported platform, classifier function, workload groups, CPU and memory caps, connection routing, database-scoped settings and compute metrics. These checks primarily test Resource Governor and scoped compute configuration 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 diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings. These checks primarily test Intelligent Insights and parameter-sensitive query analysis 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.

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 Azure SQL elastic jobs for supported scheduled T-SQL maintenance across Azure SQL Database targets, managing target groups, credentials and job execution state.

Configure SQL Server Agent schedules and job-step security on supported SQL Server or Managed Instance platforms, separating service credentials from the database data permissions they need. This addresses SQL Server Agent schedules and job ownership, not the controlling requirement here. SQL Server Agent is supported on relevant instance-level offerings but is not the native scheduling mechanism inside a standalone Azure SQL Database. Prefer: Use Azure SQL elastic jobs for supported scheduled T-SQL maintenance across Azure SQL Database targets, managing target groups, credentials and job execution state.

Configure SQL Server Agent notifications, operators and error handling so failed backups and maintenance tasks generate actionable alerts without leaking sensitive details. This addresses SQL Agent alerts operators and failure notifications, not the controlling requirement here. A green schedule and enabled operator are not evidence that email delivery or failure handling works unless the notification path is tested. Prefer: Use Azure SQL elastic jobs for supported scheduled T-SQL maintenance across Azure SQL Database targets, managing target groups, credentials and job execution state.