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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.

A restore to the required historical point completes into a supported database target and data plus dependent identities are validated. This would validate Azure SQL automatic PITR and long-term retention, 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.

After a planned or forced failover the intended database endpoint serves valid writes and read-only workloads under documented consistency limits. This would validate Azure SQL Database active geo replication and failover groups, 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 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 database recovery model, complete backup-chain LSNs, log backup cadence, backup checksums, database state and recovery target.

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 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.

Database files and supported backups remain encrypted with accessible protectors, and rotation or failover does not block required recovery operations. This would validate Transparent data encryption keys and protector lifecycle, but it does not establish the required outcome here. Verify instead: Authorized workloads resolve the intended endpoint and connect over the required private or permitted path while unauthorized public sources are blocked.

Low-trust users receive suitably masked results while sensitive storage and access controls still enforce separately tested privacy requirements. This would validate Dynamic data masking classification and permission differences, but it does not establish the required outcome here. Verify instead: Authorized workloads resolve the intended endpoint and connect over the required private or permitted path while unauthorized public sources are blocked.

Investigators obtain the intended activity or row-change evidence, while tampering or synchronization gaps are detected by the correct feature-specific controls. This would validate Azure SQL auditing ledger and change tracking distinctions, but it does not establish the required outcome here. Verify instead: Authorized workloads resolve the intended endpoint and connect over the required private or permitted path while unauthorized public sources are blocked.

Configure supported Microsoft Entra authentication and database users or contained identities for Azure SQL, ensuring the application principal receives only required permissions. This addresses Microsoft Entra authentication with contained service identity, not the controlling requirement here. A successful token request is not sufficient if the database principal, scope or permissions are missing or the token targets the wrong resource. Prefer: 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.

Distinguish authentication from authorization using SQL error context, Entra membership and database principal mapping before changing firewall or granting elevated roles. This addresses Diagnosing login failure and group-based access, not the controlling requirement here. Network allow rules cannot repair a database permission denial, and a valid directory group may still lack an associated database user. Prefer: 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.

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 addresses Row-level security and ownership predicates, not the controlling requirement here. Client-side WHERE filters are not a database security boundary, and incorrectly scoped predicates can leak cross-tenant data. Prefer: 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.

Review sign-in error number and state, selected database, Entra token principal, group claims, CREATE USER scope, role membership and firewall reachability. These checks primarily test Diagnosing login failure and group-based access instead of the critical system behavior in this case. Examine: Check effective permission using EXECUTE AS, user/database role membership, ownership chain, DENY overrides, T-SQL GRANT statements and precise failed operation.

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: Check effective permission using EXECUTE AS, user/database role membership, ownership chain, DENY overrides, T-SQL GRANT statements and precise failed operation.

Examine audit destinations, retention and access, captured event classes, Change Tracking version retention, CDC capture jobs, ledger digest verification and application expectations. These checks primarily test Azure SQL auditing ledger and change tracking distinctions instead of the critical system behavior in this case. Examine: Check effective permission using EXECUTE AS, user/database role membership, ownership chain, DENY overrides, T-SQL GRANT statements and precise failed operation.

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: 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.

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: 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.

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: 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.

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: 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.

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 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.

Review actual execution plans, predicate sargability, cardinality estimates and suitable index designs before adding indexes or scaling hardware. This addresses Execution plans cardinality and indexing improvements, not the controlling requirement here. A missing-index suggestion is a starting hypothesis, not a license to create every suggested index; SARGable predicates and accurate estimates matter. 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.

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: Queries use appropriate access paths under representative parameters while maintenance and write overhead remain acceptable.

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: Queries use appropriate access paths under representative parameters while maintenance and write overhead remain acceptable.

Alerts fire for meaningful sustained database risk while log delivery and missing-metric gaps are independently observable. This would validate Azure Monitor alerts diagnostics and resource metrics, but it does not establish the required outcome here. Verify instead: Queries use appropriate access paths under representative parameters while maintenance and write overhead remain acceptable.

Investigators obtain the intended activity or row-change evidence, while tampering or synchronization gaps are detected by the correct feature-specific controls. This would validate Azure SQL auditing ledger and change tracking distinctions, but it does not establish the required outcome here. Verify instead: Low-trust users receive suitably masked results while sensitive storage and access controls still enforce separately tested privacy requirements.

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: Low-trust users receive suitably masked results while sensitive storage and access controls still enforce separately tested privacy requirements.

Clients establish the intended authentication method without insecure credential sharing, and failed sign-ins can be tied to the correct principal and database. This would validate SQL authentication versus Entra and managed-instance principals, but it does not establish the required outcome here. Verify instead: Low-trust users receive suitably masked results while sensitive storage and access controls still enforce separately tested privacy requirements.

Use Extended Events sessions to capture targeted SQL events and wait/blocking evidence with bounded collection and a safe target rather than enabling excessive full-query traces in production. This addresses Extended Events and workload-specific incident evidence, not the controlling requirement here. Extended Events supports focused diagnostic collection; an unfiltered high-volume session can itself increase production overhead and leak query text. Prefer: 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.

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: 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.

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: 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.

Alerts fire for meaningful sustained database risk while log delivery and missing-metric gaps are independently observable. This choice pertains to Azure Monitor alerts diagnostics and resource metrics, not the needed response/evidence pair. Use: 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. Check: Inspect diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings.

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 choice pertains to Query Store plan regressions and forced plans, not the needed response/evidence pair. Use: 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. Check: Inspect diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings.

Inspect supported platform, classifier function, workload groups, CPU and memory caps, connection routing, database-scoped settings and compute metrics. This choice pertains to Resource Governor and scoped compute configuration, not the needed response/evidence pair. Use: 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. Check: Inspect diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings.