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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: Review actual execution plans, predicate sargability, cardinality estimates and suitable index designs before adding indexes or scaling hardware.

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: Review actual execution plans, predicate sargability, cardinality estimates and suitable index designs before adding indexes or scaling hardware.

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: Review actual execution plans, predicate sargability, cardinality estimates and suitable index designs before adding indexes or scaling hardware.

The identified bottleneck has a specific measured cause, and the chosen corrective action changes the relevant metric under representative load. This would validate SQL operational baseline across waits locks and storage, but it does not establish the required outcome here. Verify instead: Long blocking chains shorten and transaction correctness remains valid after safe tuning, with deadlock retry policy and consistency requirements addressed.

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: Long blocking chains shorten and transaction correctness remains valid after safe tuning, with deadlock retry policy and consistency requirements addressed.

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: Long blocking chains shorten and transaction correctness remains valid after safe tuning, with deadlock retry policy and consistency requirements addressed.

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 choice pertains to Database watcher and Azure SQL monitoring baseline, not the needed response/evidence pair. Use: 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. Check: Inspect metric namespace, resource dimensions, aggregation periods, diagnostic destinations, alert scope, delivery permissions, action groups and sample timestamps.

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: 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. Check: Inspect metric namespace, resource dimensions, aggregation periods, diagnostic destinations, alert scope, delivery permissions, action groups and sample timestamps.

A representative mix of small and large parameter executions achieves stable resource use, with intelligent feature effects monitored over time. This choice pertains to Intelligent Insights and parameter-sensitive query analysis, not the needed response/evidence pair. Use: 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. Check: Inspect metric namespace, resource dimensions, aggregation periods, diagnostic destinations, alert scope, delivery permissions, action groups and sample timestamps.

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 choice pertains to Azure Monitor alerts diagnostics and resource metrics, not the needed response/evidence pair. Use: Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources. Check: Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change.

Inspect blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration. This choice pertains to Blocking deadlocks and isolation strategy, not the needed response/evidence pair. Use: Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources. Check: Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change.

Queries use appropriate access paths under representative parameters while maintenance and write overhead remain acceptable. This choice pertains to Execution plans cardinality and indexing improvements, not the needed response/evidence pair. Use: Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources. Check: Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change.

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: Check Managed Instance region pairing, VNet/DNS dependencies, SQL MI link prerequisites where used, migration and failover sequence, replication health and authentication continuity.

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: Check Managed Instance region pairing, VNet/DNS dependencies, SQL MI link prerequisites where used, migration and failover sequence, replication health and authentication continuity.

Inspect shared disk or supported file-share topology, Windows cluster validation, storage permissions, listener name, resource ownership and node failover tests. These checks primarily test SQL Server failover cluster instances shared storage constraints instead of the critical system behavior in this case. Examine: Check Managed Instance region pairing, VNet/DNS dependencies, SQL MI link prerequisites where used, migration and failover sequence, replication health and authentication continuity.

Inspect blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration. These checks primarily test Blocking deadlocks and isolation strategy instead of the critical system behavior in this case. Examine: Inspect diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings.

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 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. These checks primarily test Resource Governor and scoped compute configuration instead of the critical system behavior in this case. Examine: Inspect diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings.

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 recovery objectives, replication modes, failure domains, backup frequencies, availability SLA, network and DNS cutover plus application dependency map.

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

Inspect replica role, readable secondary capacity, asynchronous replication lag, failover-group listener endpoint, DNS TTL and connection retry handling. These checks primarily test Azure SQL failover groups versus active geo replication decisions instead of the critical system behavior in this case. Examine: Inspect recovery objectives, replication modes, failure domains, backup frequencies, availability SLA, network and DNS cutover plus application dependency map.

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 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.

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 addresses Automatic tuning and intelligent query processing guardrails, not the controlling requirement here. Automatic tuning may need time to evaluate evidence and can reverse actions; feature availability is platform and compatibility-level dependent. Prefer: 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.

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 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.

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 replication state, endpoint listeners, failover policy, allowed data loss, application reconnect errors, secondary region capacity and DNS cache behavior.

Inspect shared disk or supported file-share topology, Windows cluster validation, storage permissions, listener name, resource ownership and node failover tests. These checks primarily test SQL Server failover cluster instances shared storage constraints instead of the critical system behavior in this case. Examine: Inspect replication state, endpoint listeners, failover policy, allowed data loss, application reconnect errors, secondary region capacity and DNS cache behavior.

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 replication state, endpoint listeners, failover policy, allowed data loss, application reconnect errors, secondary region capacity and DNS cache behavior.

Inspect login/user mapping, server principals, password policy, Entra admin configuration, directory identity, database containment and connection string authentication mode. This choice pertains to SQL authentication versus Entra and managed-instance principals, not the needed response/evidence pair. Use: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required. Check: Inspect sensitivity labels, masking expressions, UNMASK permissions, privileged role membership and direct query behavior across application identities.

Only approved SQL principals or application clients decrypt the intended data and key rotation retains tested recovery capability. This choice pertains to Object-level encryption and database cryptographic controls, not the needed response/evidence pair. Use: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required. Check: Inspect sensitivity labels, masking expressions, UNMASK permissions, privileged role membership and direct query behavior across application identities.

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: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required. Check: Inspect sensitivity labels, masking expressions, UNMASK permissions, privileged role membership and direct query behavior across application identities.