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Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health. This choice pertains to Deployment monitoring rollback and drift reconciliation, not the needed response/evidence pair. Use: Automate supported Azure SQL operational tasks with an appropriately scoped schedule or runbook and route failures into measurable alert channels while handling retries idempotently. Check: Inspect automation account managed identity, runbook schedule, Azure Monitor action group, job output, retry policy, resource locks and target database state.

The automation updates only intended database resources and produces repeatable audit evidence of changes and failures without storing plaintext passwords. This choice pertains to Azure PowerShell and CLI operations with scoped automation identity, not the needed response/evidence pair. Use: Automate supported Azure SQL operational tasks with an appropriately scoped schedule or runbook and route failures into measurable alert channels while handling retries idempotently. Check: Inspect automation account managed identity, runbook schedule, Azure Monitor action group, job output, retry policy, resource locks and target database state.

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 choice pertains to Azure SQL Database elastic jobs for fleet maintenance, not the needed response/evidence pair. Use: Automate supported Azure SQL operational tasks with an appropriately scoped schedule or runbook and route failures into measurable alert channels while handling retries idempotently. Check: Inspect automation account managed identity, runbook schedule, Azure Monitor action group, job output, retry policy, resource locks and target database state.

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: The secondary replays the intended log sequence and can be recovered with documented RPO/RTO and acceptable data loss when primary fails.

The intended database replicas transition primary role as designed and application connections reach the listener without unsupported database or instance assumptions. This would validate Always On availability groups on SQL Server VMs and SQL MI links, but it does not establish the required outcome here. Verify instead: The secondary replays the intended log sequence and can be recovered with documented RPO/RTO and acceptable data loss when primary fails.

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: The secondary replays the intended log sequence and can be recovered with documented RPO/RTO and acceptable data loss when primary fails.

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 choice pertains to Resource Governor and scoped compute configuration, not the needed response/evidence pair. Use: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night. Check: Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows.

Check automatic tuning mode, automatic plan correction state, index create/drop recommendations, plan verification, workload compatibility level and rollback events. This choice pertains to Automatic tuning and intelligent query processing guardrails, not the needed response/evidence pair. Use: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night. Check: Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows.

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: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night. Check: Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows.

The identified bottleneck has a specific measured cause, and the chosen corrective action changes the relevant metric under representative load. This choice pertains to SQL operational baseline across waits locks and storage, not the needed response/evidence pair. Use: 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. Check: Inspect Query Store capture mode, runtime statistics, plan IDs, compatibility level, force-plan failure reasons and workload parameter distributions.

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 choice pertains to Intelligent Insights and parameter-sensitive query analysis, not the needed response/evidence pair. Use: 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. Check: Inspect Query Store capture mode, runtime statistics, plan IDs, compatibility level, force-plan failure reasons and workload parameter distributions.

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: 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. Check: Inspect Query Store capture mode, runtime statistics, plan IDs, compatibility level, force-plan failure reasons and workload parameter distributions.

Measure vCore pressure, memory grants, data and log latency, tempdb contention, IOPS ceilings, disk caching, VM SKU caps and scaling windows. These checks primarily test Managed Instance and SQL VM compute storage scaling instead of the critical system behavior in this case. Examine: Inventory SQL Agent, cross-database dependencies, instance features, latency, licensing, OS access and application connection requirements before choosing a target.

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: Inventory SQL Agent, cross-database dependencies, instance features, latency, licensing, OS access and application connection requirements before choosing a target.

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: Inventory SQL Agent, cross-database dependencies, instance features, latency, licensing, OS access and application connection requirements before choosing a target.

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: 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.

After resizing or tuning storage, relevant workload bottlenecks improve without breaching instance compatibility, availability or maintenance requirements. This choice pertains to Managed Instance and SQL VM compute storage scaling, 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.

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 TDE state, Azure Key Vault or Managed HSM key permissions, protector version, key availability, rotation policy and restore dependencies.

Review key hierarchy, certificate availability, encrypted payload format, authorized decryption path, T-SQL function usage, rotation and backup procedures. These checks primarily test Object-level encryption and database cryptographic controls instead of the critical system behavior in this case. Examine: Inspect TDE state, Azure Key Vault or Managed HSM key permissions, protector version, key availability, rotation policy and restore dependencies.

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 TDE state, Azure Key Vault or Managed HSM key permissions, protector version, key availability, rotation policy and restore dependencies.

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: Diagnose blocked and deadlocked sessions using live DMVs and deadlock graphs, then fix transaction scope, indexing or isolation behavior with concurrency tests. Check: Inspect blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration.

The identified bottleneck has a specific measured cause, and the chosen corrective action changes the relevant metric under representative load. This choice pertains to SQL operational baseline across waits locks and storage, not the needed response/evidence pair. Use: Diagnose blocked and deadlocked sessions using live DMVs and deadlock graphs, then fix transaction scope, indexing or isolation behavior with concurrency tests. Check: Inspect blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration.

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 choice pertains to Intelligent Insights and parameter-sensitive query analysis, not the needed response/evidence pair. Use: Diagnose blocked and deadlocked sessions using live DMVs and deadlock graphs, then fix transaction scope, indexing or isolation behavior with concurrency tests. Check: Inspect blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration.

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 choice pertains to Azure Arc-enabled SQL and hybrid fleet governance, not the needed response/evidence pair. Use: Deploy governed Azure SQL resources with repeatable infrastructure templates and evaluate Azure SQL Database in Fabric for supported analytics-connected workflows rather than assuming all workloads are equivalent. Check: Compare database offering capabilities, deployment parameters, identity, networking, Fabric integration support, source control and tenant governance before provisioning.

Inspect backup format, target offering limits, export size, authentication mapping, integrity checks, DBCC findings, workload baselines and cutover test results. This choice pertains to Offline migration and post-migration verification, not the needed response/evidence pair. Use: Deploy governed Azure SQL resources with repeatable infrastructure templates and evaluate Azure SQL Database in Fabric for supported analytics-connected workflows rather than assuming all workloads are equivalent. Check: Compare database offering capabilities, deployment parameters, identity, networking, Fabric integration support, source control and tenant governance before provisioning.

Queries and retention maintenance operate on intended partitions with predictable execution plans, storage growth and acceptable CPU overhead. This choice pertains to Table partitioning compression and scale-out tradeoffs, not the needed response/evidence pair. Use: Deploy governed Azure SQL resources with repeatable infrastructure templates and evaluate Azure SQL Database in Fabric for supported analytics-connected workflows rather than assuming all workloads are equivalent. Check: Compare database offering capabilities, deployment parameters, identity, networking, Fabric integration support, source control and tenant governance before provisioning.

Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change. These checks primarily test SQL operational baseline across waits locks and storage instead of the critical system behavior in this case. Examine: Inspect Query Store capture mode, runtime statistics, plan IDs, compatibility level, force-plan failure reasons and workload parameter distributions.

Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows. These checks primarily test Index and statistics maintenance with targeted cadence instead of the critical system behavior in this case. Examine: Inspect Query Store capture mode, runtime statistics, plan IDs, compatibility level, force-plan failure reasons and workload parameter distributions.

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 Query Store capture mode, runtime statistics, plan IDs, compatibility level, force-plan failure reasons and workload parameter distributions.

The secondary replays the intended log sequence and can be recovered with documented RPO/RTO and acceptable data loss when primary fails. This would validate Log shipping health and recovery orchestration, but it does not establish the required outcome here. Verify instead: 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.

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: 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.

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: 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.