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Competing workloads respect their configured limits and critical requests retain service within documented platform and resource constraints. This choice pertains to Resource Governor and scoped compute configuration, not the needed response/evidence pair. Use: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time. Check: Review database watcher configuration and targets, query and instance samples, CPU, data/log IO, wait statistics, alert scope and time-series retention.

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: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time. Check: Review database watcher configuration and targets, query and instance samples, CPU, data/log IO, wait statistics, alert scope and time-series retention.

Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources. This choice pertains to SQL operational baseline across waits locks and storage, not the needed response/evidence pair. Use: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time. Check: Review database watcher configuration and targets, query and instance samples, CPU, data/log IO, wait statistics, alert scope and time-series retention.

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: New environment deployments match approved SKU, networking and identities while Fabric access follows supported synchronization and permission boundaries.

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: New environment deployments match approved SKU, networking and identities while Fabric access follows supported synchronization and permission boundaries.

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: New environment deployments match approved SKU, networking and identities while Fabric access follows supported synchronization and permission boundaries.

Approved applications can query protected columns with authorized client drivers while other SQL readers cannot obtain original plaintext directly. This would validate Always Encrypted client-side protection and enclaves, but it does not establish the required outcome here. Verify instead: Only approved SQL principals or application clients decrypt the intended data and key rotation retains tested recovery capability.

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: Only approved SQL principals or application clients decrypt the intended data and key rotation retains tested recovery capability.

Approved users can execute only intended tables, views and procedures, while unauthorized writes and administrative operations fail reproducibly. This would validate Least-privilege SQL roles and ownership chaining, but it does not establish the required outcome here. Verify instead: Only approved SQL principals or application clients decrypt the intended data and key rotation retains tested recovery capability.

Measure vCore pressure, memory grants, data and log latency, tempdb contention, IOPS ceilings, disk caching, VM SKU caps and scaling windows. This choice pertains to Managed Instance and SQL VM compute storage scaling, 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.

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.

The migrated service opens with consistent data, working identities and restored application workflows within the agreed maintenance period. This choice pertains to Offline migration and post-migration verification, 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.

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.

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 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.

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 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.

Configure supported active geo-replication or Azure SQL failover groups with clear primary-secondary roles, read-only routing, connection listeners and manual/automatic recovery procedures. This choice pertains to Azure SQL Database active geo replication and failover groups, not the needed response/evidence pair. Use: Build and monitor supported SQL Server Always On availability groups on Azure VMs and understand SQL MI link behavior, verifying cluster quorum, replica health, endpoint access and synchronization mode. Check: Inspect Windows Server failover cluster or supported Linux HA configuration, availability replica state, listener IP/DNS, synchronization health, AG endpoint port and failover readiness.

Inspect monitor-server history, last backup/copied/restored times, log chain continuity, share access, standby mode and restore delay. This choice pertains to Log shipping health and recovery orchestration, not the needed response/evidence pair. Use: Build and monitor supported SQL Server Always On availability groups on Azure VMs and understand SQL MI link behavior, verifying cluster quorum, replica health, endpoint access and synchronization mode. Check: Inspect Windows Server failover cluster or supported Linux HA configuration, availability replica state, listener IP/DNS, synchronization health, AG endpoint port and failover readiness.

The application reconnects through the intended endpoint after a tested supported failover, with documented possible data loss and read/write behavior. This choice pertains to Azure SQL failover groups versus active geo replication decisions, not the needed response/evidence pair. Use: Build and monitor supported SQL Server Always On availability groups on Azure VMs and understand SQL MI link behavior, verifying cluster quorum, replica health, endpoint access and synchronization mode. Check: Inspect Windows Server failover cluster or supported Linux HA configuration, availability replica state, listener IP/DNS, synchronization health, AG endpoint port and failover readiness.

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.

Approved applications can query protected columns with authorized client drivers while other SQL readers cannot obtain original plaintext directly. This would validate Always Encrypted client-side protection and enclaves, 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.

Approved users can execute only intended tables, views and procedures, while unauthorized writes and administrative operations fail reproducibly. This would validate Least-privilege SQL roles and ownership chaining, 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.

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 intended database replicas transition primary role as designed and application connections reach the listener without unsupported database or instance assumptions.

A complete chain can be restored to the required point using valid full, differential and sequential log backups with proven integrity. This would validate SQL Server native backup chain and recovery model, but it does not establish the required outcome here. Verify instead: The intended database replicas transition primary role as designed and application connections reach the listener without unsupported database or instance assumptions.

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 intended database replicas transition primary role as designed and application connections reach the listener without unsupported database or instance assumptions.

The application reconnects through the intended endpoint after a tested supported failover, with documented possible data loss and read/write behavior. This choice pertains to Azure SQL failover groups versus active geo replication decisions, not the needed response/evidence pair. Use: Build and monitor supported SQL Server Always On availability groups on Azure VMs and understand SQL MI link behavior, verifying cluster quorum, replica health, endpoint access and synchronization mode. Check: Inspect Windows Server failover cluster or supported Linux HA configuration, availability replica state, listener IP/DNS, synchronization health, AG endpoint port and failover readiness.

Inspect database recovery model, complete backup-chain LSNs, log backup cadence, backup checksums, database state and recovery target. This choice pertains to SQL Server native backup chain and recovery model, not the needed response/evidence pair. Use: Build and monitor supported SQL Server Always On availability groups on Azure VMs and understand SQL MI link behavior, verifying cluster quorum, replica health, endpoint access and synchronization mode. Check: Inspect Windows Server failover cluster or supported Linux HA configuration, availability replica state, listener IP/DNS, synchronization health, AG endpoint port and failover readiness.

Configure SQL Server log shipping across supported instances with reliable log backup, copy and restore schedules, delay and standby policies, then test manual recovery and ownership of jobs. This choice pertains to Log shipping health and recovery orchestration, not the needed response/evidence pair. Use: Build and monitor supported SQL Server Always On availability groups on Azure VMs and understand SQL MI link behavior, verifying cluster quorum, replica health, endpoint access and synchronization mode. Check: Inspect Windows Server failover cluster or supported Linux HA configuration, availability replica state, listener IP/DNS, synchronization health, AG endpoint port and failover readiness.

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: Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change.

Review database watcher configuration and targets, query and instance samples, CPU, data/log IO, wait statistics, alert scope and time-series retention. These checks primarily test Database watcher and Azure SQL monitoring baseline instead of the critical system behavior in this case. Examine: Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change.

Inspect event session predicates, event fields, ring buffer or event file targets, dropped events, blocked process reports and collection overhead. These checks primarily test Extended Events and workload-specific incident evidence instead of the critical system behavior in this case. Examine: Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change.

Use Azure SQL automated backups and configured point-in-time retention or long-term retention policy for supported managed offerings, choosing the correct restore workflow and retention costs. This choice pertains to Azure SQL automatic PITR and long-term retention, not the needed response/evidence pair. Use: Plan full, differential and transaction-log backups using appropriate SQL Server recovery model and restore order for the intended point-in-time objective. Check: Inspect database recovery model, complete backup-chain LSNs, log backup cadence, backup checksums, database state and recovery target.

After a planned or forced failover the intended database endpoint serves valid writes and read-only workloads under documented consistency limits. This choice pertains to Azure SQL Database active geo replication and failover groups, not the needed response/evidence pair. Use: Plan full, differential and transaction-log backups using appropriate SQL Server recovery model and restore order for the intended point-in-time objective. Check: Inspect database recovery model, complete backup-chain LSNs, log backup cadence, backup checksums, database state and recovery target.

Inspect shared disk or supported file-share topology, Windows cluster validation, storage permissions, listener name, resource ownership and node failover tests. This choice pertains to SQL Server failover cluster instances shared storage constraints, not the needed response/evidence pair. Use: Plan full, differential and transaction-log backups using appropriate SQL Server recovery model and restore order for the intended point-in-time objective. Check: Inspect database recovery model, complete backup-chain LSNs, log backup cadence, backup checksums, database state and recovery target.