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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 diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings.

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 diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings.

Inspect Query Store capture mode, runtime statistics, plan IDs, compatibility level, force-plan failure reasons and workload parameter distributions. These checks primarily test Query Store plan regressions and forced plans 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.

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: Each authorized tenant sees only its records through SQL and API queries, while unauthorized predicate bypass attempts return no rows.

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: Each authorized tenant sees only its records through SQL and API queries, while unauthorized predicate bypass attempts return no rows.

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: Each authorized tenant sees only its records through SQL and API queries, while unauthorized predicate bypass attempts return no rows.

Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change. This choice pertains to SQL operational baseline across waits locks and storage, not the needed response/evidence pair. Use: 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. Check: Check automatic tuning mode, automatic plan correction state, index create/drop recommendations, plan verification, workload compatibility level and rollback events.

Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night. This choice pertains to Index and statistics maintenance with targeted cadence, not the needed response/evidence pair. Use: 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. Check: Check automatic tuning mode, automatic plan correction state, index create/drop recommendations, plan verification, workload compatibility level and rollback events.

Long blocking chains shorten and transaction correctness remains valid after safe tuning, with deadlock retry policy and consistency requirements addressed. This choice pertains to Blocking deadlocks and isolation strategy, not the needed response/evidence pair. Use: 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. Check: Check automatic tuning mode, automatic plan correction state, index create/drop recommendations, plan verification, workload compatibility level and rollback events.

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

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 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. These checks primarily test Index and statistics maintenance with targeted cadence instead of the critical system behavior in this case. Examine: Inspect metric namespace, resource dimensions, aggregation periods, diagnostic destinations, alert scope, delivery permissions, action groups and sample timestamps.

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 addresses Log shipping health and recovery orchestration, not the controlling requirement here. Log shipping is backup-copy-restore orchestration, not synchronous automatic database failover or a substitute for manual application cutover. Prefer: Plan full, differential and transaction-log backups using appropriate SQL Server recovery model and restore order for the intended point-in-time objective.

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 addresses Azure SQL automatic PITR and long-term retention, not the controlling requirement here. Point-in-time restore usually creates a separate database and does not undo production writes in place; retention is constrained by service-specific limits. Prefer: Plan full, differential and transaction-log backups using appropriate SQL Server recovery model and restore order for the intended point-in-time objective.

Back up supported SQL Server databases to Azure Blob storage through approved credential or managed identity patterns, then validate access, checksums and test restores from cloud targets. This addresses Backup to Azure Blob URL and tested cloud restore, not the controlling requirement here. A backup file's existence or a VERIFYONLY success does not replace a tested full restoration and end-to-end workload check. Prefer: Plan full, differential and transaction-log backups using appropriate SQL Server recovery model and restore order for the intended point-in-time objective.

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 addresses Azure SQL Database active geo replication and failover groups, not the controlling requirement here. Geo-replication is normally asynchronous and does not guarantee all uncommitted or recently committed data survives a forced regional failover. Prefer: 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.

Test availability and recovery plans with realistic but controlled primary outages, application write workload, backups and rollback exercises before claiming a recovery objective. This addresses HA DR tabletop and destructive failure testing, not the controlling requirement here. A failover button clicking successfully is not proof the application, authentication, jobs and downstream services all recovered end to end. Prefer: 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.

Back up supported SQL Server databases to Azure Blob storage through approved credential or managed identity patterns, then validate access, checksums and test restores from cloud targets. This addresses Backup to Azure Blob URL and tested cloud restore, not the controlling requirement here. A backup file's existence or a VERIFYONLY success does not replace a tested full restoration and end-to-end workload check. Prefer: 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.

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. This choice pertains to Azure SQL auditing ledger and change tracking distinctions, not the needed response/evidence pair. Use: Implement Row-Level Security with a carefully tested predicate function and security policy for each tenant or business boundary, independent of form-level filtering. Check: Inspect SESSION_CONTEXT usage, predicate function logic, policy state, object permissions, execution plans and cross-tenant test accounts.

Approved applications can query protected columns with authorized client drivers while other SQL readers cannot obtain original plaintext directly. This choice pertains to Always Encrypted client-side protection and enclaves, not the needed response/evidence pair. Use: Implement Row-Level Security with a carefully tested predicate function and security policy for each tenant or business boundary, independent of form-level filtering. Check: Inspect SESSION_CONTEXT usage, predicate function logic, policy state, object permissions, execution plans and cross-tenant test accounts.

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: Implement Row-Level Security with a carefully tested predicate function and security policy for each tenant or business boundary, independent of form-level filtering. Check: Inspect SESSION_CONTEXT usage, predicate function logic, policy state, object permissions, execution plans and cross-tenant test accounts.

Inspect tenant and server Entra administrator settings, token audience, user mapping, group membership, service principal, role grants and sign-in failures. This choice pertains to Microsoft Entra authentication with contained service identity, not the needed response/evidence pair. Use: Use Always Encrypted to protect selected columns with client-side cryptography and evaluate secure enclaves for supported confidential computations and richer query operations. Check: Inspect encrypted column metadata, column master and encryption keys, driver feature support, enclave attestation where applicable and key store permissions.

Low-trust users receive suitably masked results while sensitive storage and access controls still enforce separately tested privacy requirements. This choice pertains to Dynamic data masking classification and permission differences, not the needed response/evidence pair. Use: Use Always Encrypted to protect selected columns with client-side cryptography and evaluate secure enclaves for supported confidential computations and richer query operations. Check: Inspect encrypted column metadata, column master and encryption keys, driver feature support, enclave attestation where applicable and key store permissions.

Enable supported Transparent Data Encryption and choose managed or customer-managed protectors based on compliance, rotating keys without losing the ability to decrypt database backups. This choice pertains to Transparent data encryption keys and protector lifecycle, not the needed response/evidence pair. Use: Use Always Encrypted to protect selected columns with client-side cryptography and evaluate secure enclaves for supported confidential computations and richer query operations. Check: Inspect encrypted column metadata, column master and encryption keys, driver feature support, enclave attestation where applicable and key store permissions.

Inspect template parameters, deployment scope, what-if output, module dependencies, identity role assignments and post-deploy effective configuration. This choice pertains to ARM and Bicep declarative Azure SQL provisioning, not the needed response/evidence pair. Use: Use Azure SQL elastic jobs for supported scheduled T-SQL maintenance across Azure SQL Database targets, managing target groups, credentials and job execution state. Check: Inspect elastic job agent setup, target server groups, database-scoped credentials, schedule, job version, execution history and permissions.

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 choice pertains to Azure database task automation alerts and retries, not the needed response/evidence pair. Use: Use Azure SQL elastic jobs for supported scheduled T-SQL maintenance across Azure SQL Database targets, managing target groups, credentials and job execution state. Check: Inspect elastic job agent setup, target server groups, database-scoped credentials, schedule, job version, execution history and permissions.

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: Use Azure SQL elastic jobs for supported scheduled T-SQL maintenance across Azure SQL Database targets, managing target groups, credentials and job execution state. Check: Inspect elastic job agent setup, target server groups, database-scoped credentials, schedule, job version, execution history and permissions.

Use a current migration assessment to inventory database dependencies, unsupported features, downtime constraints and target platform compatibility before moving data. This choice pertains to Azure SQL migration assessment and feature compatibility, not the needed response/evidence pair. Use: 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. Check: Inspect backup format, target offering limits, export size, authentication mapping, integrity checks, DBCC findings, workload baselines and cutover test results.

The deployed database meets compatibility and service-management requirements without unnecessary virtual-machine maintenance or unsupported instance features. This choice pertains to Choosing Azure SQL Database versus Managed Instance versus SQL VM, not the needed response/evidence pair. Use: 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. Check: Inspect backup format, target offering limits, export size, authentication mapping, integrity checks, DBCC findings, workload baselines and cutover test results.

Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits. This choice pertains to Azure SQL Database service tiers and elastic pools, not the needed response/evidence pair. Use: 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. Check: Inspect backup format, target offering limits, export size, authentication mapping, integrity checks, DBCC findings, workload baselines and cutover test results.

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: Competing workloads respect their configured limits and critical requests retain service within documented platform and resource constraints.

Access paths and estimates remain healthy with acceptable maintenance duration, transaction log growth and production concurrency. This would validate Index and statistics maintenance with targeted cadence, but it does not establish the required outcome here. Verify instead: Competing workloads respect their configured limits and critical requests retain service within documented platform and resource constraints.

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: Competing workloads respect their configured limits and critical requests retain service within documented platform and resource constraints.