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

Inspect diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings. These checks primarily test Intelligent Insights and parameter-sensitive query analysis 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.

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.

Check effective permission using EXECUTE AS, user/database role membership, ownership chain, DENY overrides, T-SQL GRANT statements and precise failed operation. This choice pertains to Least-privilege SQL roles and ownership chaining, not the needed response/evidence pair. Use: 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. Check: Inspect TDE state, Azure Key Vault or Managed HSM key permissions, protector version, key availability, rotation policy and restore dependencies.

Protect targeted sensitive objects through supported SQL encryption functions, key hierarchy and application threat model while distinguishing column encryption from network and storage encryption. This choice pertains to Object-level encryption and database cryptographic controls, not the needed response/evidence pair. Use: 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. Check: Inspect TDE state, Azure Key Vault or Managed HSM key permissions, protector version, key availability, rotation policy and restore dependencies.

Investigators obtain the intended activity or row-change evidence, while tampering or synchronization gaps are detected by the correct feature-specific controls. This choice pertains to Azure SQL auditing ledger and change tracking distinctions, not the needed response/evidence pair. Use: 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. Check: Inspect TDE state, Azure Key Vault or Managed HSM key permissions, protector version, key availability, rotation policy and restore dependencies.

Inspect metric namespace, resource dimensions, aggregation periods, diagnostic destinations, alert scope, delivery permissions, action groups and sample timestamps. These checks primarily test Azure Monitor alerts diagnostics and resource metrics instead of the critical system behavior in this case. Examine: 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: Check automatic tuning mode, automatic plan correction state, index create/drop recommendations, plan verification, workload compatibility level and rollback events.

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: Check automatic tuning mode, automatic plan correction state, index create/drop recommendations, plan verification, workload compatibility level and rollback events.

Inspect migration replication health, source change rate, lag, database compatibility, authentication, connection-string cutover and residual transaction state. These checks primarily test Online migration with controlled cutover and validation instead of the critical system behavior in this case. Examine: Measure vCore pressure, memory grants, data and log latency, tempdb contention, IOPS ceilings, disk caching, VM SKU caps and scaling windows.

Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits. These checks primarily test Azure SQL Database service tiers and elastic pools instead of the critical system behavior in this case. Examine: Measure vCore pressure, memory grants, data and log latency, tempdb contention, IOPS ceilings, disk caching, VM SKU caps and scaling windows.

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: Measure vCore pressure, memory grants, data and log latency, tempdb contention, IOPS ceilings, disk caching, VM SKU caps and scaling windows.

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: The intended app authenticates through the managed identity or Entra principal without shared SQL passwords and can perform only its permitted queries.

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: The intended app authenticates through the managed identity or Entra principal without shared SQL passwords and can perform only its permitted queries.

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: The intended app authenticates through the managed identity or Entra principal without shared SQL passwords and can perform only its permitted queries.

Scheduled maintenance produces validated database results and on-call teams receive accurate failure notifications with safe recovery playbooks. This would validate Azure database task automation alerts and retries, but it does not establish the required outcome here. Verify instead: The job runs on intended databases with controlled concurrency and job history that identifies any failed or skipped target for remediation.

The automation updates only intended database resources and produces repeatable audit evidence of changes and failures without storing plaintext passwords. This would validate Azure PowerShell and CLI operations with scoped automation identity, but it does not establish the required outcome here. Verify instead: The job runs on intended databases with controlled concurrency and job history that identifies any failed or skipped target for remediation.

The maintenance job executes on the approved cadence with least-privilege identity and traceable execution logs without depending on a personal admin login. This would validate SQL Server Agent schedules and job ownership, but it does not establish the required outcome here. Verify instead: The job runs on intended databases with controlled concurrency and job history that identifies any failed or skipped target for remediation.

Grant database and object rights to explicit roles, distinguish server-level from database permissions, and avoid applying sysadmin or db_owner as a workaround for every access error. This addresses Least-privilege SQL roles and ownership chaining, not the controlling requirement here. GRANT on one schema or stored procedure does not automatically confer arbitrary permissions on all underlying cross-database resources. Prefer: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required.

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: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required.

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: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required.

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: Plan full, differential and transaction-log backups using appropriate SQL Server recovery model and restore order for the intended point-in-time objective.

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: Plan full, differential and transaction-log backups using appropriate SQL Server recovery model and restore order for the intended point-in-time objective.

Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health. These checks primarily test Deployment monitoring rollback and drift reconciliation instead of the critical system behavior in this case. Examine: Review subscription context, service principal or managed identity, Azure RBAC role, API response, database resource state and script error handling.

Inspect job history return status, Database Mail profile, operator assignment, alert severity, retry steps, failure branching and delivery errors. These checks primarily test SQL Agent alerts operators and failure notifications instead of the critical system behavior in this case. Examine: Review subscription context, service principal or managed identity, Azure RBAC role, API response, database resource state and script error handling.

Inspect step command, proxy token, connection defaults, deadlocks, Agent history output, last success timestamp and job rerun behavior. These checks primarily test SQL Agent job troubleshooting and idempotent maintenance instead of the critical system behavior in this case. Examine: Review subscription context, service principal or managed identity, Azure RBAC role, API response, database resource state and script error handling.

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: Choose Azure SQL failover groups or active geo-replication according to group-level endpoint continuity, supported replication topology, read scale and application reconnect requirements.

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: Choose Azure SQL failover groups or active geo-replication according to group-level endpoint continuity, supported replication topology, read scale and application reconnect requirements.

Assess Managed Instance service tiers, built-in availability, failover groups and supported SQL Managed Instance link scenarios for hybrid resilience without inventing unsupported topologies. This addresses Managed Instance hybrid recovery and operational readiness, not the controlling requirement here. Managed Instance includes built-in platform availability features, but customer responsibilities still include cross-region recovery plans and application connection design. Prefer: Choose Azure SQL failover groups or active geo-replication according to group-level endpoint continuity, supported replication topology, read scale and application reconnect requirements.

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

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: 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.

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: 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.