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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.

A failed deployment is diagnosed without deleting productive data, and the corrected infrastructure remains consistent with reviewed desired state. This would validate Deployment monitoring rollback and drift reconciliation, but it does not establish the required outcome here. Verify instead: A real job failure triggers one useful notification to the right team and its history records sufficient context for a safe repair or retry.

The repaired scheduled job completes reliably and re-executing a partially completed workflow does not double-post or delete unintended data. This would validate SQL Agent job troubleshooting and idempotent maintenance, but it does not establish the required outcome here. Verify instead: A real job failure triggers one useful notification to the right team and its history records sufficient context for a safe repair or retry.

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: A real job failure triggers one useful notification to the right team and its history records sufficient context for a safe repair or retry.

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 application reconnects through the intended endpoint after a tested supported failover, with documented possible data loss and read/write behavior.

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 application reconnects through the intended endpoint after a tested supported failover, with documented possible data loss and read/write behavior.

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 application reconnects through the intended endpoint after a tested supported failover, with documented possible data loss and read/write behavior.

Inspect scans/seeks, key lookup counts, implicit conversions, estimated-versus-actual rows, missing-index recommendations and write amplification. This choice pertains to Execution plans cardinality and indexing improvements, 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.

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. This choice pertains to Extended Events and workload-specific incident evidence, 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.

Performance improvements persist under measured production workloads and failed recommendations are reversed or disabled according to policy. This choice pertains to Automatic tuning and intelligent query processing guardrails, 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 real job failure triggers one useful notification to the right team and its history records sufficient context for a safe repair or retry. This would validate SQL Agent alerts operators and failure notifications, but it does not establish the required outcome here. Verify instead: The maintenance job executes on the approved cadence with least-privilege identity and traceable execution logs without depending on a personal admin login.

The repaired scheduled job completes reliably and re-executing a partially completed workflow does not double-post or delete unintended data. This would validate SQL Agent job troubleshooting and idempotent maintenance, but it does not establish the required outcome here. Verify instead: The maintenance job executes on the approved cadence with least-privilege identity and traceable execution logs without depending on a personal admin login.

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 maintenance job executes on the approved cadence with least-privilege identity and traceable execution logs without depending on a personal admin login.

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 addresses Azure SQL Database elastic jobs for fleet maintenance, not the controlling requirement here. Elastic jobs are a separate managed job-agent service and should not be confused with SQL Server Agent jobs inside Azure SQL Database. Prefer: Configure SQL Server Agent schedules and job-step security on supported SQL Server or Managed Instance platforms, separating service credentials from the database data permissions they need.

Investigate SQL Agent job-step execution context, timing and dependencies before retrying, and make destructive maintenance operations safe for partial reruns. This addresses SQL Agent job troubleshooting and idempotent maintenance, not the controlling requirement here. Retrying a failed job without tracking committed work can create duplicate actions; application and SQL job state must be reconciled. Prefer: Configure SQL Server Agent schedules and job-step security on supported SQL Server or Managed Instance platforms, separating service credentials from the database data permissions they need.

Configure SQL Server Agent notifications, operators and error handling so failed backups and maintenance tasks generate actionable alerts without leaking sensitive details. This addresses SQL Agent alerts operators and failure notifications, not the controlling requirement here. A green schedule and enabled operator are not evidence that email delivery or failure handling works unless the notification path is tested. Prefer: Configure SQL Server Agent schedules and job-step security on supported SQL Server or Managed Instance platforms, separating service credentials from the database data permissions they need.

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: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time.

Review actual execution plans, predicate sargability, cardinality estimates and suitable index designs before adding indexes or scaling hardware. This addresses Execution plans cardinality and indexing improvements, not the controlling requirement here. A missing-index suggestion is a starting hypothesis, not a license to create every suggested index; SARGable predicates and accurate estimates matter. Prefer: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time.

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: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time.

Investigate SQL Agent job-step execution context, timing and dependencies before retrying, and make destructive maintenance operations safe for partial reruns. This addresses SQL Agent job troubleshooting and idempotent maintenance, not the controlling requirement here. Retrying a failed job without tracking committed work can create duplicate actions; application and SQL job state must be reconciled. Prefer: Use ARM or Bicep to define Azure SQL servers, databases, identity, networking and policy settings in source-controlled templates with environment parameters and gated deployment checks.

Monitor Azure resource deployments and resolve failed dependencies or policy denials using logs and change review, with safe rollback plans for data-bearing SQL resources. This addresses Deployment monitoring rollback and drift reconciliation, not the controlling requirement here. Rolling back an infrastructure template is not identical to restoring committed database transactions, and destructive replacements require explicit protection. Prefer: Use ARM or Bicep to define Azure SQL servers, databases, identity, networking and policy settings in source-controlled templates with environment parameters and gated deployment checks.

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 addresses Azure SQL Database elastic jobs for fleet maintenance, not the controlling requirement here. Elastic jobs are a separate managed job-agent service and should not be confused with SQL Server Agent jobs inside Azure SQL Database. Prefer: Use ARM or Bicep to define Azure SQL servers, databases, identity, networking and policy settings in source-controlled templates with environment parameters and gated deployment checks.

Size Managed Instance and SQL Server VM compute, storage latency and throughput using performance evidence, including correct storage-layout and platform support limits. This addresses Managed Instance and SQL VM compute storage scaling, not the controlling requirement here. Increasing CPU cannot fix a saturated log throughput budget or a storage path with high sustained write latency. Prefer: 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.

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 addresses Azure Arc-enabled SQL and hybrid fleet governance, not the controlling requirement here. Azure Arc projects management and governance into Azure; Arc onboarding is not equivalent to migration or replacing SQL Server backups and patching. Prefer: 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.

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. This addresses Online migration with controlled cutover and validation, not the controlling requirement here. Online migration reduces downtime but is not automatically zero-downtime; final synchronization, traffic redirection and application tests still matter. Prefer: 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.

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: After a planned or forced failover the intended database endpoint serves valid writes and read-only workloads under documented consistency limits.

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: After a planned or forced failover the intended database endpoint serves valid writes and read-only workloads under documented consistency limits.

Approved managed-instance failover or hybrid transition works after controlled traffic redirection, with required instance features and logins present. This would validate Managed Instance hybrid recovery and operational readiness, but it does not establish the required outcome here. Verify instead: After a planned or forced failover the intended database endpoint serves valid writes and read-only workloads under documented consistency limits.

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: Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs.

Inventory SQL Agent, cross-database dependencies, instance features, latency, licensing, OS access and application connection requirements before choosing a target. These checks primarily test Choosing Azure SQL Database versus Managed Instance versus SQL VM instead of the critical system behavior in this case. Examine: Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs.

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: Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs.