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The captured evidence explains the failing operation at the right time with sufficient context and acceptable overhead, without recording unrestricted sensitive payloads. This would validate Extended Events and workload-specific incident evidence, but it does not establish the required outcome here. Verify instead: The identified bottleneck has a specific measured cause, and the chosen corrective action changes the relevant metric under representative load.

Competing workloads respect their configured limits and critical requests retain service within documented platform and resource constraints. This would validate Resource Governor and scoped compute configuration, but it does not establish the required outcome here. Verify instead: The identified bottleneck has a specific measured cause, and the chosen corrective action changes the relevant metric under representative load.

Operators detect regressions against representative baseline periods and can link database symptoms to trustworthy collected performance evidence. This would validate Database watcher and Azure SQL monitoring baseline, but it does not establish the required outcome here. Verify instead: The identified bottleneck has a specific measured cause, and the chosen corrective action changes the relevant metric under representative load.

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 addresses Object-level encryption and database cryptographic controls, not the controlling requirement here. SQL object encryption can protect selected values, but compromised key access or overly broad SQL privileges undermine the intended isolation. Prefer: Use Always Encrypted to protect selected columns with client-side cryptography and evaluate secure enclaves for supported confidential computations and richer query operations.

Select Entra or SQL authentication deliberately for Azure SQL Database, Managed Instance and SQL Server VMs based on platform support, application constraints and security controls. This addresses SQL authentication versus Entra and managed-instance principals, not the controlling requirement here. A login defined in one SQL Server instance is not automatically a contained user in another database or a globally valid Entra identity. Prefer: Use Always Encrypted to protect selected columns with client-side cryptography and evaluate secure enclaves for supported confidential computations and richer query operations.

Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required. This addresses Dynamic data masking classification and permission differences, not the controlling requirement here. Dynamic Data Masking is not encryption or a complete defense against inference; administrators and UNMASK principals can still access original values. Prefer: Use Always Encrypted to protect selected columns with client-side cryptography and evaluate secure enclaves for supported confidential computations and richer query operations.

Select a supported HA and disaster recovery architecture from business-defined RPO, RTO, region failure assumptions, planned failover steps and service-specific capabilities. This addresses RPO RTO strategy and availability versus recovery, not the controlling requirement here. High availability reduces some outages but cannot substitute for recoverable backups after corruption or human deletion; RPO and RTO are separate targets. Prefer: 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.

Use supported SQL Server failover cluster instances on Azure virtual machines with compatible shared storage and cluster quorum instead of treating FCIs as data-replication AGs. This addresses SQL Server failover cluster instances shared storage constraints, not the controlling requirement here. An FCI moves SQL Server instance ownership over shared storage but does not automatically provide a separate cross-region copy of database files. Prefer: 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.

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: 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.

Competing workloads respect their configured limits and critical requests retain service within documented platform and resource constraints. This would validate Resource Governor and scoped compute configuration, but it does not establish the required outcome here. Verify instead: Queries use appropriate access paths under representative parameters while maintenance and write overhead remain acceptable.

A representative mix of small and large parameter executions achieves stable resource use, with intelligent feature effects monitored over time. This would validate Intelligent Insights and parameter-sensitive query analysis, but it does not establish the required outcome here. Verify instead: Queries use appropriate access paths under representative parameters while maintenance and write overhead remain acceptable.

Alerts fire for meaningful sustained database risk while log delivery and missing-metric gaps are independently observable. This would validate Azure Monitor alerts diagnostics and resource metrics, but it does not establish the required outcome here. Verify instead: Queries use appropriate access paths under representative parameters while maintenance and write overhead remain acceptable.

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 scans/seeks, key lookup counts, implicit conversions, estimated-versus-actual rows, missing-index recommendations and write amplification.

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 scans/seeks, key lookup counts, implicit conversions, estimated-versus-actual rows, missing-index recommendations and write amplification.

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 scans/seeks, key lookup counts, implicit conversions, estimated-versus-actual rows, missing-index recommendations and write amplification.

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: Test availability and recovery plans with realistic but controlled primary outages, application write workload, backups and rollback exercises before claiming a recovery objective.

Choose Azure SQL failover groups or active geo-replication according to group-level endpoint continuity, supported replication topology, read scale and application reconnect requirements. This addresses Azure SQL failover groups versus active geo replication decisions, not the controlling requirement here. Asynchronous cross-region replication may lose recent commits on forced failover; automatic failover behavior differs by service and configuration. Prefer: Test availability and recovery plans with realistic but controlled primary outages, application write workload, backups and rollback exercises before claiming a recovery objective.

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: Test availability and recovery plans with realistic but controlled primary outages, application write workload, backups and rollback exercises before claiming a recovery objective.

Inspect sensitivity labels, masking expressions, UNMASK permissions, privileged role membership and direct query behavior across application identities. These checks primarily test Dynamic data masking classification and permission differences 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.

Examine audit destinations, retention and access, captured event classes, Change Tracking version retention, CDC capture jobs, ledger digest verification and application expectations. These checks primarily test Azure SQL auditing ledger and change tracking distinctions 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 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.

Design Azure SQL firewall policies and Private Link or approved service endpoints at the required scope, enforcing TLS and minimizing public endpoint exposure. This addresses Azure SQL private endpoints firewall and TLS boundary, not the controlling requirement here. A Private Endpoint does not provide correct DNS records automatically in every custom network; name resolution and access policy require separate verification. Prefer: Implement Row-Level Security with a carefully tested predicate function and security policy for each tenant or business boundary, independent of form-level filtering.

Distinguish authentication from authorization using SQL error context, Entra membership and database principal mapping before changing firewall or granting elevated roles. This addresses Diagnosing login failure and group-based access, not the controlling requirement here. Network allow rules cannot repair a database permission denial, and a valid directory group may still lack an associated database user. Prefer: Implement Row-Level Security with a carefully tested predicate function and security policy for each tenant or business boundary, independent of form-level filtering.

Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required. This addresses Dynamic data masking classification and permission differences, not the controlling requirement here. Dynamic Data Masking is not encryption or a complete defense against inference; administrators and UNMASK principals can still access original values. Prefer: Implement Row-Level Security with a carefully tested predicate function and security policy for each tenant or business boundary, independent of form-level filtering.

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

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: 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.

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.

Inspect step command, proxy token, connection defaults, deadlocks, Agent history output, last success timestamp and job rerun behavior. This choice pertains to SQL Agent job troubleshooting and idempotent maintenance, 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.

A failed deployment is diagnosed without deleting productive data, and the corrected infrastructure remains consistent with reviewed desired state. This choice pertains to Deployment monitoring rollback and drift reconciliation, 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.

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.