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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 SQL Server instance restarts on an eligible node using accessible shared data and log storage while the application resolves the cluster virtual network name.

A restore to the required historical point completes into a supported database target and data plus dependent identities are validated. This would validate Azure SQL automatic PITR and long-term retention, but it does not establish the required outcome here. Verify instead: The SQL Server instance restarts on an eligible node using accessible shared data and log storage while the application resolves the cluster virtual network name.

After a planned or forced failover the intended database endpoint serves valid writes and read-only workloads under documented consistency limits. This would validate Azure SQL Database active geo replication and failover groups, but it does not establish the required outcome here. Verify instead: The SQL Server instance restarts on an eligible node using accessible shared data and log storage while the application resolves the cluster virtual network name.

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

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: 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.

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: Use Query Store to compare query runtime and plan history, identify a genuine plan regression, and force a known suitable plan only with ongoing validation and rollback.

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. This addresses Azure Monitor alerts diagnostics and resource metrics, not the controlling requirement here. A low CPU value does not mean an app is healthy; incorrect aggregation or missing telemetry can hide blocked sessions and failures. Prefer: Use Query Store to compare query runtime and plan history, identify a genuine plan regression, and force a known suitable plan only with ongoing validation and rollback.

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 Query Store to compare query runtime and plan history, identify a genuine plan regression, and force a known suitable plan only with ongoing validation and rollback.

The migrated service opens with consistent data, working identities and restored application workflows within the agreed maintenance period. This would validate Offline migration and post-migration verification, but it does not establish the required outcome here. Verify instead: The destination is consistent at an accepted cutover point, applications use the intended endpoint, and rollback criteria are documented before retirement of source.

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: The destination is consistent at an accepted cutover point, applications use the intended endpoint, and rollback criteria are documented before retirement of source.

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: The destination is consistent at an accepted cutover point, applications use the intended endpoint, and rollback criteria are documented before retirement of source.

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 addresses Azure database task automation alerts and retries, not the controlling requirement here. An Azure automation runbook reporting successful execution does not prove its database command succeeded or that an alert was delivered. Prefer: Investigate SQL Agent job-step execution context, timing and dependencies before retrying, and make destructive maintenance operations safe for partial reruns.

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: Investigate SQL Agent job-step execution context, timing and dependencies before retrying, and make destructive maintenance operations safe for partial reruns.

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. This addresses SQL Server Agent schedules and job ownership, not the controlling requirement here. SQL Server Agent is supported on relevant instance-level offerings but is not the native scheduling mechanism inside a standalone Azure SQL Database. Prefer: Investigate SQL Agent job-step execution context, timing and dependencies before retrying, and make destructive maintenance operations safe for partial reruns.

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. This addresses Table partitioning compression and scale-out tradeoffs, not the controlling requirement here. Partitioning alone does not make every query faster; indexes and predicate alignment determine pruning and compression may trade CPU for IO. Prefer: Size Managed Instance and SQL Server VM compute, storage latency and throughput using performance evidence, including correct storage-layout and platform support limits.

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: Size Managed Instance and SQL Server VM compute, storage latency and throughput using performance evidence, including correct storage-layout and platform support limits.

Choose database vCore or DTU purchasing models, compute tier and elastic-pool membership from measured resource patterns, isolation needs and predictable workload peaks. This addresses Azure SQL Database service tiers and elastic pools, not the controlling requirement here. Elastic pools share resources across eligible Azure SQL databases; adding a pool cannot eliminate an individual database's workload or resource configuration limits. Prefer: Size Managed Instance and SQL Server VM compute, storage latency and throughput using performance evidence, including correct storage-layout and platform support limits.

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. This choice pertains to SQL Server Agent schedules and job ownership, not the needed response/evidence pair. Use: Configure SQL Server Agent notifications, operators and error handling so failed backups and maintenance tasks generate actionable alerts without leaking sensitive details. Check: Inspect job history return status, Database Mail profile, operator assignment, alert severity, retry steps, failure branching and delivery errors.

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: Configure SQL Server Agent notifications, operators and error handling so failed backups and maintenance tasks generate actionable alerts without leaking sensitive details. Check: Inspect job history return status, Database Mail profile, operator assignment, alert severity, retry steps, failure branching and delivery errors.

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: Configure SQL Server Agent notifications, operators and error handling so failed backups and maintenance tasks generate actionable alerts without leaking sensitive details. Check: Inspect job history return status, Database Mail profile, operator assignment, alert severity, retry steps, failure branching and delivery errors.

Review subscription context, service principal or managed identity, Azure RBAC role, API response, database resource state and script error handling. This choice pertains to Azure PowerShell and CLI operations with scoped automation identity, not the needed response/evidence pair. Use: 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. Check: Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health.

The repaired scheduled job completes reliably and re-executing a partially completed workflow does not double-post or delete unintended data. This choice pertains to SQL Agent job troubleshooting and idempotent maintenance, not the needed response/evidence pair. Use: 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. Check: Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health.

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. This choice pertains to ARM and Bicep declarative Azure SQL provisioning, not the needed response/evidence pair. Use: 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. Check: Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health.

The destination is consistent at an accepted cutover point, applications use the intended endpoint, and rollback criteria are documented before retirement of source. This would validate Online migration with controlled cutover and validation, but it does not establish the required outcome here. Verify instead: Administrators can view authorized hybrid SQL inventory and policy findings while database files remain in their existing managed locations.

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: Administrators can view authorized hybrid SQL inventory and policy findings while database files remain in their existing managed locations.

The chosen tier or pool absorbs expected peaks with measured response times while avoiding waste from persistently idle dedicated allocations. This would validate Azure SQL Database service tiers and elastic pools, but it does not establish the required outcome here. Verify instead: Administrators can view authorized hybrid SQL inventory and policy findings while database files remain in their existing managed locations.

Inspect login/user mapping, server principals, password policy, Entra admin configuration, directory identity, database containment and connection string authentication mode. These checks primarily test SQL authentication versus Entra and managed-instance principals instead of the critical system behavior in this case. Examine: Inspect sensitivity labels, masking expressions, UNMASK permissions, privileged role membership and direct query behavior across application identities.

Inspect SESSION_CONTEXT usage, predicate function logic, policy state, object permissions, execution plans and cross-tenant test accounts. These checks primarily test Row-level security and ownership predicates instead of the critical system behavior in this case. Examine: Inspect sensitivity labels, masking expressions, UNMASK permissions, privileged role membership and direct query behavior across application identities.

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 sensitivity labels, masking expressions, UNMASK permissions, privileged role membership and direct query behavior across application identities.

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: Long blocking chains shorten and transaction correctness remains valid after safe tuning, with deadlock retry policy and consistency requirements addressed.

Queries use appropriate access paths under representative parameters while maintenance and write overhead remain acceptable. This would validate Execution plans cardinality and indexing improvements, but it does not establish the required outcome here. Verify instead: Long blocking chains shorten and transaction correctness remains valid after safe tuning, with deadlock retry policy and consistency requirements addressed.

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: Long blocking chains shorten and transaction correctness remains valid after safe tuning, with deadlock retry policy and consistency requirements addressed.