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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: 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.

Select Azure SQL Database for managed database-level PaaS needs, Azure SQL Managed Instance when near-instance compatibility is required, or SQL Server on Azure VMs for OS and instance control. This addresses Choosing Azure SQL Database versus Managed Instance versus SQL VM, not the controlling requirement here. A managed database offering does not necessarily expose every SQL Server instance feature; platform responsibility differs from SQL Server on a VM. Prefer: 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.

Deploy governed Azure SQL resources with repeatable infrastructure templates and evaluate Azure SQL Database in Fabric for supported analytics-connected workflows rather than assuming all workloads are equivalent. This addresses Azure SQL deployment automation and Fabric SQL integration boundary, not the controlling requirement here. Using Fabric with Azure SQL does not remove operational security responsibilities or grant unsupported cross-service transactional semantics. Prefer: 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.

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 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 job runs on intended databases with controlled concurrency and job history that identifies any failed or skipped target for remediation.

Use a current migration assessment to inventory database dependencies, unsupported features, downtime constraints and target platform compatibility before moving data. This addresses Azure SQL migration assessment and feature compatibility, not the controlling requirement here. Successful schema import does not prove that instance-level jobs, security identities and linked dependencies survive a migration. Prefer: 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.

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: 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.

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: 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.

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: Performance improvements persist under measured production workloads and failed recommendations are reversed or disabled according to policy.

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: Performance improvements persist under measured production workloads and failed recommendations are reversed or disabled according to policy.

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: Performance improvements persist under measured production workloads and failed recommendations are reversed or disabled according to policy.

Select Azure SQL Database for managed database-level PaaS needs, Azure SQL Managed Instance when near-instance compatibility is required, or SQL Server on Azure VMs for OS and instance control. This addresses Choosing Azure SQL Database versus Managed Instance versus SQL VM, not the controlling requirement here. A managed database offering does not necessarily expose every SQL Server instance feature; platform responsibility differs from SQL Server on a VM. Prefer: Deploy governed Azure SQL resources with repeatable infrastructure templates and evaluate Azure SQL Database in Fabric for supported analytics-connected workflows rather than assuming all workloads are equivalent.

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: Deploy governed Azure SQL resources with repeatable infrastructure templates and evaluate Azure SQL Database in Fabric for supported analytics-connected workflows rather than assuming all workloads are equivalent.

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: Deploy governed Azure SQL resources with repeatable infrastructure templates and evaluate Azure SQL Database in Fabric for supported analytics-connected workflows rather than assuming all workloads are equivalent.

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 addresses Transparent data encryption keys and protector lifecycle, not the controlling requirement here. TDE encrypts storage at rest but does not hide column values from authorized SQL queries or replace end-to-end TLS. Prefer: Design Azure SQL firewall policies and Private Link or approved service endpoints at the required scope, enforcing TLS and minimizing public endpoint exposure.

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: Design Azure SQL firewall policies and Private Link or approved service endpoints at the required scope, enforcing TLS and minimizing public endpoint exposure.

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: Design Azure SQL firewall policies and Private Link or approved service endpoints at the required scope, enforcing TLS and minimizing public endpoint exposure.

Inspect elastic job agent setup, target server groups, database-scoped credentials, schedule, job version, execution history and permissions. These checks primarily test Azure SQL Database elastic jobs for fleet maintenance instead of the critical system behavior in this case. Examine: Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health.

Inspect SQL Agent service state, job owner, proxy or credential, step subsystem, schedules, permissions, execution history and target database. These checks primarily test SQL Server Agent schedules and job ownership instead of the critical system behavior in this case. Examine: Inspect deployment operation results, resource provider error codes, policy denials, what-if diffs, resource locks, template validation and database post-deploy health.

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

Inspect diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings. This choice pertains to Intelligent Insights and parameter-sensitive query analysis, 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.

A problematic query recovers representative execution time without forcing a plan that fails under other parameter or schema conditions. This choice pertains to Query Store plan regressions and forced plans, 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.

Database files and supported backups remain encrypted with accessible protectors, and rotation or failover does not block required recovery operations. This would validate Transparent data encryption keys and protector lifecycle, but it does not establish the required outcome here. Verify instead: Approved users can execute only intended tables, views and procedures, while unauthorized writes and administrative operations fail reproducibly.

Low-trust users receive suitably masked results while sensitive storage and access controls still enforce separately tested privacy requirements. This would validate Dynamic data masking classification and permission differences, but it does not establish the required outcome here. Verify instead: Approved users can execute only intended tables, views and procedures, while unauthorized writes and administrative operations fail reproducibly.

A previously failing user connects with the correct principal and performs only approved operations after a narrowly scoped corrective change. This would validate Diagnosing login failure and group-based access, but it does not establish the required outcome here. Verify instead: Approved users can execute only intended tables, views and procedures, while unauthorized writes and administrative operations fail reproducibly.

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: Distinguish authentication from authorization using SQL error context, Entra membership and database principal mapping before changing firewall or granting elevated roles.

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 addresses Transparent data encryption keys and protector lifecycle, not the controlling requirement here. TDE encrypts storage at rest but does not hide column values from authorized SQL queries or replace end-to-end TLS. Prefer: Distinguish authentication from authorization using SQL error context, Entra membership and database principal mapping before changing firewall or granting elevated roles.

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 addresses Azure SQL auditing ledger and change tracking distinctions, not the controlling requirement here. SQL auditing, change tracking, CDC and ledger answer different questions; enabling one does not guarantee identical transaction history or immutable external evidence. Prefer: Distinguish authentication from authorization using SQL error context, Entra membership and database principal mapping before changing firewall or granting elevated roles.

Queries use appropriate access paths under representative parameters while maintenance and write overhead remain acceptable. This choice pertains to Execution plans cardinality and indexing improvements, not the needed response/evidence pair. Use: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time. Check: Review database watcher configuration and targets, query and instance samples, CPU, data/log IO, wait statistics, alert scope and time-series retention.

Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows. This choice pertains to Index and statistics maintenance with targeted cadence, not the needed response/evidence pair. Use: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time. Check: Review database watcher configuration and targets, query and instance samples, CPU, data/log IO, wait statistics, alert scope and time-series retention.

Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources. This choice pertains to SQL operational baseline across waits locks and storage, not the needed response/evidence pair. Use: Create a workload baseline and enable appropriate Azure SQL monitoring, including database watcher where supported, to correlate query behavior with resource saturation over time. Check: Review database watcher configuration and targets, query and instance samples, CPU, data/log IO, wait statistics, alert scope and time-series retention.