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The migration plan explicitly maps each source feature to a supported destination behavior and documented remediation before cutover. This would validate Azure SQL migration assessment and feature compatibility, but it does not establish the required outcome here. Verify instead: The chosen tier or pool absorbs expected peaks with measured response times while avoiding waste from persistently idle dedicated allocations.

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: The chosen tier or pool absorbs expected peaks with measured response times while avoiding waste from persistently idle dedicated allocations.

Queries and retention maintenance operate on intended partitions with predictable execution plans, storage growth and acceptable CPU overhead. This would validate Table partitioning compression and scale-out tradeoffs, but it does not establish the required outcome here. Verify instead: The chosen tier or pool absorbs expected peaks with measured response times while avoiding waste from persistently idle dedicated allocations.

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. This addresses Offline migration and post-migration verification, not the controlling requirement here. A BACPAC export captures schema and data for applicable databases but is not an instance backup that automatically recreates all server features and jobs. Prefer: 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.

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: 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.

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: 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.

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: 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.

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: 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.

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: 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.

Inspect Windows Server failover cluster or supported Linux HA configuration, availability replica state, listener IP/DNS, synchronization health, AG endpoint port and failover readiness. These checks primarily test Always On availability groups on SQL Server VMs and SQL MI links instead of the critical system behavior in this case. Examine: Inspect recovery objectives, replication modes, failure domains, backup frequencies, availability SLA, network and DNS cutover plus application dependency map.

Inspect database recovery model, complete backup-chain LSNs, log backup cadence, backup checksums, database state and recovery target. These checks primarily test SQL Server native backup chain and recovery model instead of the critical system behavior in this case. Examine: Inspect recovery objectives, replication modes, failure domains, backup frequencies, availability SLA, network and DNS cutover plus application dependency map.

Examine failover runbook owner, replica health, DNS and application retry time, last confirmed transaction, validation queries and restore evidence. These checks primarily test HA DR tabletop and destructive failure testing instead of the critical system behavior in this case. Examine: Inspect recovery objectives, replication modes, failure domains, backup frequencies, availability SLA, network and DNS cutover plus application dependency map.

Clients establish the intended authentication method without insecure credential sharing, and failed sign-ins can be tied to the correct principal and database. This choice pertains to SQL authentication versus Entra and managed-instance principals, not the needed response/evidence pair. Use: Design Azure SQL firewall policies and Private Link or approved service endpoints at the required scope, enforcing TLS and minimizing public endpoint exposure. Check: Inspect server-level and database-level firewall rules, private endpoint approval and DNS, VNet/subnet routing, public network access status and certificate validation.

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 choice pertains to Transparent data encryption keys and protector lifecycle, not the needed response/evidence pair. Use: Design Azure SQL firewall policies and Private Link or approved service endpoints at the required scope, enforcing TLS and minimizing public endpoint exposure. Check: Inspect server-level and database-level firewall rules, private endpoint approval and DNS, VNet/subnet routing, public network access status and certificate validation.

Inspect SESSION_CONTEXT usage, predicate function logic, policy state, object permissions, execution plans and cross-tenant test accounts. This choice pertains to Row-level security and ownership predicates, not the needed response/evidence pair. Use: Design Azure SQL firewall policies and Private Link or approved service endpoints at the required scope, enforcing TLS and minimizing public endpoint exposure. Check: Inspect server-level and database-level firewall rules, private endpoint approval and DNS, VNet/subnet routing, public network access status and certificate validation.

The migrated service opens with consistent data, working identities and restored application workflows within the agreed maintenance period. This choice pertains to Offline migration and post-migration verification, not the needed response/evidence pair. Use: Choose database vCore or DTU purchasing models, compute tier and elastic-pool membership from measured resource patterns, isolation needs and predictable workload peaks. Check: Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits.

Inspect predicate selectivity, partition elimination, split/merge plans, aligned indexes, compression CPU overhead and real data size reduction. This choice pertains to Table partitioning compression and scale-out tradeoffs, not the needed response/evidence pair. Use: Choose database vCore or DTU purchasing models, compute tier and elastic-pool membership from measured resource patterns, isolation needs and predictable workload peaks. Check: Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits.

Size Managed Instance and SQL Server VM compute, storage latency and throughput using performance evidence, including correct storage-layout and platform support limits. This choice pertains to Managed Instance and SQL VM compute storage scaling, not the needed response/evidence pair. Use: Choose database vCore or DTU purchasing models, compute tier and elastic-pool membership from measured resource patterns, isolation needs and predictable workload peaks. Check: Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits.

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: Access paths and estimates remain healthy with acceptable maintenance duration, transaction log growth and production concurrency.

Performance improvements persist under measured production workloads and failed recommendations are reversed or disabled according to policy. This would validate Automatic tuning and intelligent query processing guardrails, but it does not establish the required outcome here. Verify instead: Access paths and estimates remain healthy with acceptable maintenance duration, transaction log growth and production concurrency.

A problematic query recovers representative execution time without forcing a plan that fails under other parameter or schema conditions. This would validate Query Store plan regressions and forced plans, but it does not establish the required outcome here. Verify instead: Access paths and estimates remain healthy with acceptable maintenance duration, transaction log growth and production concurrency.

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: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night. Check: Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows.

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 choice pertains to Automatic tuning and intelligent query processing guardrails, not the needed response/evidence pair. Use: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night. Check: Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows.

Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change. This choice pertains to SQL operational baseline across waits locks and storage, not the needed response/evidence pair. Use: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night. Check: Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows.

Each authorized tenant sees only its records through SQL and API queries, while unauthorized predicate bypass attempts return no rows. This choice pertains to Row-level security and ownership predicates, not the needed response/evidence pair. Use: 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. Check: Check effective permission using EXECUTE AS, user/database role membership, ownership chain, DENY overrides, T-SQL GRANT statements and precise failed operation.

Inspect server-level and database-level firewall rules, private endpoint approval and DNS, VNet/subnet routing, public network access status and certificate validation. This choice pertains to Azure SQL private endpoints firewall and TLS boundary, not the needed response/evidence pair. Use: 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. Check: Check effective permission using EXECUTE AS, user/database role membership, ownership chain, DENY overrides, T-SQL GRANT statements and precise failed operation.

Distinguish authentication from authorization using SQL error context, Entra membership and database principal mapping before changing firewall or granting elevated roles. This choice pertains to Diagnosing login failure and group-based access, not the needed response/evidence pair. Use: 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. Check: Check effective permission using EXECUTE AS, user/database role membership, ownership chain, DENY overrides, T-SQL GRANT statements and precise failed operation.

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: 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.

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. This addresses Query Store plan regressions and forced plans, not the controlling requirement here. Forcing a historical plan can mitigate a regression but does not make poor statistics or parameter sensitivity disappear and may fail after schema changes. Prefer: 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.

Use Resource Governor on supported SQL Server platforms for workload classification and resource controls, while choosing Azure SQL compute tier and database-scoped settings appropriately for PaaS. This addresses Resource Governor and scoped compute configuration, not the controlling requirement here. Resource Governor is not uniformly offered on every Azure SQL service, and workload isolation cannot be achieved simply by setting an unrelated database-scoped knob. Prefer: 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.

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: Inspect step command, proxy token, connection defaults, deadlocks, Agent history output, last success timestamp and job rerun behavior.

Inspect template parameters, deployment scope, what-if output, module dependencies, identity role assignments and post-deploy effective configuration. These checks primarily test ARM and Bicep declarative Azure SQL provisioning instead of the critical system behavior in this case. Examine: Inspect step command, proxy token, connection defaults, deadlocks, Agent history output, last success timestamp and job rerun behavior.

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 step command, proxy token, connection defaults, deadlocks, Agent history output, last success timestamp and job rerun behavior.