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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: 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.

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: 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.

Review actual execution plans, predicate sargability, cardinality estimates and suitable index designs before adding indexes or scaling hardware. This choice pertains to Execution plans cardinality and indexing improvements, not the needed response/evidence pair. Use: 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. Check: Inspect diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings.

The identified bottleneck has a specific measured cause, and the chosen corrective action changes the relevant metric under representative load. This choice pertains to SQL operational baseline across waits locks and storage, not the needed response/evidence pair. Use: 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. Check: Inspect diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings.

Inspect event session predicates, event fields, ring buffer or event file targets, dropped events, blocked process reports and collection overhead. This choice pertains to Extended Events and workload-specific incident evidence, not the needed response/evidence pair. Use: 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. Check: Inspect diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings.

The maintenance job executes on the approved cadence with least-privilege identity and traceable execution logs without depending on a personal admin login. This choice pertains to SQL Server Agent schedules and job ownership, 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.

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.

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: 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.

Measure vCore pressure, memory grants, data and log latency, tempdb contention, IOPS ceilings, disk caching, VM SKU caps and scaling windows. This choice pertains to Managed Instance and SQL VM compute storage scaling, not the needed response/evidence pair. Use: 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. Check: Inspect predicate selectivity, partition elimination, split/merge plans, aligned indexes, compression CPU overhead and real data size reduction.

The destination is consistent at an accepted cutover point, applications use the intended endpoint, and rollback criteria are documented before retirement of source. This choice pertains to Online migration with controlled cutover and validation, not the needed response/evidence pair. Use: 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. Check: Inspect predicate selectivity, partition elimination, split/merge plans, aligned indexes, compression CPU overhead and real data size reduction.

Choose database vCore or DTU purchasing models, compute tier and elastic-pool membership from measured resource patterns, isolation needs and predictable workload peaks. This choice pertains to Azure SQL Database service tiers and elastic pools, not the needed response/evidence pair. Use: 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. Check: Inspect predicate selectivity, partition elimination, split/merge plans, aligned indexes, compression CPU overhead and real data size reduction.

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: Configure SQL Server log shipping across supported instances with reliable log backup, copy and restore schedules, delay and standby policies, then test manual recovery and ownership of jobs.

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 SQL Server log shipping across supported instances with reliable log backup, copy and restore schedules, delay and standby policies, then test manual recovery and ownership of jobs.

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 SQL Server log shipping across supported instances with reliable log backup, copy and restore schedules, delay and standby policies, then test manual recovery and ownership of jobs.

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.

Review key hierarchy, certificate availability, encrypted payload format, authorized decryption path, T-SQL function usage, rotation and backup procedures. This choice pertains to Object-level encryption and database cryptographic controls, 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.

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: 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.

Review sign-in error number and state, selected database, Entra token principal, group claims, CREATE USER scope, role membership and firewall reachability. These checks primarily test Diagnosing login failure and group-based access instead of the critical system behavior in this case. Examine: Check effective permission using EXECUTE AS, user/database role membership, ownership chain, DENY overrides, T-SQL GRANT statements and precise failed operation.

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: Check effective permission using EXECUTE AS, user/database role membership, ownership chain, DENY overrides, T-SQL GRANT statements and precise failed operation.

Inspect tenant and server Entra administrator settings, token audience, user mapping, group membership, service principal, role grants and sign-in failures. These checks primarily test Microsoft Entra authentication with contained service identity instead of the critical system behavior in this case. Examine: Check effective permission using EXECUTE AS, user/database role membership, ownership chain, DENY overrides, T-SQL GRANT statements and precise failed operation.

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

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.

Diagnose blocked and deadlocked sessions using live DMVs and deadlock graphs, then fix transaction scope, indexing or isolation behavior with concurrency tests. This addresses Blocking deadlocks and isolation strategy, not the controlling requirement here. Raising an app timeout does not remove a blocking root cause; snapshot isolation can reduce some reader/writer contention but changes version-store behavior. 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.

Inspect server-level and database-level firewall rules, private endpoint approval and DNS, VNet/subnet routing, public network access status and certificate validation. These checks primarily test Azure SQL private endpoints firewall and TLS boundary 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 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 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.

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.