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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.

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. This addresses Log shipping health and recovery orchestration, not the controlling requirement here. Log shipping is backup-copy-restore orchestration, not synchronous automatic database failover or a substitute for manual application cutover. 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.

Plan full, differential and transaction-log backups using appropriate SQL Server recovery model and restore order for the intended point-in-time objective. This addresses SQL Server native backup chain and recovery model, not the controlling requirement here. A full backup without subsequent transaction-log backups cannot recover every later transaction; SIMPLE recovery changes log backup support. 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.

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: 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.

Use Always Encrypted to protect selected columns with client-side cryptography and evaluate secure enclaves for supported confidential computations and richer query operations. This addresses Always Encrypted client-side protection and enclaves, not the controlling requirement here. Always Encrypted is distinct from TDE; queries and operations may be constrained without supported enclave capabilities and drivers. Prefer: Design Azure SQL firewall policies and Private Link or approved service endpoints at the required scope, enforcing TLS and minimizing public endpoint exposure.

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: Inspect SESSION_CONTEXT usage, predicate function logic, policy state, object permissions, execution plans and cross-tenant test accounts.

Review key hierarchy, certificate availability, encrypted payload format, authorized decryption path, T-SQL function usage, rotation and backup procedures. These checks primarily test Object-level encryption and database cryptographic controls instead of the critical system behavior in this case. Examine: Inspect SESSION_CONTEXT usage, predicate function logic, policy state, object permissions, execution plans and cross-tenant test accounts.

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 SESSION_CONTEXT usage, predicate function logic, policy state, object permissions, execution plans and cross-tenant test accounts.

Plan full, differential and transaction-log backups using appropriate SQL Server recovery model and restore order for the intended point-in-time objective. This choice pertains to SQL Server native backup chain and recovery model, not the needed response/evidence pair. Use: 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. Check: Check service tier, available restore points, retention setting, logical server and database permissions, LTR vault policy and restore destination.

Inspect replication state, endpoint listeners, failover policy, allowed data loss, application reconnect errors, secondary region capacity and DNS cache behavior. This choice pertains to Azure SQL Database active geo replication and failover groups, not the needed response/evidence pair. Use: 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. Check: Check service tier, available restore points, retention setting, logical server and database permissions, LTR vault policy and restore destination.

A documented exercise demonstrates recovery within measured time and data-loss objectives and reveals unmet application dependencies before production emergencies. This choice pertains to HA DR tabletop and destructive failure testing, not the needed response/evidence pair. Use: 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. Check: Check service tier, available restore points, retention setting, logical server and database permissions, LTR vault policy and restore destination.

Administrators can view authorized hybrid SQL inventory and policy findings while database files remain in their existing managed locations. This would validate Azure Arc-enabled SQL and hybrid fleet governance, but it does not establish the required outcome here. Verify instead: The migration plan explicitly maps each source feature to a supported destination behavior and documented remediation before cutover.

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 migration plan explicitly maps each source feature to a supported destination behavior and documented remediation before cutover.

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 migration plan explicitly maps each source feature to a supported destination behavior and documented remediation before cutover.

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: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night.

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: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night.

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 addresses Automatic tuning and intelligent query processing guardrails, not the controlling requirement here. Automatic tuning may need time to evaluate evidence and can reverse actions; feature availability is platform and compatibility-level dependent. Prefer: Schedule integrity checks, index and statistics maintenance based on measured fragmentation, modification rates and SLA rather than indiscriminately rebuilding every object each night.

Use Extended Events sessions to capture targeted SQL events and wait/blocking evidence with bounded collection and a safe target rather than enabling excessive full-query traces in production. This choice pertains to Extended Events and workload-specific incident evidence, not the needed response/evidence pair. Use: Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources. Check: Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change.

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: Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources. Check: Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change.

Check automatic tuning mode, automatic plan correction state, index create/drop recommendations, plan verification, workload compatibility level and rollback events. This choice pertains to Automatic tuning and intelligent query processing guardrails, not the needed response/evidence pair. Use: Distinguish compute, data IO, log IO, memory and blocking contention using correlated counters, wait types and workload-specific thresholds before changing resources. Check: Compare CPU versus runnable tasks, WRITELOG, PAGEIOLATCH, resource governance, connection counts, tempdb usage, storage latency and times of workload change.

A real job failure triggers one useful notification to the right team and its history records sufficient context for a safe repair or retry. This would validate SQL Agent alerts operators and failure notifications, but it does not establish the required outcome here. Verify instead: The automation updates only intended database resources and produces repeatable audit evidence of changes and failures without storing plaintext passwords.

A failed deployment is diagnosed without deleting productive data, and the corrected infrastructure remains consistent with reviewed desired state. This would validate Deployment monitoring rollback and drift reconciliation, but it does not establish the required outcome here. Verify instead: The automation updates only intended database resources and produces repeatable audit evidence of changes and failures without storing plaintext passwords.

The maintenance job executes on the approved cadence with least-privilege identity and traceable execution logs without depending on a personal admin login. This would validate SQL Server Agent schedules and job ownership, but it does not establish the required outcome here. Verify instead: The automation updates only intended database resources and produces repeatable audit evidence of changes and failures without storing plaintext passwords.

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: A problematic query recovers representative execution time without forcing a plan that fails under other parameter or schema conditions.

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: A problematic query recovers representative execution time without forcing a plan that fails under other parameter or schema conditions.

The identified bottleneck has a specific measured cause, and the chosen corrective action changes the relevant metric under representative load. This would validate SQL operational baseline across waits locks and storage, but it does not establish the required outcome here. Verify instead: A problematic query recovers representative execution time without forcing a plan that fails under other parameter or schema conditions.

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. This addresses Least-privilege SQL roles and ownership chaining, not the controlling requirement here. GRANT on one schema or stored procedure does not automatically confer arbitrary permissions on all underlying cross-database resources. Prefer: Protect targeted sensitive objects through supported SQL encryption functions, key hierarchy and application threat model while distinguishing column encryption from network and storage encryption.

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: Protect targeted sensitive objects through supported SQL encryption functions, key hierarchy and application threat model while distinguishing column encryption from network and storage encryption.

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: Protect targeted sensitive objects through supported SQL encryption functions, key hierarchy and application threat model while distinguishing column encryption from network and storage encryption.