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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: 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.

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: 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 effective permission using EXECUTE AS, user/database role membership, ownership chain, DENY overrides, T-SQL GRANT statements and precise failed operation. These checks primarily test Least-privilege SQL roles and ownership chaining instead of the critical system behavior in this case. Examine: Inspect login/user mapping, server principals, password policy, Entra admin configuration, directory identity, database containment and connection string authentication mode.

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 login/user mapping, server principals, password policy, Entra admin configuration, directory identity, database containment and connection string authentication mode.

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 login/user mapping, server principals, password policy, Entra admin configuration, directory identity, database containment and connection string authentication mode.

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 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 automation updates only intended database resources and produces repeatable audit evidence of changes and failures without storing plaintext passwords.

The job runs on intended databases with controlled concurrency and job history that identifies any failed or skipped target for remediation. This would validate Azure SQL Database elastic jobs for fleet maintenance, 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.

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: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required.

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: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required.

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: Classify sensitive columns, apply supported Dynamic Data Masking only for presentation control, and use encryption or RLS when stronger confidentiality is required.

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: Use Azure SQL elastic jobs for supported scheduled T-SQL maintenance across Azure SQL Database targets, managing target groups, credentials and job execution state.

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: Use Azure SQL elastic jobs for supported scheduled T-SQL maintenance across Azure SQL Database targets, managing target groups, credentials and job execution state.

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 addresses ARM and Bicep declarative Azure SQL provisioning, not the controlling requirement here. A valid ARM deployment may still produce unsafe security settings if templates use broad defaults or incorrect resource group/subscription targets. Prefer: Use Azure SQL elastic jobs for supported scheduled T-SQL maintenance across Azure SQL Database targets, managing target groups, credentials and job execution state.

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: A real job failure triggers one useful notification to the right team and its history records sufficient context for a safe repair or retry.

The job runs on intended databases with controlled concurrency and job history that identifies any failed or skipped target for remediation. This would validate Azure SQL Database elastic jobs for fleet maintenance, but it does not establish the required outcome here. Verify instead: A real job failure triggers one useful notification to the right team and its history records sufficient context for a safe repair or retry.

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: A real job failure triggers one useful notification to the right team and its history records sufficient context for a safe repair or retry.

A failed deployment is diagnosed without deleting productive data, and the corrected infrastructure remains consistent with reviewed desired state. This choice pertains to Deployment monitoring rollback and drift reconciliation, 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: 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.

Investigate SQL Agent job-step execution context, timing and dependencies before retrying, and make destructive maintenance operations safe for partial reruns. This choice pertains to SQL Agent job troubleshooting and idempotent maintenance, 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 diagnostic recommendations, Query Store plan variability, runtime skew, parameter compilation, memory grant feedback and database compatibility settings. These checks primarily test Intelligent Insights and parameter-sensitive query analysis instead of the critical system behavior in this case. Examine: Inspect supported platform, classifier function, workload groups, CPU and memory caps, connection routing, database-scoped settings and compute metrics.

Check automatic tuning mode, automatic plan correction state, index create/drop recommendations, plan verification, workload compatibility level and rollback events. These checks primarily test Automatic tuning and intelligent query processing guardrails instead of the critical system behavior in this case. Examine: Inspect supported platform, classifier function, workload groups, CPU and memory caps, connection routing, database-scoped settings and compute metrics.

Inspect index page counts, fragmentation relevance, row modification counters, statistics age, update thresholds, DBCC CHECKDB results and blocking windows. These checks primarily test Index and statistics maintenance with targeted cadence instead of the critical system behavior in this case. Examine: Inspect supported platform, classifier function, workload groups, CPU and memory caps, connection routing, database-scoped settings and compute metrics.

Inspect event session predicates, event fields, ring buffer or event file targets, dropped events, blocked process reports and collection overhead. These checks primarily test Extended Events and workload-specific incident evidence instead of the critical system behavior in this case. Examine: Inspect blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration.

Inspect metric namespace, resource dimensions, aggregation periods, diagnostic destinations, alert scope, delivery permissions, action groups and sample timestamps. These checks primarily test Azure Monitor alerts diagnostics and resource metrics instead of the critical system behavior in this case. Examine: Inspect blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration.

Inspect scans/seeks, key lookup counts, implicit conversions, estimated-versus-actual rows, missing-index recommendations and write amplification. These checks primarily test Execution plans cardinality and indexing improvements instead of the critical system behavior in this case. Examine: Inspect blocker/waiter SPIDs, wait_resource, open transactions, deadlock XML, isolation level, lock escalation and transaction duration.

Inspect CPU, data IO, log write, storage, concurrent sessions, DTU/vCore utilization, pool-level pressure and per-database limits. These checks primarily test Azure SQL Database service tiers and elastic pools instead of the critical system behavior in this case. Examine: Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs.

Inventory SQL Agent, cross-database dependencies, instance features, latency, licensing, OS access and application connection requirements before choosing a target. These checks primarily test Choosing Azure SQL Database versus Managed Instance versus SQL VM instead of the critical system behavior in this case. Examine: Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs.

Inspect migration replication health, source change rate, lag, database compatibility, authentication, connection-string cutover and residual transaction state. These checks primarily test Online migration with controlled cutover and validation instead of the critical system behavior in this case. Examine: Inspect installed Arc agent/extensions, SQL resource inventory, policy assignments, supported assessment features, connectivity and data residency needs.

Collect source version, compatibility level, blockers, logins, SQL Agent jobs, linked servers, TDE key dependencies, storage size and application integration map. These checks primarily test Azure SQL migration assessment and feature compatibility instead of the critical system behavior in this case. Examine: Inspect migration replication health, source change rate, lag, database compatibility, authentication, connection-string cutover and residual transaction state.

Inspect predicate selectivity, partition elimination, split/merge plans, aligned indexes, compression CPU overhead and real data size reduction. These checks primarily test Table partitioning compression and scale-out tradeoffs instead of the critical system behavior in this case. Examine: Inspect migration replication health, source change rate, lag, database compatibility, authentication, connection-string cutover and residual transaction state.

Compare database offering capabilities, deployment parameters, identity, networking, Fabric integration support, source control and tenant governance before provisioning. These checks primarily test Azure SQL deployment automation and Fabric SQL integration boundary instead of the critical system behavior in this case. Examine: Inspect migration replication health, source change rate, lag, database compatibility, authentication, connection-string cutover and residual transaction state.