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Page 10 of 52
Diagnoses duplicate processing from Azure Service Bus lock loss or Storage Queue visibility behavior without repeating generic idempotency theory.
Explains high database CPU after an API change with practical .NET data-access examples and production trade-offs.
Turns business recovery targets into right-sized backup, restore, standby, failover, validation, ownership, and rehearsal plans.
Define logs, metrics, traces, alerts, and dashboards needed to operate an ASP.NET Core API in production.
Design API error handling across validation errors, conflicts, auth failures, not-found responses, and unexpected exceptions.
Explains batching, tracking control, idempotency, validation, and operational safety for large EF Core imports.
Explains how EF Core SaveChanges, transactions, and an outbox keep database changes and messages consistent.
Makes POST retries return one durable business outcome by combining scoped keys, request identity, atomic ownership, response replay, and reconciliation.
Designs realistic load, stress, spike, and soak tests from workload assumptions and SLOs while correlating client results with server bottlenecks.
Chooses response cache directives for public, private, sensitive, and revalidated API data without leaking user-specific content.
Explains how systems behave when the broker is down or slow and how backpressure protects callers and dependencies.
Builds request and response contracts that tolerate independently deployed clients through explicit ownership, additive evolution, stable semantics, and measured deprecation.
Explains how to reprocess event streams safely for new projections, bug fixes, and data repair.
Designs a settings service with defaults, validation, scoped preferences, caching, and safe rollout of new settings.
Uses field data, lab evidence, and release correlation to diagnose LCP, INP, and CLS regressions without guessing.
Investigate a production API slowdown caused by blocking async calls, thread-pool pressure, and dependency waits.
Investigate a slow API by checking database queries, blocking async, dependency latency, serialization, logging, and runtime pressure.
Uses instance state, termination reasons, events, logs, configuration, probes, and resource evidence to distinguish common container startup failures.
Diagnoses thread-unsafe scoped dependencies inside parallel execution and redesigns the data boundary without exhausting shared resources.
Investigate and fix an API where client retries create duplicate orders, payments, or records.
Turns a modular-monolith choice into enforceable code and data ownership rather than relying on folders and team discipline alone.
Improves architecture through protected behavior, incremental boundaries, and measurable delivery instead of a risky all-at-once rewrite.
Preserves failure context, catches only where recovery or translation is possible, and uses filters without hiding unrelated faults.
Restores client scheme, host, and address behind trusted proxies without accepting spoofed forwarding data from arbitrary callers.