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Turns a production-only role and payload failure into the smallest durable API regression test.
Explains how tenant query filters help, where they can be bypassed, and what extra safeguards protect tenant isolation.
Aggregates log entries into per-minute error counts with deterministic output.
Partitions limits by authenticated user or trusted client address and returns useful 429 retry metadata.
Design and debug rate limiting while protecting the API without blocking legitimate users and integrations.
Explains rate-limit identity, endpoint cost, login and suggestion throttles, Redis counters, user experience, and observability.
Compares server-controlled output caching with HTTP cache semantics and defines safe keys, variation, invalidation, identity, and multi-instance behavior.
Explains API keys, client credentials, managed identity, mTLS, scopes, rotation, and per-client observability.
Loads a resource once, evaluates owner-or-capability authorization, and avoids trusting route ownership claims.
Explains admin list/edit/delete workflows with capability checks, audit events, confirmation, validation, and recovery.
Checks health, authentication, and one critical read flow after deployment without creating persistent business data.
Builds a small, immutable, non-root .NET runtime image with controlled inputs, external configuration, graceful lifecycle behavior, and a practical patching strategy.
Implements bounded filtering and deterministic keyset pagination without accepting arbitrary query expressions.
Creates a resource through an explicit request DTO and returns typed 201, 409, and validation outcomes.
Explains caching and revalidation choices for static, dynamic, user-specific, and frequently changing data.
Explains dependency duration, error rate, timeout rate, query tagging, Redis latency, HTTP downstream calls, and endpoint correlation.
Explains partition key selection for ordering, load distribution, hot keys, and consumer scalability.
Compares test doubles by purpose and explains when a real dependency provides a more trustworthy test.
Maps business invariants and stale-data tolerance to strong or eventual consistency, explicit failure behavior, reconciliation, and user-visible state.
Reason through mitigation choices during a live App Service production incident.
Chooses constraints, conditional writes, optimistic conflicts, or stronger isolation to protect a limited-resource reservation without unnecessary blocking.
Explains when optimistic UI is worth it, when to wait for server confirmation, and how to recover from rejected mutations.
Compares rollback, roll-forward, feature kill switches, scaling, and degraded mode as incident mitigations.
Chooses a deployment strategy from capacity, compatibility, risk, observability, and rollback needs, then defines progressive promotion and abort signals.