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Page 9 of 14
Carries the version observed by the client into a conditional update and returns an explicit conflict instead of silently overwriting newer data.
Implements a database-backed idempotency boundary that replays one response and rejects key reuse with a different request.
Stores an idempotency key, request hash, and response in the same transaction as the created resource.
Uses a scoped idempotency key, request hash, unique constraint, and one transaction to create an order exactly once from the API perspective.
Implements a small in-memory fixed-window rate limiter with per-key counters.
Implements a small LRU cache using a dictionary plus linked list for O(1) get and put.
Explains debounce, cancellation, stale-response protection, loading indicators, and backend query safety for search UI.
Processes a large import in chunks with fresh DbContexts, idempotent keys, cancellation, and bounded tracker growth.
Verifies the exact request bytes, rejects stale signatures, deduplicates event ids, and queues processing durably.
Uses the real ASP.NET Core pipeline while replacing only environment-specific boundaries deliberately.
Explains delete-on-write, versioned keys, event-driven invalidation, and the race conditions around cache freshness.
Investigates a growing-data EF Core slowdown through one correlated request, generated SQL, representative parameters, execution plan, waits, query shape, measured repair, and guarded rollout.
Explains how to triage post-release 500 errors using deployment correlation, logs, traces, feature flags, and rollback safety.
Explains how to diagnose 401/403 spikes across token validation, identity provider issues, clock skew, deployment changes, and abuse.
Compares rollback, reset, schema, database, and tenant isolation without hiding transaction or parallelism limits.
Uses per-dependency concurrency limits, queues, pools, and workload partitions to stop one failing path from exhausting the whole API.
Explains transaction isolation levels with practical .NET data-access examples and production trade-offs.
Designs a reliable CI test system with fast feedback, deliberate test stages, bounded parallelism, visible flakiness, useful diagnostics, and clear ownership.
Explains when to store large payloads outside the broker and pass references through messages.
Explains the small set of Kubernetes resources needed to run and expose a replicated stateless API without drowning the answer in platform vocabulary.
Uses `AsSplitQuery` deliberately for a tracked aggregate with multiple collections and explains its consistency boundary.
Controls nondeterministic inputs without weakening production behavior or hard-coding test-only branches.
Uses declarative infrastructure, reviewed plans, environment parameters, protected deployment identities, state management, and drift control without hiding operational risk.
Relies on a database unique constraint and translates only the expected provider error into a domain conflict.