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Page 5 of 26
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.
Handles SIGTERM, traffic draining, cancellation, background work, and termination deadlines without losing accepted work or hanging rollout.
Investigate and mitigate API failures caused by a slow or failing downstream dependency.
Explains optimistic concurrency with EF Core rowversion tokens, API conflict responses, and safe retry boundaries.
Defines what happens when several operations fail, cancellation arrives, or only part of a parallel batch completes, without orphaning work or hiding errors.
Separates process start from readiness, coordinates hosted-service startup, and drains work within a bounded shutdown window.
Handles an urgent production fix from verified impact and deployed provenance through minimal code, focused validation, controlled rollout, rollback readiness, and merge-forward.
Explains when to capture dumps, what to compare, and how to use roots without turning dumps into guesswork.
Draws a short atomic database boundary around one invariant while moving network side effects into explicit, recoverable workflow states.
Explains EF Core query performance diagnostics with practical .NET data-access examples and production trade-offs.
Explains a structured response to a failed production deployment in a .NET application.
Explains safe EF Core migrations with practical .NET data-access examples and production trade-offs.
Covers allocation rate, collection frequency, latency symptoms, and safe production steps for GC-heavy services.
Uses file upload processing to practice bounded memory, stream ownership, temporary files, and retained buffers.
Covers chunking, bounded concurrency, scoped dependencies, progress, retries, and avoiding full-dataset memory spikes.
Covers queue-backed work, idempotency, retries, visibility timeout, poison messages, progress tracking, and choosing Storage Queues or Service Bus.
Turns LOH theory into an API investigation around large payloads, buffers, serialization, and concurrency.
Explains safe migration strategy for Azure-hosted .NET APIs, including expand-contract changes, locking risk, rollback, and deployment order.
Gives a practical incident flow for memory growth using metrics, traffic correlation, heap evidence, and safe mitigation.
Keeps HttpContext inside the active request, avoids concurrent access, and copies only immutable data needed by later work.