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Chooses safe cached, partial, pending, read-only, or unavailable behavior and makes degraded state explicit to clients and operators.
Design list endpoints with pagination, filtering, sorting, limits, and database-aware performance behavior.
Uses read replicas selectively while accounting for stale reads, read-after-write behavior, routing, failover, query cost, and replication health.
Designs live updates with polling, server-sent events, or WebSockets while preserving authorization, ordering, scale, and fallback behavior.
Designs scheduled jobs with locking, idempotency, observability, retries, and safe operations across multiple instances.
Builds request and response contracts that tolerate independently deployed clients through explicit ownership, additive evolution, stable semantics, and measured deprecation.
Design request DTOs that protect API contracts from domain and persistence models.
Explains retry strategy for transient failures and poison-message handling for permanent failures.
Designs search and filtering with controlled query contracts, indexing, pagination, ranking, and operational limits.
Uses unique namespaces and correctly scoped fixtures so parallel tests improve speed without creating collisions.
Implements correct half-open interval overlap logic for booking validation.
Explains hot-key behavior, traffic concentration, partition pressure, payload size, and mitigation options.
Treats flakiness as an observable defect in timing, state, order, infrastructure, or product behavior.
Diagnoses hidden per-row EF Core queries and replaces them with a bounded query shape without creating an oversized join.
Explains a telemetry-driven path from slow endpoint symptoms to database, cache, dependency, CPU, or payload root cause.
Investigate a slow API by checking database queries, blocking async, dependency latency, serialization, logging, and runtime pressure.
Pairs a bounded isolated reproduction with telemetry and a disposal fix for leaked database connections.
Uses instance state, termination reasons, events, logs, configuration, probes, and resource evidence to distinguish common container startup failures.
Collects a bounded EventPipe CPU trace and reads stack evidence without confusing hot methods, blocked time, native work, or sampling artifacts.
Explains how hash-based collections use hash codes and equality, then chooses Dictionary or HashSet from the value the program needs to store.
Decide whether Azure SQL errors are transient platform faults or a real application/query regression.
Explains command messages versus event messages and how ownership changes their design.
Builds deterministic images efficiently by controlling context, restore layers, dependency inputs, and cache invalidation.
Uses Compose for reproducible local dependencies while preserving fast inner loops, realistic readiness, and clear differences from production orchestration.