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Explains how TTL choices balance freshness, load reduction, user expectations, and operational safety.
Chooses heap-focused GC evidence or complete process state based on memory growth, roots, hangs, crashes, locks, and collection risk.
Chooses a sequential collection from size, mutation, access pattern, memory locality, and the behavior callers actually need.
Avoids hidden quadratic work by indexing repeated matches while preserving duplicate, missing, ordering, and memory semantics.
Compares test doubles by purpose and explains when a real dependency provides a more trustworthy test.
Chooses a type shape from identity, value semantics, mutation, copy cost, default values, and serialization boundaries.
Maps business invariants and stale-data tolerance to strong or eventual consistency, explicit failure behavior, reconciliation, and user-visible state.
Creates valid objects with clear intent while choosing construction patterns proportionally to optional data, validation, and lifecycle complexity.
Reason through mitigation choices during a live App Service production incident.
Maps creation, updates, asynchronous work, validation, authorization, conflicts, and failures to useful HTTP outcomes.
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.
Selects small structural and behavioral patterns from the problem being solved instead of pattern-name recognition.
Separates ordinal identity comparison from culture-aware display ordering and keeps hash-based collection semantics consistent.
Chooses database test infrastructure by the provider behavior a test must prove.
Builds a proportionate test plan from blast radius, failure probability, detectability, and reversibility.
Chooses between sequence, counted, indexed, set, and dictionary contracts based on required operations and complexity.
Chooses counters, traces, stack snapshots, GC dumps, full dumps, or a profiler from the production symptom and investigation cost.
Explains how unit, integration, contract, component, and end-to-end tests cover different failure boundaries.
Implements chunking for batch processing without dropping final partial batches.
Explains inward dependency flow, stable business policy, adapters, and when a smaller project structure is enough.
Explains what a lambda captures, how captured state is shared, and why a long-lived callback can retain more memory than expected.
Review async .NET code that does not accept or propagate CancellationToken and explain the practical production risk.
Uses complexity as a first filter while accounting for constants, locality, allocations, data size, and the complete operation sequence.