C# Interview Question 2025

This article concerns real-time and knowledgeable  C# Interview Questions 2025. It is drafted with the interview theme in mind to provide maximum support for your interview. Go through these C# interview Questions to the end, as all scenarios have their importance and learning potential.

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1) What does the CLR actually do for a C# application?

• It loads your compiled IL, verifies it for safety, and JIT/AOT compiles it into native instructions.
• Manages memory with a generational garbage collector so you don’t manually free objects.
• Enforces type safety and security boundaries so bad casts and unsafe access are contained.
• Provides services like exceptions, threading, reflection, and interop under one runtime.
• Normalizes behavior across OSes so code is portable on .NET runtimes.
• Helps performance via JIT optimizations, tiered compilation, and runtime profiling.

2) How do value types and reference types impact performance and design?

• Value types (structs) live inline on the stack/inside objects; references point to heap objects.
• Value types avoid GC pressure for small, short‑lived data; copying large structs is expensive.
• Reference types are cheap to pass around but add allocations and GC work.
• Boxing a value type creates a heap object; avoid boxing in tight loops.
• Immutability is easier with reference types; structs should be small and immutable when possible.
• Pick the type based on size, lifetime, and semantics, not habit.

3) When would you prefer a record over a class?

• When the model is primarily data with value‑based equality semantics.
• Records give built‑in equality, with cloning, and succinct init syntax.
• They encourage immutability, which reduces bugs in shared code.
• Great for DTOs, queries, and event messages where identity isn’t the key.
• For rich domain behavior or mutable state, a class can be more appropriate.
• Be mindful of serialization defaults and positional records’ readability.

4) Why can boxing/unboxing hurt and how do you avoid it?

• Boxing allocates a new heap object when a value type is treated as object/interface.
• Unboxing copies the value back, adding work and possible churn in hot paths.
• It shows up via non‑generic collections, string.Format, or interface calls on structs.
• Use generics, typed collections, and overloads that stay strongly typed.
• Consider Span<T>/ReadOnlySpan<T> for performance‑critical paths.
• Watch perf counters; small allocations add up quickly.

5) How do IEnumerable<T> and IQueryable<T> differ in real projects?

• IEnumerable<T> represents in‑memory iteration; operations run in your process.
• IQueryable<T> builds an expression tree that providers translate (e.g., SQL).
• With IQueryable<T>, not every LINQ method is supported; translation limits apply.
• Overusing IEnumerable<T> with DB data can cause N+1 and heavy memory usage.
• Prefer composing queries as IQueryable<T> then materializing once.
• Validate generated SQL and avoid post‑materialization filtering by mistake.

6) What are the common LINQ deferred execution pitfalls?

• Queries aren’t executed until enumerated, which can surprise timing and side effects.
• Multiple enumerations re‑run the whole pipeline unless you cache results.
• Capturing changing variables in closures can alter results later.
• ToList() can fix timing but may load too much into memory.
• Be explicit where evaluation should happen (DB vs memory).
• Log generated SQL to catch accidental client‑side evaluation.

7) Async/await: what problems does it solve and where does it break?

• It frees threads during I/O waits, improving scalability on servers.
• It flattens callback hell, keeping code readable and exception‑friendly.
• Misuse (e.g., Result/Wait) can deadlock or starve thread pools.
• Forgetting to pass CancellationToken leads to hung tasks in shutdowns.
• Over‑awaiting tiny operations adds overhead; batch or pipeline when possible.
• Ensure async all the way down; mixing sync I/O blocks throughput.

8) Task vs ValueTask: how do you decide?

• Task is simpler and ideal when results are often asynchronous.
• ValueTask avoids allocations when operations often complete synchronously.
• Misusing ValueTask (multiple awaits, boxing) removes the benefit.
• For public APIs, default to Task unless profiling demands ValueTask.
• Measure hot paths; don’t optimize blindly.
• Keep API consistency—don’t mix both without reason.

9) Why is HttpClient reuse a big deal in services?

• Creating per‑request clients exhausts sockets and hurts throughput.
• Reusing via a factory/DI pools handlers and respects DNS refresh.
• It improves latency, reduces GC churn, and stabilizes connections.
• Configure timeouts and retries; defaults aren’t enough for production.
• Separate clients per downstream service for clearer policies.
• Monitor failures; transient errors need resilient handling.

10) What’s the real value of dependency injection in C# apps?

• It decouples construction from behavior, enabling testability and swaps.
• Encourages interface‑driven design and smaller, cohesive classes.
• Centralizes cross‑cutting concerns like logging and resilience.
• Prevents hidden singletons and global state that cause collisions.
• Works with lifetimes (scoped/transient/singleton) to manage resources.
• Makes feature toggles and A/B testing safer to roll out.

11) How do you avoid deadlocks with async code on servers?

• Never block on async (.Result, .Wait) inside request pipelines.
• Use async all the way, including DAL, HTTP, and messaging.
• Keep CPU work off the request path or use Task.Run sparingly.
• Configure thread‑pool growth and watch queue lengths under load.
• Use timeouts and cancellation to shed work instead of piling up.
• Add async diagnostics and dump stacks when stalls are suspected.

12) Where does locking go wrong in C#?

• Locking large critical sections kills throughput and invites contention.
• Lock on private objects, never on this or string literals.
• Avoid nested locks and unknown lock orders to prevent deadlocks.
• Prefer lock‑free or concurrent collections when possible.
• Measure with profilers to find hotspots; don’t guess.
• Keep state immutable where feasible to reduce locking needs.

13) When would you pick ConcurrentDictionary over a lock?

• When many readers/writers contend on a shared map.
• It provides atomic adds/updates without manual lock dance.
• Better granularity and throughput than a single coarse lock.
• Still profile; contention on single keys can limit gains.
• Use GetOrAdd and AddOrUpdate carefully to avoid heavy factory work.
• For bulk operations, sometimes a copy‑on‑write snapshot is faster.

14) GC generations: how do they change your design choices?

• Short‑lived objects die young in Gen0/1 cheaply; long‑lived ones reach Gen2.
• Fewer long‑lived allocations means fewer expensive Gen2 collections.
• Object pinning and large object heap (LOH) can fragment memory.
• Reuse buffers and avoid per‑request large arrays to reduce LOH pressure.
• Consider pooling (ArrayPool<T>) in high‑throughput code.
• Measure with memory profilers and ETW/PerfView to guide changes.

15) What’s the practical use of Span<T> and Memory<T>?

• They let you work with slices of data without extra allocations.
• Great for parsing, encoding, and high‑perf text/byte processing.
• Span<T> is stack‑only; Memory<T> can live on the heap/async.
• They reduce GC churn in tight loops and pipelines.
• Not a silver bullet—clarity can suffer if overused.
• Benchmark to justify complexity in non‑hot paths.

16) Exceptions: what’s your production strategy?

• Use exceptions for exceptional paths, not for flow control.
• Define clear domain exceptions and avoid catching Exception broadly.
• Add context (IDs, inputs) and log with structured fields.
• Translate to user‑friendly messages at boundaries (API/UI).
• Track rates; spikes can signal regressions or dependency failures.
• Prefer guard clauses and validations to fail early.

17) How do you design cancellation correctly?

• Accept CancellationToken on async APIs and pass it downstream.
• Treat cancellation as expected, not as an error to log loudly.
• Clean up promptly and release resources on cancel.
• Combine with timeouts to prevent stuck operations.
• Test race conditions where work completes at the cancel edge.
• Make idempotent handlers so retries don’t duplicate side effects.

18) What are the trade‑offs between EF Core and Dapper?

• EF Core speeds development with tracking, LINQ, and migrations.
• Dapper is lightweight, fast, and closer to SQL when you need control.
• EF can over‑fetch; Dapper can lead to SQL scattered across code.
• Choose EF for rich domain models; Dapper for hot read paths/reporting.
• Mixing is fine: EF for writes, Dapper for specific reads.
• Guard either with tests and performance budgets.

19) EF Core tracking vs No‑Tracking: when and why?

• Tracking keeps change snapshots for updates but costs memory/CPU.
• No‑tracking is ideal for read‑only queries and list views.
• Large projections with tracking can explode memory under load.
• You can switch per query to balance correctness vs speed.
• Use AsSplitQuery/AsSingleQuery consciously to manage SQL shape.
• Always inspect generated SQL before blaming the ORM.

20) How do you prevent N+1 queries in EF Core?

• Use projection to anonymous/DTO types instead of pulling graphs.
• Apply Include only when truly needed; prefers explicit loads.
• Batch related data or pre‑join in a single query.
• Add logging to spot repetitive parameterized selects.
• For aggregates, push computation to the database.
• Write focused queries; avoid lazy accidental enumerations.

21) When would you choose Minimal APIs over MVC?

• For small services, prototypes, or simple HTTP endpoints.
• Less ceremony and lower cold‑start overhead for quick routes.
• MVC still shines for complex routing, filters, and views.
• Team familiarity and tooling might favor MVC for large apps.
• Both support DI, filters, and middleware; pick based on complexity.
• Don’t mix styles randomly; keep consistency per service.

22) REST vs gRPC in C# backends: how do you decide?

• REST is universal, human‑readable, and cache‑friendly for public APIs.
• gRPC is binary, fast, and great for service‑to‑service communication.
• gRPC streams fit real‑time and high‑volume internal calls.
• REST tooling and browser access are simpler for teams and partners.
• Consider network constraints, latency targets, and client ecosystem.
• Sometimes both coexist: REST for outside, gRPC inside.

23) What’s your strategy for resilient outbound calls?

• Timeouts, retries with backoff, and circuit breakers as a baseline.
• Use bulkheads to isolate noisy neighbors from taking down the app.
• Add jitter to avoid thundering herds on recovery.
• Cache stable data and collapse duplicate requests when possible.
• Emit metrics on latency, failure rates, and retry counts.
• Keep policies per‑client so tuning is targeted.

24) Structured logging: why bother?

• Logs with key‑value pairs are queryable and consistent across services.
• They enable dashboards, alerts, and quick forensics on incidents.
• Correlate requests with trace IDs to trace flows end‑to‑end.
• Reduces guesswork vs free‑text logs during outages.
• Helps SREs build SLOs around error budgets and latency.
• Privacy by design: never log secrets or PII.

25) How do you manage configuration safely?

• Keep config external to code and immutable at runtime.
• Separate secrets from settings; use secure stores for secrets.
• Validate config at startup and fail fast on missing keys.
• Support environment‑based overrides without duplication.
• Reload only what’s safe; some changes need restarts.
• Version and document keys to avoid “mystery settings.”

26) What’s your approach to health checks in .NET services?

• Liveness checks ensure the process is responsive to restarts.
• Readiness checks verify dependencies before taking traffic.
• Include memory/CPU thresholds and queue depths when relevant.
• Make expensive checks cached to avoid self‑DoS.
• Expose standardized JSON for platforms to parse.
• Wire alerts so failing checks page the right team.

27) Background tasks: when to use IHostedService vs a queue?

• IHostedService fits lightweight, local recurring work.
• For durable, scalable work, use a queue/bus and worker service.
• Avoid running heavy jobs inside web processes that must scale quickly.
• Use cancellation tokens for graceful shutdowns.
• Persist checkpoints so restarts don’t repeat work endlessly.
• Keep idempotency to handle retries safely.

28) How do you decide between exceptions and error codes?

• Exceptions are clearer when failures are rare and serious.
• Error results avoid throw cost in hot, expected failure paths.
• Public APIs often prefer Try* patterns for predictable flows.
• Internally, use exceptions for truly unexpected situations.
• Align on one approach per layer to keep code uniform.
• Measure before prematurely optimizing exception cost.

29) What’s your take on domain models vs anemic models?

• Rich domain models encapsulate rules and reduce duplication.
• Anemic models push logic to services, risking scattered rules.
• Balance: keep invariants near data, orchestration in services.
• DTOs at boundaries protect domain from transport concerns.
• Tests become easier when behavior lives with the model.
• Don’t over‑engineer; start simple and grow as rules emerge.

30) CQRS: where does it help and where does it hurt?

• Helps when read/write needs differ in scale and shape.
• Clarifies intent and reduces accidental coupling in code.
• Adds complexity, duplicated models, and eventual consistency.
• Great for auditability and task‑based UIs.
• Overkill for small apps; keep it modular so you can back out.
• Introduce only with clear pain points and monitoring.

31) When would you use MediatR or a message bus inside a service?

• To decouple features and keep controllers thin.
• Enables cross‑cutting behaviors via pipeline behaviors.
• Risk of over‑indirection and hard‑to‑trace flows.
• Use sparingly; not every method needs a command/query.
• Pair with good logging and correlation IDs for traceability.
• Keep handlers small and focused on single responsibilities.

32) Serialization: System.Text.Json vs Newtonsoft.Json trade‑offs?

• System.Text.Json is faster and built‑in, good default choice.
• Newtonsoft has mature features (e.g., polymorphism) and ecosystem.
• Custom converters differ; migrating needs careful testing.
• Avoid dynamic magic in contracts; prefer explicit DTOs.
• Watch casing, null handling, and reference loops.
• Keep payloads lean; serialization cost grows with size.

33) Validation strategies that scale?

• Centralize rules with attributes or a validator library.
• Validate at the edge (API) and again at domain boundaries.
• Prefer declarative rules; avoid copy‑paste checks.
• Return actionable errors that map to fields.
• Keep client and server in sync with shared contracts.
• Log validation failures to spot UX or fraud issues.

34) How do you approach versioning public APIs?

• Use URL or header‑based versioning with clear deprecation paths.
• Preserve backward compatibility until clients migrate.
• Document changes and provide examples for each version.
• Avoid breaking renames; add fields instead of changing semantics.
• Sunset with telemetry on who still uses old versions.
• Contract tests keep versions honest over time.

35) Where does reflection make sense, and when is it risky?

• Useful for plugins, mapping, and dynamic wiring.
• It’s slower and bypasses compile‑time safety.
• Cache reflected metadata to avoid repeated costs.
• Prefer source generators or compiled expressions when possible.
• Lock down what assemblies can load to avoid security issues.
• Audit for private member access that could break in updates.

36) Source generators: why would you adopt them?

• They create code at build time, removing runtime reflection.
• Improve startup and steady‑state performance for mappings/parsers.
• Keep type safety and better diagnostics in the IDE.
• Adds complexity to builds and requires careful versioning.
• Great for repetitive boilerplate like DTO mappers.
• Roll out incrementally and measure impact.

37) Pattern matching in modern C#: real benefits?

• Expressive switch expressions reduce branching noise.
• Deconstruct and match shapes instead of nested ifs.
• Encourages non‑nullable, exhaustive handling of cases.
• Easier to evolve when new cases appear in enums/unions.
• Don’t sacrifice readability with over‑clever patterns.
• Test edge cases; pattern order and guards matter.

38) IDisposable vs IAsyncDisposable: when to implement which?

• Use IDisposable for synchronous resource cleanup.
• IAsyncDisposable fits async resources like network streams.
• Avoid finalizers unless wrapping unmanaged handles.
• Ensure double‑dispose is safe and idempotent.
• In DI, match lifetimes so containers dispose correctly.
• Document ownership so callers know who disposes.

39) How do you design libraries for other teams?

• Keep small, focused packages with clear purpose.
• Avoid leaking implementation types into public APIs.
• Provide good docs, samples, and semantic versioning.
• Don’t surprise: leave defaults safe and explicit.
• Add telemetry hooks so adopters can instrument.
• Maintain backward compatibility and migration guides.

40) What are common pitfalls with DateTime handling?

• Mixing local and UTC leads to subtle bugs and wrong math.
• Prefer DateTimeOffset for clarity across time zones.
• Convert at the boundaries; store and compute in UTC.
• Be careful with daylight‑saving transitions in schedules.
• Validate user input and always include offsets in APIs.
• Use clock abstractions to test time‑dependent code.

41) How do you guard against configuration‑drift bugs?

• Keep defaults and per‑env overrides minimal and documented.
• Use strong types for options; bind and validate at startup.
• Add config health endpoints for critical toggles.
• Track changes with version control or a config service.
• Add feature flags with clear owners and expiry dates.
• Include config in incident postmortems to tighten process.

42) Secrets management: what’s your checklist?

• Never store secrets in code, logs, or images.
• Use a managed secret store and short‑lived credentials.
• Rotate on a schedule and on incident triggers.
• Scope least privilege per app and per environment.
• Detect access anomalies and alert on misuse.
• Red‑team scenarios where secrets could leak.

43) What’s your strategy for high‑throughput JSON processing?

• Use System.Text.Json with precompiled metadata or source‑gen.
• Process streams instead of loading entire payloads in memory.
• Avoid intermediate strings; work with spans/buffers.
• Keep DTOs flat and avoid deep polymorphism.
• Benchmark with realistic payloads and concurrency.
• Monitor GC and CPU to catch regressions early.

44) How do you approach performance investigations in .NET?

• Reproduce with the smallest reliable scenario first.
• Measure with profilers and tracing; don’t trust hunches.
• Look for allocation hot spots and blocking I/O.
• Make one change at a time and re‑measure.
• Protect wins with benchmarks in CI.
• Document findings so the team learns patterns.

45) Where does caching help, and where does it backfire?

• Helps on expensive, stable computations and slow I/O.
• Backfires when data changes often or eviction is wrong.
• Choose memory, distributed, or CDN based on scope.
• Include cache keys in logs to debug misses.
• Set realistic TTLs and size limits to avoid blowups.
• Validate cache warms on deploys to prevent cold start pain.

46) What are common mistakes with DI lifetimes?

• Capturing scoped services in singletons causes leaks and bugs.
• Using transient for heavy objects churns CPU and GC.
• Singleton for mutable state invites race conditions.
• Match lifetime to dependency’s nature and usage.
• Use analyzers to catch mis‑wiring early.
• Add tests to assert lifetimes for critical types.

47) API pagination: why and how to design it?

• Prevents huge payloads and timeouts on large lists.
• Cursor‑based pagination handles changes better than offset.
• Include total counts only if you can afford the query.
• Keep consistent ordering to avoid duplicates/skips.
• Return links or tokens so clients can keep state light.
• Document limits and default page sizes clearly.

48) File uploads/downloads: what are the safe defaults?

• Limit size and type; validate before saving anywhere.
• Stream to avoid memory spikes and timeouts.
• Scan or sanitize if files are processed further.
• Use content‑disposition and correct MIME types.
• Store outside web roots with access controls.
• Log hashes and IDs for traceability.

49) How do you choose between synchronous and asynchronous APIs?

• Async is best for I/O‑bound work to free threads.
• Sync is fine for quick CPU work where async adds overhead.
• Keep both options only if you can test and maintain both.
• Consider call chains; one async link often requires the rest.
• Measure real latency/throughput gains, not theoretical.
• Favor consistency to reduce cognitive load for callers.

50) What’s your approach to graceful shutdown?

• Listen for termination signals and stop accepting new work.
• Cancel tokens to in‑flight operations with a deadline.
• Flush logs/metrics and close network listeners cleanly.
• Drain queues and checkpoint long jobs if possible.
• Orderly dispose DI scopes and singletons.
• Test shutdown paths under load to avoid surprises.

51) How do you keep controllers light and maintainable?

• Push business rules into services or domain objects.
• Use filters/middleware for cross‑cutting concerns.
• Validate early and return clear error contracts.
• Keep actions small; one responsibility per endpoint.
• Avoid static helpers that hide dependencies.
• Add integration tests to lock behavior down.

52) What are safe patterns for background scheduling?

• External schedulers or hosted services with durable stores.
• Idempotent jobs and unique job keys prevent duplicates.
• Backoff on failures and cap retries with alerts.
• Record last run time, result, and duration for ops.
• Guard external calls with resilience policies.
• Avoid cron in app config if you need central governance.

53) How do channels compare to queues in .NET?

• Channels are in‑process, lightweight, and back‑pressure aware.
• Great for pipelines inside a single service instance.
• Queues/buses add durability and cross‑process scaling.
• Channels vanish on crash; use only for ephemeral work.
• Choose based on failure domain and scale needs.
• Metrics on queue depths and lag keep systems healthy.

54) What’s your view on global using directives?

• They reduce noise for common namespaces across files.
• Keep the list short; don’t mask name collisions.
• Good in SDK‑style projects and shared libs.
• Avoid global usings for rarely used namespaces.
• Document the globals to help new team members.
• Review regularly as dependencies change.

55) Nullable reference types: how do you adopt safely?

• Turn it on per project and fix warnings gradually.
• Annotate public APIs first to improve consumer safety.
• Replace ! suppressions with real checks over time.
• Add tests around boundaries where nulls were common.
• Teach the team the warning meanings and intent.
• Track warning counts to measure progress.

56) What’s the risk with swallowing exceptions?

• You hide real failures, causing silent data loss or corruption.
• Retries may loop forever without backoff.
• Downstream systems receive partial or stale results.
• Debugging becomes guesswork without context.
• Catch narrow exception types and handle intentionally.
• Always log with enough detail to act.

57) How do you approach observability in C# services?

• Standardize logs, metrics, and traces across services.
• Emit request IDs and user/tenant context where needed.
• Collect dependency timings and error codes by type.
• Build dashboards before incidents, not after.
• Run chaos drills to validate what you see matches reality.
• Tie alerts to SLOs, not just raw thresholds.

58) Packaging and versioning libraries: what are your rules?

• Semantic versioning with clear changelogs and upgrade notes.
• Avoid breaking changes; deprecate first when possible.
• Keep dependencies minimal to reduce consumer conflicts.
• Strong names only when consumers require them.
• Provide symbols and source link for debugging.
• Test against multiple target frameworks.

59) How do you prevent over‑engineering with patterns?

• Start with the simplest thing that works.
• Add abstractions only when duplication or volatility appears.
• Prefer composition over inheritance for flexibility.
• Keep YAGNI in mind; defer features until justified.
• Review designs with real usage and performance data.
• Retire patterns that no longer pull their weight.

60) What lessons have you learned moving C# apps to production?

• Logging, metrics, and health checks are not “nice to have.”
• Timeouts and retries must be tuned per dependency.
• Memory and connection leaks show up only under real load.
• Blue/green or canary deploys save you from big rollbacks.
• Feature flags and config hygiene reduce firefighting.
• Clear runbooks and on‑call ownership keep outages short.