SQL Server Interview Questions

Use unit tests with mocks or fakes for external dependencies, integration tests against a real database or message broker when appropriate, and contract tests for APIs. SQL Server should have clear inputs/outputs so tests remain fast and deterministic.

Log structured events with correlation IDs, track latency and error rates, and alert on SLO breaches. For SQL Server, capture enough context to reproduce failures without logging secrets such as passwords or tokens.

Auth often gates access to endpoints or resources that rely on SQL Server. Apply least privilege, validate tokens or sessions at the boundary, and never trust client-side checks alone. Mention OAuth, JWT, or session cookies as appropriate to the stack.

Separate config from code using environment-specific settings, secrets managers, and twelve-factor practices. Document required variables for SQL Server so deployments to staging and production remain repeatable and auditable.

Consider idempotency, status codes, pagination, versioning, and error payloads. SQL Server should not leak internal exceptions to clients; return consistent error shapes and document them in OpenAPI or similar specs.

Catch exceptions at appropriate layers, map them to user-safe messages, retry transient failures with backoff where suitable, and record failures for operators. Avoid swallowing errors silently—failed SQL Server operations should be visible in logs and metrics.

Use the official .NET docs for SQL Server, language or framework references, and reputable community guides. Bookmark release notes and migration guides when upgrading versions, since SQL Server behavior can change between releases.

Copying snippets without understanding why SQL Server works leads to fragile code. Beginners often skip error handling, tests, or edge cases. Slow down, trace execution step by step, and validate assumptions with small experiments.

Describe where SQL Server runs relative to middleware, endpoints, and DI. Mention hosting (Kestrel) and how configuration binds for SQL Server.

async/await, records, nullable reference types, and LINQ. Show how they improve correctness for SQL Server.

xUnit + Moq/NSubstitute, WebApplicationFactory for integration, and in-memory providers when appropriate. Keep SQL Server tests deterministic.

Singleton vs scoped vs transient depends on state and DbContext rules. Justify the lifetime for SQL Server with a concrete example.

Options pattern, validation on start, and secrets outside source control. Document required settings for SQL Server.

ILogger with structured properties, correlation IDs, and no secrets in logs. Map SQL Server exceptions to problem details for APIs.

Middleware for cross-cutting pipeline concerns; filters for MVC/minimal APIs specifics. Pick the narrowest layer that fits SQL Server.

Follow team conventions, split concerns into testable units, handle edge cases, and document assumptions. Review similar modules in the codebase, add observability, and ship incrementally with feature flags if SQL Server is risky.

Watch for bottlenecks, shared state races, config drift, and unbounded resource usage. Load-test SQL Server paths, set limits, and plan horizontal scaling or caching before traffic spikes.

One approach optimizes simplicity and time-to-market; the other optimizes performance, flexibility, or compliance. Choose based on team skill, traffic, and maintenance horizon—there is rarely a single best answer for SQL Server.

Reproduce in staging, check logs/metrics/traces, narrow scope with binary search deploys, and write a postmortem. Fix SQL Server root cause, add regression tests, and improve alerts so similar failures are caught earlier.

Check correctness, tests, naming, error handling, security, and performance. Ask whether SQL Server belongs in this layer, if docs updated, and if rollback is safe.