Always On Interview Questions

Indexes speed reads but slow writes and consume storage. Analyze query plans, avoid over-indexing, and use composite indexes that match filter and sort columns. Always On workloads often benefit from covering indexes or partial indexes.

Transactions group operations atomically—either all commit or all roll back. When Always On involves multi-step updates, use appropriate isolation levels and handle deadlocks. Explain ACID properties if the database is relational.

Schedule regular backups, test restores, and consider point-in-time recovery for critical data. Always On changes should be recoverable; document RPO/RTO targets and automate backup verification.

Use EXPLAIN/EXPLAIN ANALYZE, review execution plans, check missing indexes, statistics freshness, and lock contention. Profile application queries and avoid N+1 patterns that amplify Always On load.

Use parameterized queries, least-privilege DB users, encryption at rest and in transit, and audit sensitive access. Never embed credentials in source code; rotate secrets and restrict network access to the database.

Match the database to consistency needs, query patterns, scaling model, and operational expertise. Sql Server excels when its data model and features align with Always On; be honest about trade-offs versus SQL, document stores, or caches.

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

Copying snippets without understanding why Always On 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.

Align models with reads/writes, normalize or denormalize intentionally, and plan for growth. Always On choices should match real query patterns.

Indexes speed reads but slow writes and consume storage. Analyze plans, avoid over-indexing, and match composite indexes to filter/sort columns used by Always On.

Transactions group operations atomically. When Always On involves multi-step updates, pick isolation levels carefully and handle deadlocks; cite ACID for relational stores.

Parameterized queries, least-privilege DB users, encryption in transit/at rest, and audited access. Never embed credentials in source for Always On.

Match consistency needs, query patterns, scaling model, and ops expertise. Be honest about trade-offs versus other SQL, document, or cache options for Always On.

Start with the problem Always On solves, show a minimal working example, and list the team conventions around it. Point them at official docs and one trusted internal example rather than random snippets.

Always On shows up often in production SQL-Server work—misunderstanding it leads to bugs, performance issues, or security gaps. Interviewers want clear explanations plus practical judgment.

Describe scaffolding a feature, configuring defaults, or validating input where Always On is required. Call out what goes wrong if the team skips conventions around it.

Read the official overview, run a minimal sandbox, learn key terms and common errors, then expand with a small project. Hands-on practice beats memorizing Always On definitions.

Frame Always On as improving reliability, speed, security, or maintainability. Use a product outcome analogy, then note how SQL-Server engineers apply Always On to deliver that outcome.

Define clear ownership boundaries for Always On, failure domains, caching, and observability. Plan capacity, multi-region needs if relevant, and explicit trade-offs between consistency, latency, and cost.

Map the Always On attack surface (injection, broken auth, data exposure, DoS). Layer defenses—validation, rate limits, least privilege, encryption, and regular audits.