Top 30 Most Common Sql Query Interview Questions For 5 Years Experience You Should Prepare For
Top 30 Most Common Sql Query Interview Questions For 5 Years Experience You Should Prepare For
What SQL interview questions should a candidate with 5 years' experience expect?
Short answer: Expect a mix of behavioral, design, optimization, and advanced query questions — plus a handful of real-world problem-solving tasks that test performance and tradeoffs.
With ~5 years of experience, interviewers want to see that you can write correct queries, understand execution plans, optimize for scale, and make sound design choices. Prepare succinct answers that include metrics (rows, time, index choices) and examples where you improved latency or reduced cost. For specifics, many experienced-level lists and curated question sets are available from sources like Interview Kickstart and CodeSignal.
Takeaway: Focus on correctness, performance, design rationale, and measurable impact.
Top 30 SQL interview questions (with concise answers and example approaches)
Short answer: These are the high-value questions you should practice — with short model answers and hints to demonstrate depth.
What is the difference between INNER JOIN and LEFT JOIN?
Answer: INNER returns matched rows; LEFT returns all left rows plus matches (NULL otherwise). Example: Use LEFT to find missing matches and INNER for intersecting sets.
Takeaway: Mention null handling and performance implications.
How do you find duplicate rows in a table?
Answer: GROUP BY columns with HAVING COUNT(*) > 1 or use window functions: ROW_NUMBER() OVER (PARTITION BY cols) > 1.
Takeaway: Explain dedup strategy (delete vs mark) and transactional safety.
Write a query to get the nth highest salary.
Answer: Use denserank()/rownumber() ordered by salary DESC and filter by rank = n; or use LIMIT/OFFSET (DB-specific).
Takeaway: Discuss ties and performance for large tables.
Explain EXISTS vs IN.
Answer: EXISTS checks row-by-row existence and often performs better with correlated subqueries; IN is set-based and can misbehave with NULLs.
Takeaway: Note optimizer differences and NULL edge cases.
How do you optimize a slow query?
Answer: Review execution plan, look for table scans, add/selective indexes, rewrite joins/subqueries, materialize aggregates, limit returned columns.
Takeaway: Show a before/after example with metrics.
What is normalization? Give examples of 1NF–3NF.
Answer: Remove redundancy: 1NF atomic values, 2NF remove partial dependencies, 3NF remove transitive dependencies. Show a simple customer/order split.
Takeaway: Mention when denormalization is appropriate for performance.
How do indexes work and when to use composite indexes?
Answer: Indexes speed lookups by key; composite indexes are used when queries filter/sort on multiple columns in leading-column order.
Takeaway: Explain selectivity and write-impact tradeoffs.
Explain transactions and isolation levels.
Answer: Transactions provide atomicity; isolation levels (READ UNCOMMITTED → SERIALIZABLE) trade consistency vs concurrency. Give examples of phantom reads, non-repeatable reads.
Takeaway: Mention real-world defaults (e.g., READ COMMITTED) and when to escalate.
How do you handle NULLs in comparisons?
Answer: NULL ≠ anything; use IS NULL/IS NOT NULL, COALESCE for defaults; be careful with equality tests.
Takeaway: Show a sample predicate bug and fix.
What is a window function and when to use it?
Answer: Functions like ROW_NUMBER(), RANK(), SUM() OVER() compute aggregates across partitions without collapsing rows. Useful for running totals, ranking, gaps-and-islands.
Takeaway: Prefer window functions over self-joins for clarity and performance.
Explain how a query optimizer chooses an execution plan.
Answer: The optimizer uses statistics, cost estimates, and available indexes to choose joins, scan strategies, and ordering. Provide an example where stale stats caused a bad plan.
Takeaway: Emphasize the role of statistics and EXPLAIN output.
How do you implement pagination efficiently?
Answer: Avoid OFFSET for large offsets; use keyset (seek) pagination with WHERE > last_key ORDER BY key LIMIT n.
Takeaway: Explain user-experience vs server-cost tradeoffs.
How do you design a schema for multi-tenant applications?
Answer: Options: shared schema with tenant_id filters, separate schema per tenant, separate DBs. Discuss isolation, operational overhead, and scaling.
Takeaway: Justify choices with expected tenant size and SLA.
How to perform a rolling aggregate (e.g., 7-day moving average)?
Answer: Use window functions with frame clauses: AVG(metric) OVER (PARTITION BY id ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW).
Takeaway: Show attention to order and frame.
How to debug a deadlock?
Answer: Capture the deadlock graph/log, identify conflicting locks, minimize transaction time, order resource access, use retries/backoff.
Takeaway: Provide a real example if you have one.
Describe denormalization and when you’d use it.
Answer: Denormalization duplicates data to reduce joins (e.g., precomputed aggregates, cdc). Use when read performance outweighs storage/consistency costs.
Takeaway: Discuss update complexity and cache patterns.
How do you handle large bulk inserts/updates?
Answer: Use bulk-load utilities, batch commits, disable/nonclustered indexes during load, minimal logging (where safe), and consider partitioning.
Takeaway: Measure and tune for throughput vs downtime.
How do you design partitioning strategy?
Answer: Partition by date or hash to align with common query patterns; ensure partitions are balanced and manageable.
Takeaway: Explain pruning benefits and maintenance tasks.
What are common SQL injection risks and mitigations?
Answer: Use parameterized queries/prepared statements, least privilege, input validation, stored procedures, and ORM protections.
Takeaway: Emphasize secure coding and access controls.
Explain GROUP BY vs GROUPING SETS/CUBE/ROLLUP.
Answer: GROUP BY aggregates; ROLLUP/CUBE/GROUPING SETS compute multiple aggregation granularities in one pass.
Takeaway: Note performance benefits for multi-dimensional aggregates.
How do you perform string pattern matching efficiently?
Answer: Use full-text search indexes or trigram indexes for complex patterns; avoid leading wildcards on LIKE.
Takeaway: Recommend specialized indexes for search workloads.
What is a covering index?
Answer: An index that contains all columns needed by the query so the engine can satisfy it without visiting the table (index-only scan).
Takeaway: Show when adding included columns helps performance.
Explain how you would convert a slow correlated subquery to a faster form.
Answer: Transform to a JOIN with aggregation or use window functions to remove per-row subqueries.
Takeaway: Demonstrate with an example and timings.
How do you design for eventual consistency?
Answer: Use idempotent writes, versioning (optimistic concurrency), and asynchronous replication or event-driven updates.
Takeaway: Link design choices to user-facing consistency needs.
Describe your approach to query testing and profiling.
Answer: Use EXPLAIN/ANALYZE, measure on realistic data, profile CPU/IO, and run A/B tests for plan changes.
Takeaway: Stress reproducible measurements.
How do you select a primary key?
Answer: Prefer surrogate keys for simplicity (serial/UUID) unless natural key is stable and meaningful. Consider size and indexing.
Takeaway: Discuss uniqueness, immutability, and join patterns.
How to handle schema migrations in production?
Answer: Use backward-compatible changes (add columns), run in phases (deploy code that tolerates both schemas), and avoid heavy locks.
Takeaway: Emphasize rollout strategy and rollback plans.
Explain materialized views and when to use them.
Answer: Materialized views cache computed results for fast reads and must be refreshed (on-demand or incremental). Use for expensive aggregates.
Takeaway: Balance staleness vs speed.
How do you monitor database performance in production?
Answer: Track metrics: query latency, locks, wait stats, CPU/IO, slow query logs, and set up alerting and dashboards.
Takeaway: Show how you triage an incident with data.
Give an example of a challenging SQL problem you solved.
Answer: Tell a brief STAR story: Situation (slow report), Task (reduce runtime), Action (index + rewrite + partitioning), Result (runtime cut 90%, saved $X in compute).
Takeaway: Use measurable impact and technical depth.
Sources for question types and phrasing include curated lists from Interview Kickstart and CodeSignal.
How should I prepare for SQL interviews at the mid-senior level?
Short answer: Combine focused technical practice (30 targeted problems), system-design examples, and rehearsed STAR stories showing impact.
Week 1: Review core SQL (joins, aggregates, window functions), practice 10 query problems.
Week 2: Deep-dive optimization—EXPLAIN, indexes, partitioning; fix 10 slow queries.
Week 3: Design and security topics—normalization, schema changes, access control; rehearse 3 real case studies.
Week 4: Mock interviews: pair-program, timed coding, and behavioral practice using STAR/CAR (Context, Action, Result).
Preparation roadmap:
Use sources like GeeksforGeeks and Edureka to map common themes.
Takeaway: Practice deliberately with metrics and a consistent story about impact.
How should you structure answers to behavioral and system questions (STAR/CAR)?
Short answer: Start with a brief context, explain the task/goal, describe your specific actions, and end with measurable results.
STAR: "Situation: Our daily ETL took 6 hours. Task: Reduce time. Action: Rewrote aggregation using window functions, added partitioning and indexing. Result: ETL dropped to 30 minutes — 90% faster and under SLA."
CAR: "Context, Action, Result" is shorter for quick answers.
Examples:
Always quantify (time saved, percent improvement, cost reduction) and be ready to drill into technical trade-offs.
Takeaway: Structure builds clarity and persuades interviewers that you both think and deliver.
How to demonstrate SQL query optimization and performance tuning skills in an interview?
Short answer: Show a problem → show metrics and plan → apply concrete optimizations → show improved metrics.
Run EXPLAIN/ANALYZE and interpret operator costs.
Show index selection rationale (selectivity, coverage).
Replace correlated subqueries with joins or window functions.
Use partitioning for pruning and parallelism.
Use query rewrite to reduce data scanned and network I/O.
Concrete talking points:
Example snippet: show before/after runtime and rows scanned.
Resources: optimization checklists and examples from UPES Online and practical guides on GeeksforGeeks.
Takeaway: Interviewers expect measurable improvements and a defensible approach.
What database design and normalization topics will interviewers test?
Short answer: Expect questions on normalization (1NF–3NF), primary/foreign keys, indexing strategy, partitioning, and tradeoffs for denormalization.
Normal forms and examples of moving data across tables to remove redundancy.
When to denormalize for read performance (and how to keep denormalized data consistent).
Schema versioning and safe migration patterns.
Choosing keys, composite vs surrogate, and uniqueness constraints.
Explain:
Cite real-world examples: design a high-volume orders schema and explain indexing and partitioning choices.
Takeaway: Show that you can reason about both correctness and scalability.
How should you prepare for SQL security and access control interview questions?
Short answer: Know principle of least privilege, parameterization, auditing, and DBMS-specific security features.
Parameterized queries and avoiding SQL injection.
Role-based access controls, row-level security, and grant models.
Encryption at rest/in transit and key management basics.
Audit logging and monitoring.
Study items:
Reference common security practices from guides like Interview Kickstart and Edureka.
Takeaway: Be ready to propose defense-in-depth strategies with examples.
How to answer advanced SQL and subquery questions?
Short answer: Use window functions, CTEs, and efficient subquery transformations; demonstrate thinking about complexity.
Prefer CTEs for readability in multi-step queries (and explain inlining vs materialization).
Use correlated subqueries only when necessary; consider joins or window functions to scale.
Show examples like gaps-and-islands, hierarchical queries, and recursive CTEs.
Tips:
Sources such as CodeSignal and UPES Online provide example problems.
Takeaway: Demonstrate both correctness and scalability for advanced queries.
What common mistakes cause candidates to fail SQL interviews?
Short answer: Not quantifying impact, ignoring execution plans, over-relying on syntax without discussing tradeoffs, and failing to ask clarifying questions.
Writing a correct-looking query without considering NULLs, duplicates, or performance.
Not checking edge cases (ties, empty sets).
Failing to communicate assumptions (data size, indexing).
Not providing real metrics or follow-up questions about SLAs and constraints.
Typical pitfalls:
Fix: Talk through assumptions, show EXPLAIN analysis, and always quantify improvements.
Takeaway: Communicate clearly, validate assumptions, and show measurable outcomes.
How Verve AI Interview Copilot Can Help You With This
Verve AI acts as your quiet co-pilot in live interviews: it analyzes question context, suggests concise STAR/CAR-structured responses, and offers phrasing that highlights impact and metrics. Verve AI helps you prioritize technical details, avoid filler, and recover when stuck by providing on-the-fly suggestions and follow-up prompts. Practice sessions with Verve AI Interview Copilot also let you rehearse answers and get feedback tailored to your role and experience.
Takeaway: Use it to refine clarity and structure for technical and behavioral answers.
What Are the Most Common Questions About This Topic
Q: Can I prepare for optimization questions without a production DB?
A: Yes — use sample datasets, EXPLAIN output, and synthetic tests to simulate scale.
Q: How deep should schema-design answers be for 5 years experience?
A: Focus on tradeoffs, performance considerations, and migration strategy.
Q: Should I memorize answers or practice problem-solving?
A: Practice problem-solving — memorization falls apart under pressure.
Q: Are behavioral stories necessary for SQL interviews?
A: Yes — teams hire for impact and collaboration as much as technical skill.
Q: How many practice problems should I complete before interviews?
A: Aim for 30 high-quality problems plus 5 optimization case studies.
Q: Is it okay to say "I don't know" in an interview?
A: Yes — outline how you'd find the answer and propose a reasonable approach.
Conclusion
Recap: For a 5-year SQL role, prepare a balanced mix of query-writing, optimization, schema design, security, and behavioral stories. Focus on measurable results, explain tradeoffs, and practice reading execution plans. Use structured frameworks like STAR/CAR to present impact clearly.
Final nudge: Preparation and structure breed confidence — and practiced, metric-backed examples win interviews. Try Verve AI Interview Copilot to rehearse answers, get real-time phrasing suggestions, and enter interviews calm and prepared.
