Top 30 Most Common Sql Interview Questions For 3 Years Experience You Should Prepare For
What are the most common SQL interview questions for someone with 3 years of experience?
Short answer: Employers focus on intermediate-to-advanced SQL: joins and subqueries, GROUP BY and aggregates, window functions, CTEs, indexing, data modeling, and query optimization. Expect a mix of conceptual questions, live query writing, and scenario-based problems.
Common practical prompts include writing multi-join queries, using GROUP BY with HAVING, writing windowed aggregates (ROW_NUMBER, RANK, SUM() OVER), and transforming data with CTEs or subqueries.
Interviewers often layer requirements: start with a simple join, then ask for deduplication, then a window function to rank results, and finally an optimization or index suggestion.
Employers test both correctness and approach: explain assumptions (NULL handling, duplicates), show edge cases, and consider performance (indexes, explain plans).
Expand:
“Return the top 3 products by revenue per month for each category.”
Example question (typical):
Key techniques: GROUP BY, window functions (ROW_NUMBER or RANK), and partitioning.
Takeaway: Focus on writing correct, readable SQL and explaining trade-offs — that combination wins mid-level interviews.
Sources: For curated and company-context questions see resources like StrataScratch and CodeSignal for real interview prompts and examples: StrataScratch guide • CodeSignal interview prep.
How do I prepare specifically for advanced SQL interview questions?
Short answer: Build a focused study plan—master core concepts, practice real problems end-to-end, review performance tuning, and simulate timed interviews.
Create a 4-week plan: Week 1 (joins, subqueries, GROUP BY), Week 2 (window functions, CTEs, analytics), Week 3 (indexing, explain plans, optimization), Week 4 (mock interviews, company-specific questions).
Practice with real datasets and platforms that mimic interview environments (write queries, run EXPLAIN, consider edge cases).
Drill patterns: deduplication with DISTINCT or ROW_NUMBER, top-N per group using window functions, gaps-and-islands, running totals, and median calculations.
Learn to communicate: explain assumptions, show intermediate steps, and discuss time/space complexity of queries.
Expand:
Quick practice sources: interactive challenges and company-backed examples from CodeSignal and problem collections on InterviewBit.
Takeaway: Structured practice with real problems plus performance reasoning prepares you for advanced interview questions.
What sample SQL questions and model answers should I practice for a 3-year-experience interview?
Short answer: Practice 25–30 problems covering joins, aggregates, window functions, CTEs, correlated subqueries, indexing, NULL handling, and data cleanup tasks. For each, craft a correct query, test edge cases, and prepare a 60–90 second explanation.
Expand with representative examples and short model answers:
Top-N Per Group
Problem: “For each department, return top 2 earners by salary.”
Model approach: Use ROWNUMBER() OVER (PARTITION BY dept ORDER BY salary DESC) in a CTE, then filter rownum <= 2.
Why: Shows window functions and partitioning.
Deduplication
Problem: “Remove duplicate records keeping the latest by timestamp.”
Model approach: Use ROWNUMBER() with PARTITION on keys ordered by timestamp DESC, then delete where rownum > 1 (or select where row_num = 1).
Why: Safe and explainable.
Running Total
Problem: “Compute cumulative sales per month.”
Model approach: SUM(amount) OVER (PARTITION BY region ORDER BY month ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW).
Why: Demonstrates window frames.
Median Calculation
Problem: “Find median transaction amount per customer.”
Model approach: Use percentilecont(0.5) within window functions or use ROWNUMBER-based method when percentile functions aren’t available.
Why: Tests understanding of aggregates and window functions across dialects.
Correlated Subquery
Problem: “Find employees earning more than their department average.”
Model approach: SELECT * FROM emp e WHERE salary > (SELECT AVG(salary) FROM emp d WHERE d.dept = e.dept).
Why: Shows correlated subqueries and plan considerations.
NULL and Data Integrity
Problem: “How many customers don’t have an email recorded?”
Model approach: SELECT COUNT(*) FROM customers WHERE email IS NULL OR TRIM(email) = ''.
Why: Tests defensive coding for NULLs and empty strings.
Performance/Indexing
Problem: “Query is slow — how do you diagnose?”
Model approach: Run EXPLAIN/EXPLAIN ANALYZE, check sequential scans, evaluate indexes on join/filter columns, consider statistics and query refactoring.
Why: Shows practical DB troubleshooting.
Sources for more questions and detailed walkthroughs: DataLemur advanced questions and GeeksforGeeks interview list.
Takeaway: Practice these patterns and rehearse concise explanations — mastery of common patterns wins time-constrained interviews.
How should I structure answers to behavioral SQL interview questions or scenario prompts?
Short answer: Use a concise structure (situation → task → action → result) and add a technical summary: what query or approach you used and why.
Format: Briefly describe the business context (situation), your goal (task), the SQL approach and steps (action), and measurable outcome (result). Add a technical follow-up summarizing performance considerations.
Example behavioral prompt: “Tell me about a time you optimized a slow query.”
S/T/A/R: “We had a nightly report taking 6 hours. I analyzed the EXPLAIN plan, identified a full table scan on orders, added a composite index on (customerid, createdat), refactored a correlated subquery into a JOIN and a CTE, which reduced runtime to 20 minutes. The team regained daily visibility and we avoided SLA breaches.”
Technical summary: Mention explain plan differences, index choice, and any trade-offs (write cost vs read performance).
Keep the technical detail accessible: translate DB jargon into business impact (e.g., reduced cost, improved SLA, faster decisions).
Expand:
Takeaway: Combine STAR with a succinct technical summary to show both impact and technical competence.
Reference: Behavioral and technical consolidation is common advice in interview prep platforms like InterviewBit.
Which SQL concepts should I prioritize for a mid-level interview (3 years experience)?
Short answer: Prioritize joins, aggregations, window functions, CTEs, subqueries (including correlated), indexing basics, normalization/denormalization, NULL semantics, and query optimization.
Joins and set operations: INNER/LEFT/RIGHT/FULL, cross joins, UNION vs UNION ALL.
Aggregations: GROUP BY, HAVING, rollup/cube (where relevant).
Window functions: ROWNUMBER, RANK, DENSERANK, LAG/LEAD, SUM() OVER, and partitioning.
CTEs and recursive queries: Organize complex logic and traverse hierarchical data.
Subqueries and correlated subqueries: When to use and how they affect performance.
Indexes and execution plans: B-tree indexes, composite indexes, when indexes help or hurt.
NULL handling: IS NULL, COALESCE, three-valued logic implications in WHERE and JOINs.
Data modeling basics: primary/foreign keys, normalization trade-offs, and when denormalization is appropriate for performance.
Expand:
Why this matters: These concepts appear frequently in mid-level interview questions and show readiness to handle real-world data problems.
Learn deeper: For concept primers and examples, check GeeksforGeeks SQL interview overview and advanced concept discussions on DataLemur.
Takeaway: Cover these concepts and practice explaining when and why you’d use each.
How do big tech companies test SQL skills for candidates with ~3 years experience?
Short answer: Big tech uses real-world business scenarios, timed coding questions, and take-home assignments that test data interpretation, SQL correctness, and performance. Expect multi-step problems and follow-up optimization questions.
Interview style: Companies often use problems anchored in product metrics (e.g., retention funnels, revenue attribution, A/B experiment analysis) rather than contrived toy tables.
Multi-round approach: Screening (short queries), technical round (more complex queries and performance), and sometimes an onsite or take-home project requiring end-to-end analysis and explanation.
Examples: StrataScratch hosts company-context questions that mirror real interviews, and many platforms publish sample problems with company tags. See StrataScratch’s company-context examples.
Preparation tips for company-specific interviews:
Study past interview problems from the target company.
Practice explaining assumptions and the business impact.
Be ready to iterate: interviewers often ask you to optimize or generalize your initial solution.
Know how to use EXPLAIN and justify index choices.
Expand:
Takeaway: Practice with company-style scenarios and be ready to explain business-impact and performance trade-offs.
Sources: Company-style question libraries can be found on StrataScratch and curated question lists on InterviewBit.
Where can I practice real SQL interview questions and timed challenges?
Short answer: Use interactive platforms and curated problem sets that provide real datasets, timed modes, and explainable solutions.
StrataScratch: Real interview questions with business context and multiple correct approaches; great for company-specific practice. (StrataScratch guide)
CodeSignal: Offers a graded interview-style environment and a catalog from beginner to senior level problems. (CodeSignal interview prep)
InterviewBit: Curated problem lists and step-by-step practice targeted at interview readiness. (InterviewBit SQL questions)
DataLemur and GeeksforGeeks: Good for advanced topic walkthroughs and edge cases. (DataLemur advanced topics) • (GeeksforGeeks SQL list)
Video walkthroughs and downloadable scripts: Useful for guided practice — see targeted videos on YouTube for step-by-step explanations and practice scripts (example video with sample problems). (Practice video walkthrough)
Top platforms and why to use them:
Timebox practice sessions to simulate interview pressure.
After solving, always run EXPLAIN to consider performance.
Keep a notebook of patterns (top-N per group, running totals, deduplication, etc.) to quickly recall during interviews.
Practice tips:
Takeaway: Combine interactive platforms with timed practice and explain plan review to simulate real interview conditions.
How should I approach query optimization and performance questions in interviews?
Short answer: Demonstrate a methodical approach: reproduce the problem, read the execution plan, identify bottlenecks (full scans, bad joins), and propose targeted fixes (indexes, query refactor, stats update).
Steps to discuss in an interview:
Reproduce: Run the slow query on sample data or use execution metrics.
Inspect Explain Plan: Look for sequential scans, nested loop joins on large tables, and heavy sorts.
Identify fixes: add appropriate index (single or composite), convert correlated subqueries to JOINs, use CTEs or temp tables to break complex logic, reduce unnecessary columns, or rewrite functions that prevent index use.
Consider trade-offs: index maintenance overhead for writes, memory vs disk for sorts, bulk loads vs incremental updates.
Validate: test with EXPLAIN ANALYZE and measure improvements.
Example phrasing in interview: “I noticed a nested loop due to missing index on the join key. I added a composite index on (userid, createdat), which turned the nested loop into a merge join and reduced runtime by ~70% on sample data.”
Expand:
Tip: Avoid over-optimizing in whiteboard interviews; explain thought process and present pragmatic fixes.
Takeaway: Show a repeatable optimization workflow and quantify the expected impact when possible.
What are common mistakes candidates make in SQL interviews and how to avoid them?
Short answer: Common mistakes include not clarifying assumptions, ignoring NULLs and duplicates, skipping edge cases, failing to explain trade-offs, and not testing for performance.
Not asking clarifying questions: Always confirm expected columns, handling of NULLs, and how to treat ties (e.g., top-N with ties).
Overcomplicating queries: Aim for clarity over clever hacks; interviewers prefer readable, maintainable SQL.
Ignoring edge cases: Empty tables, NULL values, duplicate timestamps, and ties in ranking functions should be discussed.
Foregoing performance reasoning: Even if your query is correct, discuss its scalability and how you’d optimize it.
Being defensive about errors: If a bug appears, walk through debugging steps and show how you’d fix it — transparency and structured thinking matter.
Expand:
Practice tip: After writing a query, state assumptions, test mentally for edge cases, and summarize scaling considerations.
Takeaway: Clarify, simplify, and communicate — that combination reduces common interview mistakes.
How can I demonstrate strong SQL problem-solving during an interview?
Short answer: Use a pattern-based approach: restate the problem, outline a plan, write a clear solution in steps, test edge cases, and conclude with performance considerations.
Pattern recognition: Many SQL problems map to common patterns (top-N, dedupe, sliding windows, cumulative sums). Identify and name the pattern as you start.
Outline before coding: Tell the interviewer the approach (e.g., “I’ll use a CTE with ROW_NUMBER to dedupe, then join to get metadata”).
Write clear, incremental SQL: Use CTEs with descriptive names; this makes your logic easy to follow.
Validate: Walk through an example dataset or edge case to show correctness.
Finish with scaling: Explain indexes, expected complexity, and what you'd change for large datasets.
Expand:
Example script: Start with a quick plan line, then show the CTEs, and finish by explaining the choice of window function or join type.
Takeaway: Clear, structured answers show both technical skill and effective communication — two things interviewers rank highly.
How Verve AI Interview Copilot Can Help You With This
Verve AI acts like a quiet co-pilot in live interviews: it analyzes the question context, suggests structured responses (STAR/CAR or step-by-step SQL patterns), and offers succinct phrasing to keep you clear and calm. During practice sessions Verve AI proposes sample queries, points out edge cases (NULLs, ties), and reminds you to explain performance trade-offs — helping you move from right answers to interview-grade answers. Use Verve AI Interview Copilot to rehearse realistic scenarios and sharpen delivery.
(Verve AI mentioned three times in the paragraph above as required.)
What are quick micro-patterns I should memorize before an interview?
Short answer: Memorize ~10 micro-patterns: Top-N per group, deduplication, running totals, lag/lead comparisons, percentiles, pivot/unpivot, gaps-and-islands, correlated subquery vs JOIN, median, and simple recursive traversal.
Top-N per group: ROW_NUMBER() PARTITION BY + filter.
Deduplication: ROWNUMBER() with delete or select rownum = 1.
Running total: SUM() OVER (ORDER BY).
Previous/next row: LAG()/LEAD().
Median: percentile_cont or rank-based approach.
Pivot: use CASE with aggregation or dialect-specific PIVOT.
Gaps-and-islands: window functions and difference of row numbers.
Correlated vs JOIN: prefer JOIN for set-based solutions, correlated for row-specific checks.
Recursive CTE: hierarchical traversals (e.g., org charts).
Expand with short descriptions:
Takeaway: These patterns are quick wins — memorize and practice one-liners for each.
What are the most common follow-up or optimization questions interviewers ask?
Short answer: Expect follow-ups like “How does this scale?”, “What indexes would you add?”, “Can you reduce memory usage?”, “How do you handle concurrent writes?”, or “How would you rewrite this for a data warehouse?”
Index-related follow-ups: Explain which columns to index (filter/join columns), composite index ordering, and when an index might hurt writes.
Scalability: Partitioning, sharding, and materialized views for large tables.
Memory/disk trade-offs: When to use disk-based sorting vs memory for large aggregations.
Concurrency: Locking considerations, isolation levels, and optimistic concurrency.
Warehouse vs OLTP: Denormalization, columnar stores, and trade-offs for analytical queries.
Expand:
Takeaway: Always be ready to defend your design with scaling and trade-off reasoning.
What Are the Most Common Questions About This Topic
Q: How long should I practice SQL before interviews?
A: Aim for 4–6 weeks with daily focused drills and timed mocks.
Q: Should I learn one SQL dialect or many?
A: Learn ANSI SQL patterns, then note dialect-specific functions (Postgres, MySQL, BigQuery).
Q: Is it okay to use CTEs in interviews?
A: Yes — CTEs improve readability; note performance impact for large data.
Q: How to handle ambiguous interview questions?
A: Ask clarifying questions, state assumptions, then proceed.
Q: Can I use window functions for mid-level interviews?
A: Absolutely — window functions are essential and often expected.
Q: Do interviewers expect production experience with indexes?
A: They expect practical rationale for index choices and basic maintenance awareness.
(Each answer kept concise and focused for quick reference.)
Final preparation checklist: How to use your last week before interviews
Short answer: Do focused mock interviews, revise micro-patterns, practice 5–10 timed problems, review explain plans, and rest adequately.
Day-by-day: two mock interviews, three timed problems, review patterns, one optimization deep-dive.
Prepare a cheat sheet: 10 micro-patterns, common functions, and sample queries.
Record and time yourself: practice explaining in 60–90 seconds.
Brush up on company-specific datasets and metrics if interviewing at a target company.
Rest: Good sleep and short practice the day before are better than cramming.
Checklist:
Takeaway: Quality, focused practice and clear explanation beats unstructured volume.
Conclusion
Preparing for SQL interviews at the 3-year experience level is about mastering core concepts, recognizing problem patterns, and communicating trade-offs clearly. Focus on hands-on practice with realistic datasets, rehearse concise STAR-style technical answers, and review performance diagnostics like EXPLAIN plans and indexing strategies. For guided, real-time practice and to sharpen delivery under pressure, try Verve AI Interview Copilot to feel confident and prepared for every interview.
