Preparing for amazon sql interview questions requires targeted practice, clear communication, and comfort with multi-table, business-focused problems. This guide walks you from the phone screen basics to the advanced onsite challenges — with sample queries, optimization tips, mock strategies, and the exact question types Amazon likes to ask.

What is the amazon sql interview questions process and what should I expect

Amazon typically splits SQL interviewing into phone screens (short, focused) and onsite rounds (deeper, multi-step problems). Phone screens test fundamentals: date handling, simple JOINs, GROUP BY, and quick aggregate estimates after JOINs. Onsite rounds move to CASE expressions, subqueries/CTEs, and window functions like LEAD/LAG for running totals, gaps, and top-N per group InterviewQuery, StrataScratch.

How this affects your prep

• Phone screen: prioritize correctness under time (15–20 minutes), explain approach aloud, and estimate result counts quickly.

• Onsite: expect multi-table data modeling, edge cases, and performance questions. Be ready to write readable CTEs and use window functions for rankings and gaps.

What core concepts appear in amazon sql interview questions and how should I prioritize them

Focus your study where interviewers focus — joins, aggregation, filtering, and window functions account for most questions:

• Joins (INNER, LEFT, RIGHT) and multi-table merging using CTEs/subqueries — most common InterviewQuery, StrataScratch.

• Aggregation (COUNT, SUM, GROUP BY, HAVING) and business metrics like cancellation rate or average order value.

• Filtering and date handling (WHERE, ORDER BY, date_trunc/date functions).

• Window functions (ROW_NUMBER, RANK, DENSE_RANK, LEAD/LAG, FIRST_VALUE) for top-N, running totals, and gaps YouTube walkthroughs.

Prioritize: 70% practice on joins + aggregates, 20% on windows, 10% on pivots and advanced optimization.

What are top amazon sql interview questions with ready examples and solutions

Below are 13 practical question types you will see. Each entry lists the core concept, difficulty, and a short solution idea. For several problems you can copy-paste the snippets into your SQL editor to test.

1. Customers buying more than $100 (Basic) — WHERE, aggregates

   • Concept: Find customers whose total purchases > 100.

   • Example:

   SELECT customer_id, SUM(purchase_amt) AS total
   FROM purchases
   GROUP BY customer_id
   HAVING SUM(purchase_amt) > 100;

2. Rolling average revenue by month (Medium) — GROUP BY, CTEs, window functions

   • Use a CTE to compute monthly revenue, then apply a rolling average with window frames.

   WITH monthly_rev AS (
     SELECT DATE_TRUNC('month', order_date) AS month,
            SUM(amount) AS revenue
     FROM orders
     GROUP BY DATE_TRUNC('month', order_date)
   )
   SELECT month,
          revenue,
          ROUND(AVG(revenue) OVER (ORDER BY month ROWS BETWEEN 2 PRECEDING AND CURRENT ROW), 2) AS rolling_3mo_avg
   FROM monthly_rev
   ORDER BY month;

   (Pattern inspired by rolling-average examples on StrataScratch)[https://www.stratascratch.com/blog/amazon-sql-interview-questions/]

3. Top 3 salaries per department (Hard) — window functions (RANK/DENSE_RANK)

   SELECT *
   FROM (
     SELECT employee_id, department, salary,
            ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn
     FROM employees
   ) t
   WHERE rn <= 3;

   Use RANK vs ROW_NUMBER depending on handling ties.

4. Cancellation rate for trips (Medium) — aggregates, CASE

   SELECT DATE_TRUNC('month', trip_date) AS month,
          SUM(CASE WHEN status = 'cancelled' THEN 1 ELSE 0 END)::float / COUNT(*) AS cancel_rate
   FROM trips
   GROUP BY DATE_TRUNC('month', trip_date)
   ORDER BY month;

5. Customers who bought all products in Table Y (Hard) — subqueries, NOT EXISTS

   SELECT c.customer_id
   FROM customers c
   WHERE NOT EXISTS (
     SELECT 1 FROM products p
     WHERE NOT EXISTS (
       SELECT 1 FROM purchases pu
       WHERE pu.customer_id = c.customer_id AND pu.product_id = p.product_id
     )
   );

6. Pivot categories into columns (Advanced) — conditional aggregates or CASE

   SELECT product_id,
          SUM(CASE WHEN color='red' THEN 1 ELSE 0 END) AS red_count,
          SUM(CASE WHEN color='blue' THEN 1 ELSE 0 END) AS blue_count
   FROM inventory
   GROUP BY product_id;

7. Employee consecutive dates / status gaps (Advanced) — LAG / FIRST_VALUE

   SELECT employee_id, work_date,
          CASE WHEN LAG(status) OVER (PARTITION BY employee_id ORDER BY work_date) = status THEN 0 ELSE 1 END AS new_group
   FROM attendance;

   Use cumulative SUM over new_group to build groups of consecutive dates.

8. Predict row counts post-JOIN (Interview quick-check) — JOIN logic and cardinality estimation

   • Explain join keys and cardinality: if table A has 100 rows, table B has 10 with one match per A, result = 100; if B has many matches per A, explain multiplication.

9. Orders with latest shipment date per order (Medium) — window + first_value or ROW_NUMBER

   SELECT order_id, shipment_date, shipment_status
   FROM (
     SELECT order_id, shipment_date, shipment_status,
            ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY shipment_date DESC) AS rn
     FROM shipments
   ) s
   WHERE rn = 1;

10. Detect duplicate purchases (Medium) — GROUP BY HAVING COUNT > 1

    SELECT customer_id, product_id, COUNT(*) AS cnt
    FROM purchases
    GROUP BY customer_id, product_id
    HAVING COUNT(*) > 1;

11. Find users’ first purchase date and lifetime spend (Medium) — window or aggregate + MIN

    SELECT customer_id,
           MIN(order_date) AS first_purchase,
           SUM(amount) AS lifetime_spend
    FROM orders
    GROUP BY customer_id;

12. Price anomalies: negative or zero amounts (Edge case) — WHERE filters and data cleaning

    SELECT *
    FROM transactions
    WHERE amount <= 0;

13. Shipping lag time by vendor and percentile (Advanced) — window or percentile functions

    • Use PERCENTILE_CONT or approximate methods depending on engine.

Sources and extra examples: InterviewQuery and StrataScratch provide Amazon-tagged practice that mirrors these types InterviewQuery, StrataScratch.

How can I optimize queries commonly seen in amazon sql interview questions

Optimization is often a follow-up in onsite rounds. Key strategies:

• Filter early: apply WHERE clauses before GROUP BY to reduce rows scanned.

• Select only needed columns: avoid SELECT * when large rows are present.

• Use indexes for join/filter predicates when the engine supports them; explain index choices conceptually if you can’t create them during the interview.

• Limit early in exploratory queries (LIMIT) and only expand for final solution.

• Break complex logic into CTEs for readability; if performance is the concern, combine steps or rewrite CTEs into derived tables depending on the engine.
  Practical tips and common patterns for performance come up in StrataScratch and InterviewQuery writeups StrataScratch, InterviewQuery.

How should I practice amazon sql interview questions under time pressure

Practice plan (80/20):

• Start with basics: solve 20 JOIN + GROUP BY problems.

• Add window functions and CTEs: 20 problems that specifically require LEAD/LAG and window frames.

• Timeboxed mocks: 15–20 minutes per problem for phone-screen style problems; 40–50 minutes for onsite-style multi-step problems.

• Use platforms with Amazon-tagged problems: StrataScratch and InterviewQuery, and supplement with LeetCode SQL mediums and GeeksforGeeks Amazon lists StrataScratch, GeeksforGeeks.

• Review solutions: focus on why one approach is simpler and what edge cases (NULLs, duplicates, negative amounts) exist.

Mock strategy:

• Speak your plan before coding: e.g., “I’ll join orders to customers on customer_id, group by month, compute revenue, then window for rolling average.”

• After coding, run through small test cases verbally if running queries isn’t possible.

What are the most common mistakes candidates make on amazon sql interview questions and how can I avoid them

Common pitfalls and remedies:

• Misestimating JOIN outputs — explain expected row counts before writing the full query. Practice cardinality reasoning on common join scenarios InterviewQuery.

• Forgetting NULLs and duplicate keys — always ask about surrogate keys and NULL behavior.

• Overcomplicating answers — start with the simplest correct query, then add optimizations or edge-case handling.

• Not communicating business impact — frame results: “This identifies high-value customers for retention campaigns,” which proves product sense StrataScratch.

• Poor window-function framing — remember to PARTITION BY the right column and ORDER BY the logical time dimension.

How can I translate amazon sql interview questions into business-first answers during interviews

Amazon pays attention to metrics and business impact. For each solution:

• Start with intent: “This query finds customers with increasing spend to target for retention.”

• Explain metrics: define numerators/denominators (e.g., cancellation rate = cancelled / total).

• State assumptions and edge cases: “Assuming purchase_amt >= 0 and customer_id is unique in customers.”

• Communicate trade-offs: “I use a CTE for clarity; for scale we could materialize this or add an index on order_date.”

Interviewer follow-ups often test your ability to connect SQL output to product decisions — practice framing results in two sentences.

How can Verve AI Copilot help you with amazon sql interview questions

Verve AI Interview Copilot accelerates practice for amazon sql interview questions by simulating timed interviews and giving feedback on query logic. Verve AI Interview Copilot offers real-time hints and code templates, while Verve AI Interview Copilot can generate targeted question sets similar to Amazon phone screens and onsites. Try the coding-focused experience at https://www.vervecopilot.com/coding-interview-copilot or visit https://vervecopilot.com to learn more.

What are the most common questions about amazon sql interview questions

Q: How many amazon sql interview questions should I solve weekly
A: Aim for 10–15 timed problems weekly; mix phone-screen and onsite styles.

Q: Are window functions required for amazon sql interview questions
A: Yes, be comfortable with LEAD/LAG and ROW_NUMBER for onsite rounds.

Q: Should I memorize syntax for amazon sql interview questions
A: Know common functions and patterns; explain logic if you forget exact syntax.

Q: Which platforms best mirror amazon sql interview questions
A: Use StrataScratch and InterviewQuery for Amazon-tagged practice sets.

Q: How long is a typical amazon sql interview question on phone screens
A: Expect 15–20 minutes for phone screens, longer for onsite deep dives.

Final checklist for acing amazon sql interview questions

• Master joins and aggregates first; practice 50+ problems with an Amazon tag InterviewQuery, StrataScratch.

• Practice window functions (top-N, running totals, gaps) and pivots — these appear often onsite and in YouTube walkthroughs for gap problems YouTube example.

• Timebox your practice and explain your approach out loud. Start with a simple correct solution, then refine and talk about performance trade-offs.

• Prepare small sample datasets mentally to test edge cases (NULLs, duplicates, negative values).

• Review optimization habits: filter early, limit columns, and explain index ideas when relevant.

References

• Amazon SQL interview summaries and question sets on InterviewQuery: https://www.interviewquery.com/p/amazon-sql-interview-questions

• Practical Amazon SQL patterns on StrataScratch: https://www.stratascratch.com/blog/amazon-sql-interview-questions/

• GeeksforGeeks Amazon SQL question list: https://www.geeksforgeeks.org/sql/amazon-sql-interview-questions/

• YouTube gap/top-N window function walkthroughs: https://www.youtube.com/watch?v=120fme_lDJY and https://www.youtube.com/watch?v=d7pZNZbpdo8

Good luck — focus on joins, aggregates, and a few well-practiced window-function patterns and you’ll cover the majority of amazon sql interview questions employers ask.