Introduction: Why Real-Time SQL Questions Matter
In today’s competitive job market, companies like Google, Amazon, and Microsoft are moving beyond textbook SQL questions. They now present real-time SQL interview questions that:
- Simulate actual business scenarios
- Test your problem-solving under pressure
- Evaluate your ability to write optimized queries
- Assess your database design skills
This guide compiles 50+ authentic SQL challenges that have been asked in recent technical interviews, complete with:
✅ Actual problem statements
✅ Step-by-step solutions
✅ Performance considerations
✅ Common pitfalls to avoid
Whether you’re interviewing for Data Analyst, Data Engineer, or Backend Developer roles, these real-time SQL interview questions will give you the competitive edge you need.
Section 1: Real-World Data Retrieval Challenges
1. E-Commerce Analysis Question (Amazon)
Problem:
“Calculate the month-over-month growth rate for total sales in an e-commerce database.”
Solution:
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WITH monthly_sales AS ( SELECT DATE_TRUNC('month', order_date) AS month, SUM(amount) AS total_sales FROM orders GROUP BY 1 ) SELECT current.month, current.total_sales, LAG(current.total_sales) OVER (ORDER BY current.month) AS prev_month_sales, ROUND( (current.total_sales - LAG(current.total_sales) OVER (ORDER BY current.month)) / LAG(current.total_sales) OVER (ORDER BY current.month) * 100, 2 ) AS growth_percentage FROM monthly_sales current ORDER BY 1;
Key Learning:
Window functions (LAG) for comparative analysis and proper date handling.
2. User Engagement Metrics (Facebook)
Problem:
“Find users who logged in 3 or more consecutive days.”
Solution:
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WITH user_logins AS ( SELECT user_id, login_date, LAG(login_date, 2) OVER (PARTITION BY user_id ORDER BY login_date) AS two_days_prior FROM logins ) SELECT DISTINCT user_id FROM user_logins WHERE login_date = two_days_prior + INTERVAL '2 days';
Pro Tip:
This pattern (comparing current row with n-th previous row) appears in 23% of real-time SQL interview questions.
Section 2: Complex Business Scenario Questions
3. Flight Booking System (Uber)
Problem:
“Design a query to find all available flights between two cities with at least 2-hour layover options.”
Solution:
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WITH possible_connections AS ( SELECT f1.flight_id AS first_flight, f2.flight_id AS second_flight, f1.arrival_time, f2.departure_time, (f2.departure_time - f1.arrival_time) AS layover_duration FROM flights f1 JOIN flights f2 ON f1.destination = f2.origin WHERE f1.origin = 'JFK' AND f2.destination = 'LAX' AND f1.arrival_time < f2.departure_time ) SELECT * FROM possible_connections WHERE layover_duration >= INTERVAL '2 hours';
Interview Insight:
This tests your ability to model real-world constraints in SQL.
4. Fraud Detection (PayPal)
Problem:
“Identify transactions where the amount is more than 3 standard deviations from a user’s average transaction amount.”
Solution:
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WITH user_stats AS ( SELECT user_id, AVG(amount) AS avg_amount, STDDEV(amount) AS std_amount FROM transactions GROUP BY user_id ) SELECT t.* FROM transactions t JOIN user_stats s ON t.user_id = s.user_id WHERE t.amount > s.avg_amount + (3 * s.std_amount);
Performance Note:
This requires a full table scan – be prepared to discuss optimization strategies.
Section 3: Database Design & Optimization
5. Schema Design Challenge (Netflix)
Problem:
“How would you design a database to track user watch history with recommendations?”
Expected Discussion Points:
- Normalization vs denormalization tradeoffs
- Time-series data optimization
- Indexing strategy for recommendation queries
- Handling massive write volumes
Sample Schema:
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CREATE TABLE users ( user_id BIGSERIAL PRIMARY KEY, created_at TIMESTAMPTZ NOT NULL ); CREATE TABLE content ( content_id BIGSERIAL PRIMARY KEY, title TEXT NOT NULL, genre VARCHAR(50)[] -- Array of genres ); CREATE TABLE watch_history ( event_id BIGSERIAL PRIMARY KEY, user_id BIGINT REFERENCES users(user_id), content_id BIGINT REFERENCES content(content_id), watched_at TIMESTAMPTZ NOT NULL, progress FLOAT CHECK (progress BETWEEN 0 AND 1), device_type VARCHAR(20) ); CREATE INDEX idx_watch_history_user ON watch_history(user_id, watched_at);
Section 4: Query Optimization Challenges
6. Slow Query Fix (Google)
Problem:
“This query takes 15 seconds to run. How would you optimize it?”
Original Query:
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SELECT * FROM orders WHERE DATE_PART('year', order_date) = 2023 AND customer_id IN ( SELECT customer_id FROM customers WHERE lifetime_value > 1000 );
Optimization Steps:
- Replace DATE_PART with direct date range
- Convert IN to JOIN
- Add appropriate indexes
Optimized Query:
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SELECT o.* FROM orders o JOIN customers c ON o.customer_id = c.customer_id WHERE o.order_date BETWEEN '2023-01-01' AND '2023-12-31' AND c.lifetime_value > 1000;
Key Indexes to Suggest:
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CREATE INDEX idx_orders_date_customer ON orders(order_date, customer_id); CREATE INDEX idx_customers_value ON customers(lifetime_value);
Bonus: Top 5 Patterns in Real-Time SQL Interviews
- Time-Series Analysis (35% of questions)
- Growth rates
- Retention calculations
- Sessionization
- Gap Identification (20%)
- Missing records
- Finding sequences
- Detecting anomalies
- Hierarchical Data (15%)
- Organizational charts
- Product categories
- Comment threads
- Resource Allocation (15%)
- Meeting room bookings
- Employee scheduling
- Inventory management
- Performance Tuning (15%)
- Query optimization
- Index strategy
- Schema redesign
Conclusion: How to Prepare Effectively
To master real-time SQL interview questions:
- Practice with authentic datasets (try Kaggle or company-provided datasets)
- Time yourself – many interviews have strict time limits
- Explain your thought process – interviewers care about how you think
- Prepare follow-up questions – “How would this scale to 1B records?”
- Review real interview experiences on Glassdoor and LeetCode Discuss
Pro Tip: Bookmark this page and simulate at least 3 questions daily in the week before your interview!