Real-world SQL Scenario based Interview Questions with Answers

Question 1: Calculate conversion rate for a sales funnel

Tables: events (user_id, event_name, event_date)

Events: signup → product_view → purchase

Requirement: Count users at each stage, compute conversion from signup to purchase

WITH stages AS (
  SELECT user_id,
    MAX(CASE WHEN event_name = 'signup' THEN 1 ELSE 0 END) AS signup,
    MAX(CASE WHEN event_name = 'product_view' THEN 1 ELSE 0 END) AS view,
    MAX(CASE WHEN event_name = 'purchase' THEN 1 ELSE 0 END) AS purchase
  FROM events
  GROUP BY user_id
)

SELECT 
  SUM(signup) AS signups,
  SUM(purchase) AS purchases,
  ROUND(100.0 * SUM(purchase) / SUM(signup), 2) AS conversion_rate
FROM stages;

Question 2: Build a monthly retention cohort

Tables: users (user_id, signup_date), logins (user_id, login_date)

Requirement: Cohort by signup month, show retained users by login month

WITH cohort AS (
  SELECT user_id, DATE_TRUNC('month', signup_date) AS cohort_month
  FROM users
),
activity AS (
  SELECT l.user_id, DATE_TRUNC('month', l.login_date) AS activity_month
  FROM logins l
)

SELECT 
  c.cohort_month,
  a.activity_month,
  COUNT(DISTINCT a.user_id) AS active_users
FROM cohort c
JOIN activity a ON c.user_id = a.user_id
GROUP BY c.cohort_month, a.activity_month
ORDER BY c.cohort_month, a.activity_month;

Question 3: Calculate monthly churn rate

Tables: subnoscriptions (user_id, start_date, end_date)

Requirement: Churned users per month, churn rate = churned / active users

WITH active_users AS (
  SELECT DATE_TRUNC('month', start_date) AS month, COUNT(DISTINCT user_id) AS active_users
  FROM subnoscriptions
  GROUP BY DATE_TRUNC('month', start_date)
),
churned_users AS (
  SELECT DATE_TRUNC('month', end_date) AS month, COUNT(DISTINCT user_id) AS churned_users
  FROM subnoscriptions
  WHERE end_date IS NOT NULL
  GROUP BY DATE_TRUNC('month', end_date)
)
SELECT 
  a.month,
  a.active_users,
  COALESCE(c.churned_users, 0) AS churned_users,
  ROUND(100.0 * COALESCE(c.churned_users, 0) / a.active_users, 2) AS churn_rate
FROM active_users a
LEFT JOIN churned_users c ON a.month = c.month
ORDER BY a.month;

Question 4: Calculate Daily Active Users

Table: user_activity (user_id, activity_date)

Requirement: DAU per day

SELECT activity_date, COUNT(DISTINCT user_id) AS dau
FROM user_activity
GROUP BY activity_date
ORDER BY activity_date;

Question 5: Revenue by marketing channel

Tables: orders (order_id, user_id, amount, order_date), users (user_id, channel)

Requirement: Total revenue per channel, monthly breakdown

SELECT 
  DATE_TRUNC('month', o.order_date) AS month,
  u.channel,
  SUM(o.amount) AS revenue
FROM orders o
JOIN users u ON o.user_id = u.user_id
GROUP BY DATE_TRUNC('month', o.order_date), u.channel
ORDER BY month, revenue DESC;

Question 6: Identify returning customers

Table: orders (order_id, customer_id, order_date)

Requirement: Customers with more than one order

SELECT customer_id
FROM orders
GROUP BY customer_id
HAVING COUNT(order_id) > 1;

Question 7: Top product by revenue per month

Table: sales (product_id, amount, sale_date)

WITH monthly_sales AS (
  SELECT 
    DATE_TRUNC('month', sale_date) AS month,
    product_id,
    SUM(amount) AS revenue
  FROM sales
  GROUP BY DATE_TRUNC('month', sale_date), product_id
),
ranked AS (
  SELECT *, RANK() OVER (PARTITION BY month ORDER BY revenue DESC) AS rnk
  FROM monthly_sales
)

SELECT month, product_id, revenue
FROM ranked
WHERE rnk = 1;

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✅ Complete SQL Roadmap in 2 Months

Month 1: Strong SQL Foundations
Week 1: Database and query basics
• What SQL does in analytics and business
• Tables, rows, columns
• Primary key and foreign key
• SELECT, DISTINCT
• WHERE with AND, OR, IN, BETWEEN
Outcome: You understand data structure and fetch filtered data.

Week 2: Sorting and aggregation
• ORDER BY and LIMIT
• COUNT, SUM, AVG, MIN, MAX
• GROUP BY
• HAVING vs WHERE
• Use case like total sales per product
Outcome: You summarize data clearly.

Week 3: Joins fundamentals
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• Join conditions
• Handling NULL values
Outcome: You combine multiple tables correctly.

Week 4: Joins practice and cleanup
• Duplicate rows after joins
• SELF JOIN with examples
• Data cleaning using SQL
• Daily join-based questions
Outcome: You stop making join mistakes.

Month 2: Analytics-Level SQL
Week 5: Subqueries and CTEs
• Subqueries in WHERE and SELECT
• Correlated subqueries
• Common Table Expressions
• Readability and reuse
Outcome: You write structured queries.

Week 6: Window functions
• ROW_NUMBER, RANK, DENSE_RANK
• PARTITION BY and ORDER BY
• Running totals
• Top N per category problems
Outcome: You solve advanced analytics queries.

Week 7: Date and string analysis
• Date functions for daily, monthly analysis
• Year-over-year and month-over-month logic
• String functions for text cleanup
Outcome: You handle real business datasets.

Week 8: Project and interview prep
• Build a SQL project using sales or HR data
• Write KPI queries
• Explain query logic step by step
• Daily interview questions practice
Outcome: You are SQL interview ready.

Practice platforms
• LeetCode SQL
• HackerRank SQL
• Kaggle datasets

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Glad to see the amazing response on SQL roadmap. ❤️

Today, let's start with the first topic of SQL roadmap:

✅ Introduction to SQL

SQL is the language you use to ask questions from data stored in databases. Companies store all important data in databases. Sales. Users. Payments. Inventory. When a manager asks a question, SQL pulls the answer.

What a database is
A database is an organized storage system for data. Think of it as a digital cupboard where each drawer holds related data. Each drawer is called a table.

What a table is
A table looks like an Excel sheet. It has rows and columns. Each table stores one type of data.

Example table: customers
- Columns
- customer_id
- name
- email
- city
- signup_date
- Rows
- Each row represents one customer
- One row equals one real-world record

How rows and columns work together
- Columns define what kind of data you store
- Rows hold actual values
- All rows follow the same column structure

Example row
- customer_id: 101
- name: Rahul
- email: rahul@gmail.com
- city: Pune
- signup_date: 2024-03-10

Why structure matters
- Clean structure makes data reliable
- Easy to filter, count, and analyze
- Required for accurate reporting

How SQL interacts with tables
- SQL reads data from tables
- SQL filters rows
- SQL selects columns
- SQL summarizes results

Simple SQL example
You ask the database to show names and cities of customers.

SELECT name, city FROM customers;

What happens behind the scenes
- Database scans the customers table
- Picks name and city columns
- Returns matching rows

Where you will use this daily
- Pull user lists
- Check sales numbers
- Validate data issues

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Today, let's move to the next topic of SQL roadmap:

✅ Database Basics: Primary Key and Foreign Key

Why Keys Exist
Databases store millions of rows. Keys help identify and connect data correctly. Without keys, data breaks fast.

Primary Key
- A primary key uniquely identifies each row in a table
- No two rows share the same primary key
- It never stays empty

Example Table: customers
- Columns: customer_id, name, email, city
- Primary key: customer_id

Why Primary Key Matters
- Prevents duplicate records
- Helps find a row fast
- Keeps data consistent

Foreign Key
- A foreign key links one table to another
- It creates relationships between tables

Example Table: orders
- Columns: order_id, customer_id, order_date, amount
- Foreign key: customer_id

Relationship Explained
- customers.customer_id is primary key
- orders.customer_id is foreign key
- One customer has many orders

Why Foreign Keys Matter
- Enable joins
- Prevent orphan records
- Maintain data integrity

Simple Join Idea
SQL matches customer_id in both tables. This gives customer name with order amount.

Where Beginners Go Wrong
- Using names instead of IDs
- Allowing duplicate primary keys
- Ignoring missing foreign key values

Checkpoint
- You understand primary keys
- You understand foreign keys
- You know how tables connect

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Today, let's move to the next topic of SQL Roadmap:

Basic SQL Queries: SELECT, WHERE, Filtering 🖥️

What SELECT Does
- SELECT chooses columns
- You decide what data you want to see
- Database returns only those columns

Example Table: customers
customer_id | name | city | signup_date
101 | Rahul | Pune | 2024-01-15
102 | Neha | Mumbai | 2024-02-10
103 | Amit | Delhi | 2024-03-05
104 | Priya | Pune | 2024-04-20

Basic SELECT

SELECT name, city FROM customers;

Output
name | city
Rahul | Pune
Neha | Mumbai
Amit | Delhi
Priya | Pune

What Happens
- Database scans all rows
- Returns name and city columns
- No filtering yet

Why SELECT Matters
- Smaller output
- Faster queries
- Clear analysis

What WHERE Does
- WHERE filters rows
- It answers conditions like who, when, how much

Think Like This
- SELECT decides columns
- WHERE decides rows

Basic WHERE Example

SELECT name, city FROM customers WHERE city = 'Pune';

Output
name | city
Rahul | Pune
Priya | Pune

Common Operators
- = equal
- != not equal
- > greater than
- < less than
- > =, <=

Example

SELECT name, signup_date FROM customers WHERE signup_date >= '2024-03-01';

Output
name | signup_date
Amit | 2024-03-05
Priya | 2024-04-20

Logical Filters
AND
- All conditions must match
Example: SELECT name FROM customers WHERE city = 'Pune' AND signup_date >= '2024-01-01';

Output
name
Rahul
Priya

OR
- Any condition can match
Example: SELECT name FROM customers WHERE city = 'Pune' OR city = 'Mumbai';

Output
name
Rahul
Neha
Priya

IN
- Shortcut for multiple OR conditions
Example: SELECT name FROM customers WHERE city IN ('Pune','Mumbai','Delhi');

Output
name
Rahul
Neha
Amit
Priya

BETWEEN
- Filters within a range
- Inclusive of start and end
Example: SELECT name FROM customers WHERE signup_date BETWEEN '2024-01-01' AND '2024-03-31';

Output
name
Rahul
Neha
Amit

Filtering Numbers
Example table: orders
order_id | customer_id | amount | order_date
1 | 101 | 3000 | 2024-01-16
2 | 102 | 6000 | 2024-02-11
3 | 103 | 4000 | 2024-03-06

SELECT order_id, amount FROM orders WHERE amount > 5000;

Output
order_id | amount
2 | 6000

Filtering Text
- Text values go inside quotes
- Case sensitivity depends on database

Filtering NULL Values
- NULL means missing value
- = NULL does not work
Correct way: SELECT name FROM customers WHERE city IS NULL;

Assume city is NULL for customer_id 103
Output
name
Amit

Exclude NULL: SELECT name FROM customers WHERE city IS NOT NULL;

Output
name
Rahul
Neha
Priya

How SELECT and WHERE Work Together
- FROM picks the table
- WHERE filters rows
- SELECT picks columns
- Result is sent back

Real Business Example
- Manager asks for Pune customers with orders above 5,000
- WHERE applies city and amount filters
- SELECT shows required columns

Assume orders table has customer_id 101 with amount 6000

SELECT name FROM customers WHERE city = 'Pune' AND customer_id IN (SELECT customer_id FROM orders WHERE amount > 5000);

Output
name
Rahul

Common Beginner Mistakes
- Using WHERE before FROM
- Forgetting quotes for text
- Using = NULL
- Writing SELECT * always

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Today, let's move to the next topic of SQL Roadmap:

✅ Basic SQL Queries: ORDER BY and LIMIT - Sorting and Controlling Output

Why Sorting
- Raw data has no order
- Sorted data reveals patterns
- Analysts sort data in almost every query

What ORDER BY Does
- ORDER BY sorts result rows
- Sorting happens after filtering
- Default order is ascending

Basic Syntax

SELECT column_name
FROM table_name
ORDER BY column_name;

Example Table: customers
| customer_id | name | city | signup_date |

Sort by Name

SELECT name, city
FROM customers
ORDER BY name;

- Rows sorted alphabetically by name (A to Z)

Descending Order
- Use DESC for reverse order

SELECT name, signup_date
FROM customers
ORDER BY signup_date DESC;

Use Cases
- Latest users first
- Highest sales first
- Recent transactions on top

Sorting Numbers

SELECT order_id, amount
FROM orders
ORDER BY amount DESC;

Sorting by Multiple Columns
- First column sorts primary
- Second column breaks ties

SELECT city, signup_date
FROM customers
ORDER BY city, signup_date DESC;

LIMIT Explained
- LIMIT restricts number of rows returned
- Used to preview data
- Used to get top results

SELECT name, signup_date
FROM customers
ORDER BY signup_date DESC
LIMIT 5;

Top N Queries

SELECT name, amount
FROM orders
ORDER BY amount DESC
LIMIT 10;

Filtering + Sorting + Limiting Together
Execution order:
1. FROM
2. WHERE
3. ORDER BY
4. LIMIT

SELECT name, amount
FROM orders
WHERE amount > 5000
ORDER BY amount DESC
LIMIT 3;

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Today, let's move to the next topic of SQL Roadmap:

✅ Basic SQL Queries: Aggregations and GROUP BY

• Why aggregations matter
• Raw rows hide patterns
• Businesses care about totals, averages, counts
• Aggregations turn rows into answers

• Common aggregate functions
• COUNT: counts rows
• SUM: adds values
• AVG: finds average
• MIN: finds smallest value
• MAX: finds largest value

• Examples
SELECT COUNT(*) FROM orders;  -- total orders 
SELECT SUM(amount) FROM orders;  -- total revenue 
SELECT AVG(amount) FROM orders;  -- average order value 
SELECT MIN(amount), MAX(amount) FROM orders;  -- spend range 

• GROUP BY
• Groups rows by a column
• Applies aggregation per group
• One result per group
SELECT customer_id, SUM(amount) FROM orders GROUP BY customer_id;  -- total spend per customer 
SELECT order_date, COUNT(*) FROM orders GROUP BY order_date;  -- daily order volume 

• Important rule
Every column in SELECT must be aggregated or present in GROUP BY.

• Using WHERE with GROUP BY
SELECT customer_id, SUM(amount) FROM orders
WHERE amount > 5000
GROUP BY customer_id;  -- high-value orders per customer 

Real business use:
• Revenue per customer
• Orders per day
• Average order value per city

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Today, let's move to the next topic of SQL Roadmap:

✅ HAVING vs WHERE

Why HAVING exists

- WHERE filters individual rows.
- Aggregations work on groups.
- You need HAVING to filter groups.

Key difference
- WHERE filters rows before grouping
- HAVING filters groups after aggregation

Think in order
- FROM reads table
- WHERE filters rows
- GROUP BY creates groups
- HAVING filters groups

Example table. orders
order_id | customer_id | amount | order_date

WHERE with aggregation

SELECT customer_id, SUM(amount)
FROM orders
WHERE amount > 5000
GROUP BY customer_id;

What this query does
- Removes orders below 5,000
- Groups remaining orders by customer
- Calculates total high value spend per customer

HAVING example

SELECT customer_id, SUM(amount)
FROM orders
GROUP BY customer_id
HAVING SUM(amount) > 20000;

What this query does
- Groups all orders by customer
- Calculates total spend per customer
- Keeps only customers with total spend above 20,000

WHERE vs HAVING side by side

Use WHERE when
- Filtering raw rows
- Condition does not involve aggregate functions

Use HAVING when
- Filtering aggregated results
- Condition uses SUM, COUNT, AVG

Combined WHERE and HAVING

SELECT customer_id, COUNT(*)
FROM orders
WHERE order_date >= '2024-01-01'
GROUP BY customer_id
HAVING COUNT(*) >= 5;

What this query does
- Keeps orders from 2024 onward
- Groups orders by customer
- Counts orders per customer
- Returns customers with at least 5 orders

Common beginner mistakes
- Using HAVING without GROUP BY
- Using WHERE with SUM or COUNT
- Mixing WHERE and HAVING logic

Interview one-liner
WHERE filters rows. HAVING filters groups.

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Today, let's move to the next topic of SQL Roadmap:

✅ SQL JOINS

What a JOIN is
• A JOIN combines data from two or more tables
• Tables connect using a common column
• That column is usually an ID
• JOIN answers questions one table cannot answer

Why JOINs exist
• Customer details sit in one table
• Orders sit in another table
• JOIN links customers to their orders

Example tables
customers 
customer_id | name | city 
orders 
order_id | customer_id | amount 

Connection
• customers.customer_id is primary key
• orders.customer_id is foreign key
• This shared column enables JOIN

Types of JOINs you must know
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• FULL JOIN
• SELF JOIN

INNER JOIN
• Returns only matching rows from both tables
• Drops anything without a match

SELECT name, amount 
FROM customers 
INNER JOIN orders 
ON customers.customer_id = orders.customer_id;

• Matches customers with their orders
• Shows only customers who placed orders
• Removes customers with no orders
• Removes orders without customers

LEFT JOIN
• Returns all rows from left table
• Matches data from right table
• Shows NULL when no match exists

SELECT name, amount 
FROM customers 
LEFT JOIN orders 
ON customers.customer_id = orders.customer_id;

• Returns every customer
• Shows order amount if available
• Shows NULL if customer never ordered

RIGHT JOIN
• Returns all rows from right table
• Matches data from left table
• Opposite of LEFT JOIN

SELECT name, amount 
FROM customers 
RIGHT JOIN orders 
ON customers.customer_id = orders.customer_id;

• Returns all orders
• Shows customer name if exists
• Shows NULL for missing customer data

FULL JOIN
• Returns all rows from both tables
• Matches where possible
• Shows NULL when no match

SELECT name, amount 
FROM customers 
FULL JOIN orders 
ON customers.customer_id = orders.customer_id;

• Shows all customers
• Shows all orders
• Includes unmatched data from both sides

SELF JOIN
• Table joins with itself
• Used for hierarchy or comparison

SELECT e.name, m.name AS manager_name 
FROM employees e 
LEFT JOIN employees m 
ON e.manager_id = m.employee_id;

• Matches employee with manager
• Uses same table twice
• Shows reporting hierarchy

JOIN Comparison Summary
• INNER JOIN: Only matching data
• LEFT JOIN: All left table rows
• RIGHT JOIN: All right table rows
• FULL JOIN: Everything from both tables
• SELF JOIN: Table joins itself

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If you want to Excel at using the most used database language in the world, learn these powerful SQL features:

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