📊 Interviewer: How do you remove duplicate records in SQL?

👋 Me: We can remove duplicates using DISTINCT, GROUP BY, or delete duplicate rows using ROW_NUMBER().


✅ 1️⃣ Using DISTINCT (to fetch unique values)

SELECT DISTINCT column_name
FROM employees;

👉 Returns unique records but does not delete duplicates.


✅ 2️⃣ Using GROUP BY (to identify duplicates)

SELECT name, COUNT(*)
FROM employees
GROUP BY name
HAVING COUNT(*) > 1;

👉 Helps find duplicate records.


✅ 3️⃣ Delete Duplicates Using ROW_NUMBER() (Most Important ⭐)
(Keeps one record and deletes others)

DELETE FROM employees
WHERE id IN (
  SELECT id FROM (
    SELECT id,
           ROW_NUMBER() OVER (
             PARTITION BY name, salary
             ORDER BY id
           ) AS rn
    FROM employees
  ) t
  WHERE rn > 1
);

🧠 Logic Breakdown:

- DISTINCT → shows unique records
- GROUP BY → identifies duplicates
- ROW_NUMBER() → removes duplicates safely


✅ Use Case: Data cleaning, ETL processes, data quality checks.

💡 Tip: Always take a backup before deleting duplicate records.

💬 Tap ❤️ for more!

📊 Essential SQL Concepts Every Data Analyst Must Know

🚀 SQL is the most important skill for Data Analysts. Almost every analytics job requires working with databases to extract, filter, analyze, and summarize data.

Understanding the following SQL concepts will help you write efficient queries and solve real business problems with data.

1️⃣ SELECT Statement (Data Retrieval)

What it is: Retrieves data from a table.

SELECT name, salary
FROM employees;

Use cases: Retrieving specific columns, viewing datasets, extracting required information.

2️⃣ WHERE Clause (Filtering Data)

What it is: Filters rows based on specific conditions.

SELECT *
FROM orders
WHERE order_amount > 500;

Common conditions: =, >, <, >=, <=, BETWEEN, IN, LIKE

3️⃣ ORDER BY (Sorting Data)

What it is: Sorts query results in ascending or descending order.

SELECT name, salary
FROM employees
ORDER BY salary DESC;

Sorting options: ASC (default), DESC

4️⃣ GROUP BY (Aggregation)

What it is: Groups rows with same values into summary rows.

SELECT department, COUNT(*)
FROM employees
GROUP BY department;

Use cases: Sales per region, customers per country, orders per product category.

5️⃣ Aggregate Functions

What they do: Perform calculations on multiple rows.

SELECT AVG(salary)
FROM employees;

Common functions: COUNT(), SUM(), AVG(), MIN(), MAX()

6️⃣ HAVING Clause

What it is: Filters grouped data after aggregation.

SELECT department, COUNT(*)
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;

Key difference: WHERE filters rows before grouping, HAVING filters groups after aggregation.

7️⃣ SQL JOINS (Combining Tables)

What they do: Combine tables.

-- INNER JOIN

SELECT orders.order_id, customers.customer_name
FROM orders
INNER JOIN customers
ON orders.customer_id = customers.customer_id;

-- LEFT JOIN

SELECT customers.customer_name, orders.order_id
FROM customers
LEFT JOIN orders
ON customers.customer_id = orders.customer_id;

Common types: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN

8️⃣ Subqueries

What it is: Query inside another query.

SELECT name
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);

Use cases: Comparing values, filtering based on aggregated results.

9️⃣ Common Table Expressions (CTE)

What it is: Temporary result set used inside a query.

WITH high_salary AS (
  SELECT name, salary
  FROM employees
  WHERE salary > 70000
)
SELECT *
FROM high_salary;

Benefits: Cleaner queries, easier debugging, better readability.

🔟 Window Functions

What they do: Perform calculations across rows related to current row.

SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS salary_rank
FROM employees;

Common functions: ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD()

Why SQL is Critical for Data Analysts
• Extract data from databases
• Analyze large datasets efficiently
• Generate reports and dashboards
• Support business decision-making

SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

Double Tap ♥️ For More

Essential Excel Functions for Data Analysts 🚀

1️⃣ Basic Functions

SUM() – Adds a range of numbers. =SUM(A1:A10)

AVERAGE() – Calculates the average. =AVERAGE(A1:A10)

MIN() / MAX() – Finds the smallest/largest value. =MIN(A1:A10)


2️⃣ Logical Functions

IF() – Conditional logic. =IF(A1>50, "Pass", "Fail")

IFS() – Multiple conditions. =IFS(A1>90, "A", A1>80, "B", TRUE, "C")

AND() / OR() – Checks multiple conditions. =AND(A1>50, B1<100)


3️⃣ Text Functions

LEFT() / RIGHT() / MID() – Extract text from a string.

=LEFT(A1, 3) (First 3 characters)

=MID(A1, 3, 2) (2 characters from the 3rd position)


LEN() – Counts characters. =LEN(A1)

TRIM() – Removes extra spaces. =TRIM(A1)

UPPER() / LOWER() / PROPER() – Changes text case.


4️⃣ Lookup Functions

VLOOKUP() – Searches for a value in a column.

=VLOOKUP(1001, A2:B10, 2, FALSE)


HLOOKUP() – Searches in a row.

XLOOKUP() – Advanced lookup replacing VLOOKUP.

=XLOOKUP(1001, A2:A10, B2:B10, "Not Found")



5️⃣ Date & Time Functions

TODAY() – Returns the current date.

NOW() – Returns the current date and time.

YEAR(), MONTH(), DAY() – Extracts parts of a date.

DATEDIF() – Calculates the difference between two dates.


6️⃣ Data Cleaning Functions

REMOVE DUPLICATES – Found in the "Data" tab.

CLEAN() – Removes non-printable characters.

SUBSTITUTE() – Replaces text within a string.

=SUBSTITUTE(A1, "old", "new")



7️⃣ Advanced Functions

INDEX() & MATCH() – More flexible alternative to VLOOKUP.

TEXTJOIN() – Joins text with a delimiter.

UNIQUE() – Returns unique values from a range.

FILTER() – Filters data dynamically.

=FILTER(A2:B10, B2:B10>50)



8️⃣ Pivot Tables & Power Query

PIVOT TABLES – Summarizes data dynamically.

GETPIVOTDATA() – Extracts data from a Pivot Table.

POWER QUERY – Automates data cleaning & transformation.


You can find Free Excel Resources here: https://t.me/excel_data

Hope it helps :)

#dataanalytics

Top 100 Data Analyst Interview Questions

✅ Data Analytics Basics
1. What is data analytics?
2. Difference between data analytics and data science?
3. What problems does a data analyst solve?
4. What are the types of data analytics?
5. What tools do data analysts use daily?
6. What is a KPI?
7. What is a metric vs KPI?
8. What is descriptive analytics?
9. What is diagnostic analytics?
10. What does a typical day of a data analyst look like?

Data and Databases
11. What is structured data?
12. What is semi-structured data?
13. What is unstructured data?
14. What is a database?
15. Difference between OLTP and OLAP?
16. What is a primary key?
17. What is a foreign key?
18. What is a fact table?
19. What is a dimension table?
20. What is a data warehouse?

SQL for Data Analysts
21. What is SELECT used for?
22. Difference between WHERE and HAVING?
23. What is GROUP BY?
24. What are aggregate functions?
25. Difference between INNER and LEFT JOIN?
26. What are subqueries?
27. What is a CTE?
28. How do you handle duplicates in SQL?
29. How do you handle NULL values?
30. What are window functions?

Excel for Data Analysis
31. What are pivot tables?
32. Difference between VLOOKUP and XLOOKUP?
33. What is conditional formatting?
34. What are COUNTIFS and SUMIFS?
35. What is data validation?
36. How do you remove duplicates in Excel?
37. What is IF formula used for?
38. Difference between relative and absolute reference?
39. How do you clean data in Excel?
40. What are common Excel mistakes analysts make?

Data Cleaning and Preparation
41. What is data cleaning?
42. How do you handle missing data?
43. How do you treat outliers?
44. What is data normalization?
45. What is data standardization?
46. How do you check data quality?
47. What is duplicate data?
48. How do you validate source data?
49. What is data transformation?
50. Why is data preparation important?

Statistics for Data Analysts
51. Difference between mean and median?
52. What is standard deviation?
53. What is variance?
54. What is correlation?
55. Difference between correlation and causation?
56. What is an outlier?
57. What is sampling?
58. What is distribution?
59. What is skewness?
60. When do you use median over mean?

Data Visualization
61. Why is data visualization important?
62. Difference between bar and line chart?
63. When do you use a pie chart?
64. What is a dashboard?
65. What makes a good dashboard?
66. What is a KPI card?
67. Common visualization mistakes?
68. How do you choose the right chart?
69. What is drill down?
70. What is data storytelling?

Power BI or Tableau
71. What is Power BI or Tableau used for?
72. What is a data model?
73. What is a relationship?
74. What is DAX?
75. Difference between measure and calculated column?
76. What is Power Query?
77. What are filters and slicers?
78. What is row level security?
79. What is refresh schedule?
80. How do you optimize reports?

Business and Case Questions
81. How do you analyze a sales drop?
82. How do you define success metrics?
83. What business metrics have you worked on?
84. How do you prioritize insights?
85. How do you validate insights?
86. What questions do you ask stakeholders?
87. How do you handle vague requirements?
88. How do you measure business impact?
89. How do you explain numbers to managers?
90. How do you recommend actions?

Projects and Real World
91. Explain your best project.
92. What data sources did you use?
93. How did you clean the data?
94. What insight had the most impact?
95. What challenge did you face?
96. How did you solve it?
97. How did stakeholders use your dashboard?
98. What would you improve in your project?
99. How do you handle tight deadlines?
100. Why should we hire you as a data analyst?

Double Tap ♥️ For Detailed Answers

🧠 SQL Interview Question (Moderate–Tricky & Identifying Users with Increasing Transactions)
📌

transactions(transaction_id, user_id, transaction_date, amount)

❓ Ques :

👉 Find users whose transaction amount strictly increases with every new transaction.

🧩 How Interviewers Expect You to Think

• Sort transactions by date for each user
• Compare each amount with the previous one
• Identify users whose amounts always increase

💡 SQL Solution

WITH t AS (
SELECT
user_id,
amount,
LAG(amount) OVER (
PARTITION BY user_id
ORDER BY transaction_date
) AS prev_amount
FROM transactions
)

SELECT user_id
FROM t
GROUP BY user_id
HAVING SUM(
CASE
WHEN prev_amount IS NOT NULL AND amount <= prev_amount
THEN 1 ELSE 0
END
) = 0;

🔥 Why This Question Is Powerful

• Tests understanding of LAG() with conditional logic
• Evaluates ability to validate patterns across sequential data
• Reflects real-world analytics like tracking user spending growth trends

❤️ React if you want more tricky real interview-level SQL questions 🚀

𝗛𝗼𝘄 𝗥𝗮𝘄 𝗗𝗮𝘁𝗮 𝗕𝗲𝗰𝗼𝗺𝗲𝘀 𝗥𝗲𝗮𝗹 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗩𝗮𝗹𝘂𝗲

Data creates impact only when it turns into decisions. The analytics process can be seen as a simple journey:

🔹 Data – Raw, messy information collected from systems, users, or transactions.

🔹 Sorted – Cleaning and organizing the data by removing duplicates and fixing inconsistencies.

🔹 Arranged – Analyzing the data through aggregation, grouping, and exploration to find patterns.

🔹 Presented Visually – Using charts and dashboards to make insights easy to understand.

🔹 Explained with a Story – Connecting insights to real business problems and context.

🔹 Actionable – Turning insights into better decisions and improvements.

📊 Great analysts don’t just analyze data — they turn it into decisions that create value.

🚀 Data Analyst Roadmap


First things first 👇
❌ Don’t buy expensive courses to become a Data Analyst.

💡 Consistency > Certifications > Courses

Skills and practice are what actually get you hired.


✅ Mandatory Skills for a Data Analyst

1️⃣ SQL

Practice as much as possible.

This is the most important skill for any Data Analyst.

📚 Resource
YouTube Channel: Ankit Bansal
Playlist: SQL Practice / SQL Interview Questions


2️⃣ Excel

Advanced Excel is required.

Focus on:

• Formulas
• Pivot Tables
• Power Query Basics
• Data Cleaning
• Data Analysis functions


3️⃣ BI Tools

Choose ONE:

• Power BI
• Tableau

❌ Do NOT learn both at the same time.

If you choose Power BI, learn these deeply:

• Power Query
• DAX
• M Code

📚 Resources

YouTube Channel: Learnit Training
Video: Power BI DAX Full Tutorial for Beginners

YouTube Channel: Enterprise DNA
Playlist: DAX Practice Series

YouTube Channel: Goodly (Chandeep Chhabra)
Playlists: Power Query Tutorials and M Code Tutorials


4️⃣ Python

Focus mainly on:

• NumPy
• Pandas
• Basic visualization libraries (Matplotlib / Seaborn)

You don’t need deep ML knowledge for Data Analyst roles.


⭐ Good-to-Have Skills

These are not mandatory but help in career growth:

• Machine Learning (basic understanding)
• PySpark
• Databricks (becoming popular in data teams)
• Cloud platforms

Cloud options:

• Azure
• GCP


🎓 Certifications (Optional)

Certifications can help but are not required.

Useful ones:

• Microsoft Power BI Certification – PL-300
• Tableau Certification
• Azure Cloud Certification


❌ No other certifications are required.

Save your money.

Focus on skills, projects, and practice.

Credit: Mohan

🧠 SQL Interview Question (Moderate–Tricky & Retention Analysis)
📌

subscriptions(user_id, start_date, end_date)

❓ Ques :

👉 Find users who renewed their subscription immediately after the previous one ended (no gap between subscriptions).

🧩 How Interviewers Expect You to Think

• Sort subscriptions by start_date for each user
• Use a window function to access the previous subscription end date
• Check if the next start_date equals the previous end_date

💡 SQL Solution

WITH sub_cte AS (
SELECT
user_id,
start_date,
end_date,
LAG(end_date) OVER (
PARTITION BY user_id
ORDER BY start_date
) AS prev_end_date
FROM subscriptions
)

SELECT DISTINCT user_id
FROM sub_cte
WHERE start_date = prev_end_date;

🔥 Why This Question Is Powerful

• Tests ability to analyze subscription lifecycle data
• Evaluates knowledge of window functions for sequential comparisons
• Similar logic used in retention and churn analysis

❤️ React if you want more real interview-level SQL questions like this. 🚀

How to Become a Data Analyst from Scratch! 🚀

Whether you're starting fresh or upskilling, here's your roadmap:

➜ Master Excel and SQL - solve SQL problems from leetcode & hackerank
➜ Get the hang of either Power BI or Tableau - do some hands-on projects
➜ learn what the heck ATS is and how to get around it
➜ learn to be ready for any interview question
➜ Build projects for a data portfolio
➜ And you don't need to do it all at once!
➜ Fail and learn to pick yourself up whenever required

Whether it's acing interviews or building an impressive portfolio, give yourself the space to learn, fail, and grow. Good things take time ✅

Like if it helps ❤️

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634

Hope it helps :)

✅ Power BI Interview Questions 🎯📊

1️⃣ What is Power BI?
A Microsoft tool for data visualization, reporting, and business intelligence.

2️⃣ What are the building blocks of Power BI?
• Datasets
• Reports
• Dashboards
• Tiles
• Visualizations

3️⃣ Difference between Power BI Desktop and Power BI Service?
• Desktop: Used to create and design reports
• Service: Cloud-based platform to share and collaborate

4️⃣ What is Power Query?
A data transformation tool for cleaning and shaping data before loading into the model.

5️⃣ What is DAX?
Data Analysis Expressions – a formula language used for calculations in Power BI.

6️⃣ What are measures and calculated columns?
• Measure: Calculated on aggregation (e.g. SUM of sales)
• Calculated Column: Row-level computation (e.g. profit = revenue - cost)

7️⃣ What is a slicer?
A visual filter that allows users to dynamically filter data on a report.

8️⃣ How do you handle data refresh in Power BI?
• Schedule refresh via Power BI Service
• Use gateways for on-prem data sources

9️⃣ What is the difference between direct query and import mode?
• Import: Data is loaded into Power BI
• Direct Query: Queries run directly on the source in real time

🔟 What is the Power BI Gateway?
A bridge between on-premise data sources and Power BI cloud service.

💬 Tap ❤️ for more

Useful websites to practice and enhance your Data Analytics skills
👇👇

1. SQL

https://mode.com/sql-tutorial/introduction-to-sql

https://t.me/sqlspecialist/232?single

2. Python

https://www.learnpython.org/

https://bit.ly/3T7y4ta

https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial

3. R

https://www.datacamp.com/courses/free-introduction-to-r

4. Data Structures

https://leetcode.com/study-plan/data-structure/

https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513

5. Data Visualization

https://www.freecodecamp.org/learn/data-visualization/

https://www.tableau.com/learn/training/20223

https://www.workout-wednesday.com/power-bi-challenges/

6. Excel

https://excel-practice-online.com/

https://www.w3schools.com/EXCEL/index.php

Join @free4unow_backup for more free courses

ENJOY LEARNING 👍👍

🧠 SQL Interview Question (Moderate–Tricky & Top Performer Analysis)
📌

sales(region, salesperson_id, revenue)

❓ Ques :

👉 Find the top 2 highest revenue-generating salespersons in each region.

🧩 How Interviewers Expect You to Think

• Data is grouped by region 🌍
• Need ranking within each group
• Handle ties carefully (RANK / DENSE_RANK)
• Filter top N per group

💡 SQL Solution

SELECT region, salesperson_id, revenue
FROM (
SELECT
region,
salesperson_id,
revenue,
DENSE_RANK() OVER (PARTITION BY region ORDER BY revenue DESC) AS rnk
FROM sales
) t
WHERE rnk <= 2;

🔥 Why This Question Is Powerful

• Tests window functions (RANK / DENSE_RANK) 🧠
• Very common in business reporting & leaderboards 📊
• Checks understanding of partitioning + ordering logic

❤️ React if you want more such real interview-level SQL questions 🚀

🧠 SQL Interview Question (Moderate–Tricky & Duplicate Detection + Latest Record)
📌

employees(emp_id, email, updated_at)

❓ Ques :

👉 Find duplicate emails, but return only the latest record for each duplicate email.

🧩 How Interviewers Expect You to Think

• Identify duplicates using COUNT() 📊
• Use window functions for ranking
• Partition by email
• Order by latest timestamp
• Filter only duplicates + latest row

💡 SQL Solution

SELECT emp_id, email, updated_at
FROM (
SELECT
emp_id,
email,
updated_at,
COUNT(*) OVER (PARTITION BY email) AS cnt,
ROW_NUMBER() OVER (
PARTITION BY email
ORDER BY updated_at DESC
) AS rn
FROM employees
) t
WHERE cnt > 1
AND rn = 1;

🔥 Why This Question Is Powerful

• Tests window functions (COUNT OVER, ROW_NUMBER) 🧠
• Combines deduplication + ranking logic
• Very common in data cleaning scenarios 🧹
• Real-world use case: keeping latest user records

❤️ React if you want more such real interview-level SQL questions 🚀

Key Power BI Functions Every Analyst Should Master

DAX Functions:

1. CALCULATE():

Purpose: Modify context or filter data for calculations.

Example: CALCULATE(SUM(Sales[Amount]), Sales[Region] = "East")



2. SUM():

Purpose: Adds up column values.

Example: SUM(Sales[Amount])



3. AVERAGE():

Purpose: Calculates the mean of column values.

Example: AVERAGE(Sales[Amount])



4. RELATED():

Purpose: Fetch values from a related table.

Example: RELATED(Customers[Name])



5. FILTER():

Purpose: Create a subset of data for calculations.

Example: FILTER(Sales, Sales[Amount] > 100)



6. IF():

Purpose: Apply conditional logic.

Example: IF(Sales[Amount] > 1000, "High", "Low")



7. ALL():

Purpose: Removes filters to calculate totals.

Example: ALL(Sales[Region])



8. DISTINCT():

Purpose: Return unique values in a column.

Example: DISTINCT(Sales[Product])



9. RANKX():

Purpose: Rank values in a column.

Example: RANKX(ALL(Sales[Region]), SUM(Sales[Amount]))



10. FORMAT():

Purpose: Format numbers or dates as text.

Example: FORMAT(TODAY(), "MM/DD/YYYY")

You can refer these Power BI Interview Resources to learn more: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Like this post if you want me to continue this Power BI series 👍♥️

Share with credits: https://t.me/sqlspecialist

Hope it helps :)

✅ How to Grow Fast in Data Analytics 📈💼

1️⃣ Master Core Tools
- Excel: Pivot tables, lookups, charts
- SQL: Joins, aggregations, subqueries
- Power BI / Tableau: Dashboards, filters, visuals
- Python: pandas, matplotlib, seaborn for deeper analysis

2️⃣ Learn Key Concepts
- Descriptive stats: mean, median, variance
- Data cleaning: missing values, outliers
- Visualization best practices
- Business KPIs and metrics (e.g., churn rate, CAC, ROI)

3️⃣ Build Practical Projects
- Sales dashboard in Power BI
- SQL analysis of e-commerce data
- Python analysis of COVID-19 trends
- Excel-based budget tracker

4️⃣ Share Your Work
- Post dashboards on LinkedIn
- Upload projects to GitHub
- Record quick YouTube explainers

5️⃣ Join the Community
- LinkedIn groups, Reddit (r/dataisbeautiful), Kaggle
- Attend webinars, local meetups, analytics bootcamps

6️⃣ Stay Current
- Follow Google Analytics, Microsoft BI, Mode
- Subscribe to newsletters: Data Elixir, Analytics Vidhya
- Learn new tools: Looker, BigQuery, Power Query

🎯 Practice daily. Improve weekly. Share monthly.

💬 Tap ❤️ if this helped you!

🧠 SQL Interview Question (Self Join + Salary Comparison)
📌

employees(emp_id, manager_id, salary)

❓ Ques :

👉 Find employees whose salary is higher than their manager’s salary.

🧩 How Interviewers Expect You to Think

• Understand hierarchical relationships 👥
• Use self join on same table
• Compare values across related rows
• Handle NULL manager cases

💡 SQL Solution

SELECT
e.emp_id,
e.salary AS emp_salary,
m.salary AS manager_salary
FROM employees e
JOIN employees m
ON e.manager_id = m.emp_id
WHERE e.salary > m.salary;

🔥 Why This Question Is Powerful

• Tests self join concept deeply 🧠
• Real-world scenario in org hierarchy analysis
• Checks ability to compare across rows
• Frequently asked in interviews

❤️ React if you want more real interview-level SQL questions 🚀

Data Analyst Interview Preparation Roadmap ✅

Technical skills to revise

- SQL
Write queries from scratch.
Practice joins, group by, subqueries.
Handle duplicates and NULLs.
Window functions basics.

- Excel
Pivot tables without help.
XLOOKUP and IF confidently.
Data cleaning steps.

- Power BI or Tableau
Explain data model.
Write basic DAX.
Explain one dashboard end to end.

- Statistics
Mean vs median.
Standard deviation meaning.
Correlation vs causation.

- Python. If required
Pandas basics.
Groupby and filtering.

Interview question types

- SQL questions
Top N per group.
Running totals.
Duplicate records.
Date based queries.

- Business case questions
Why did sales drop.
Which metric matters most and why.

- Dashboard questions
Explain one KPI.
How users will use this report.

- Project questions
Data source.
Cleaning logic.
Key insight.
Business action.

Resume preparation
- Must have Tools section.
- One strong project.
- Metrics driven points.
Example: Improved reporting time by 30 percent using Power BI.

Mock interviews
- Practice explaining out loud.
- Time your answers.
- Use real datasets.

Daily prep plan
1 SQL problem.
1 dashboard review.
10 interview questions.

- Common mistakes
Memorizing queries.
No project explanation.
Weak business reasoning.

- Final task
- Prepare one project story.
- Prepare one SQL solution on paper.
- Prepare one business metric explanation.

Double Tap ♥️ For More

🧠 SQL Interview Question (Products Frequently Bought Together)
📌

order_items(order_id, product_id)

❓ Ques :

👉 Find pairs of products that are frequently bought together in the same order

👉 Return product_id_1, product_id_2, pair_count

🧩 How Interviewers Expect You to Think

• Self-join on same order 🛒
• Avoid duplicate/reverse pairs
• Count frequency of each pair

💡 SQL Solution

SELECT
o1.product_id AS product_id_1,
o2.product_id AS product_id_2,
COUNT(*) AS pair_count
FROM order_items o1
JOIN order_items o2
ON o1.order_id = o2.order_id
AND o1.product_id < o2.product_id
GROUP BY
o1.product_id,
o2.product_id
ORDER BY pair_count DESC;

🔥 Why This Question Is Powerful

• Classic market basket analysis 🧠
• Tests self-join + combinations logic
• Frequently asked in e-commerce & analytics roles

❤️ React for more SQL interview questions 🚀

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🔥 Top SQL Interview Questions with Answers

🎯 1️⃣ Find 2nd Highest Salary
📊 Table: employees
id | name | salary
1 | Rahul | 50000
2 | Priya | 70000
3 | Amit | 60000
4 | Neha | 70000

❓ Problem Statement: Find the second highest distinct salary from the employees table.

✅ Solution
SELECT MAX(salary) FROM employees WHERE salary < ( SELECT MAX(salary) FROM employees );

🎯 2️⃣ Find Nth Highest Salary
📊 Table: employees
id | name | salary
1 | A | 100
2 | B | 200
3 | C | 300
4 | D | 200

❓ Problem Statement: Write a query to find the 3rd highest salary.

✅ Solution
SELECT salary FROM ( SELECT salary, DENSE_RANK() OVER(ORDER BY salary DESC) r FROM employees ) t WHERE r = 3;

🎯 3️⃣ Find Duplicate Records
📊 Table: employees
id | name
1 | Rahul
2 | Amit
3 | Rahul
4 | Neha

❓ Problem Statement: Find all duplicate names in the employees table.

✅ Solution
SELECT name, COUNT(*) FROM employees GROUP BY name HAVING COUNT(*) > 1;

🎯 4️⃣ Customers with No Orders
📊 Table: customers
customer_id | name
1 | Rahul
2 | Priya
3 | Amit

📊 Table: orders
order_id | customer_id
101 | 1
102 | 2

❓ Problem Statement: Find customers who have not placed any orders.

✅ Solution
SELECT c.name FROM customers c LEFT JOIN orders o ON c.customer_id = o.customer_id WHERE o.customer_id IS NULL;

🎯 5️⃣ Top 3 Salaries per Department
📊 Table: employees
name | department | salary
A | IT | 100
B | IT | 200
C | IT | 150
D | HR | 120
E | HR | 180

❓ Problem Statement: Find the top 3 highest salaries in each department.

✅ Solution
SELECT * FROM ( SELECT name, department, salary, ROW_NUMBER() OVER( PARTITION BY department ORDER BY salary DESC ) r FROM employees ) t WHERE r <= 3;

🎯 6️⃣ Running Total of Sales
📊 Table: sales
date | sales
2024-01-01 | 100
2024-01-02 | 200
2024-01-03 | 300

❓ Problem Statement: Calculate the running total of sales by date.

✅ Solution
SELECT date, sales, SUM(sales) OVER(ORDER BY date) AS running_total FROM sales;

🎯 7️⃣ Employees Above Average Salary
📊 Table: employees
name | salary
A | 100
B | 200
C | 300

❓ Problem Statement: Find employees earning more than the average salary.

✅ Solution
SELECT name, salary FROM employees WHERE salary > ( SELECT AVG(salary) FROM employees );

🎯 8️⃣ Department with Highest Total Salary
📊 Table: employees
name | department | salary
A | IT | 100
B | IT | 200
C | HR | 500

❓ Problem Statement: Find the department with the highest total salary.

✅ Solution
SELECT department, SUM(salary) AS total_salary FROM employees GROUP BY department ORDER BY total_salary DESC LIMIT 1;

🎯 9️⃣ Customers Who Placed Orders
📊 Tables: Same as Q4
❓ Problem Statement: Find customers who have placed at least one order.

✅ Solution
SELECT name FROM customers c WHERE EXISTS ( SELECT 1 FROM orders o WHERE c.customer_id = o.customer_id );

🎯 🔟 Remove Duplicate Records
📊 Table: employees
id | name
1 | Rahul
2 | Rahul
3 | Amit

❓ Problem Statement: Delete duplicate records but keep one unique record.

✅ Solution
DELETE FROM employees WHERE id NOT IN ( SELECT MIN(id) FROM employees GROUP BY name );

🚀 Pro Tip:
👉 In interviews:
First explain logic
Then write query
Then optimize

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