🧠 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

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🧠 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

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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

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Hope it helps :)

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- Python: pandas, matplotlib, seaborn for deeper analysis

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🧠 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

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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.

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🧠 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

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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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