🧠 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

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✅ 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
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A bridge between on-premise data sources and Power BI cloud service.

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

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https://www.learnpython.org/

https://bit.ly/3T7y4ta

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

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

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

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

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

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

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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.

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

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