Practical Questions

 Practcal Exam Questions of Data Visualization using Power BI

1. Students Data, display subject and marks in Pie chart.

Student Name	Subject	Marks
Aditi	Math	85
Aditi	Science	78
Rahul	Math	90
Rahul	Science	88
Sneha	Math	70
Sneha	Science	75

2. Sales Data, Display Category and sales amount in Funnel chart.

Product	Category	Sales Amount
Pen	Stationery	100
Pencil	Stationery	80
Notebook	Stationery	200
Chips	Snacks	150
Biscuits	Snacks	120
Juice	Drinks	180

3. Import CSV data display sales and region in Stacked Column Chart.

Product,Region,Sales,Quantity,Date

Laptop,West,50000,2,01-01-2024

Mobile,East,20000,3,02-01-2024

Tablet,South,15000,1,03-01-2024

Laptop,North,45000,2,04-01-2024

Mobile,West,25000,4,05-01-2024

4. Import CSV data, 

    a. Display name and marks in Stacked column chart.

    b. Display Subject and Marks in Pie Chart

    c. Total Marks of all student in Card visual.

Name,Subject,Marks,City

Rahul,Maths,78,Pune

Sneha,Science,85,Mumbai

Amit,Maths,65,Nagpur

Priya,Science,90,Pune

Rohit,Maths,88,Mumbai

5. Removing Duplicates & Handling Missing Values

Name,Marks,City

Rahul,80,Pune

Sneha,90,Mumbai

Amit,,Nagpur

Rahul,80,Pune

Priya,85,

Rohit,70,Mumbai

•  Display Clustered Bar Chart with name and marks.
•  Display Pie Chart with city, name

6. Import data and handle missing values and duplicate duplicate entries.

Product	Quantity	Category
Laptop	15	Electronics
Mobile	25	Electronics
Chair	(Missing)	Furniture
Laptop	15	Electronics
Table	12	(Missing)
Sofa	8	Furniture

•  Display Stacked column chart with Product vs Quantity
•  Display Funnel Chart with Products by Category

7. Import data and visualize the data.

StudentID	Full Name	DOB	Percentage	City-State
101	Rahul Sharma	15-08-2003	82.5	Pune-MH
102	Sneha Patil	22-01-2004	91	Mumbai-MH
103	Amit Verma	10-12-2003	78	Nagpur-MH

Perform the following operations using Power Query Editor:

1. Change StudentID data type to Whole Number.
2. Change DOB data type to Date.
3. Rename Percentage column to Marks.
4. Split City-State into City and State using the "-" delimiter. 

• Display Full Name and Marks in Clustered Bar Chart.

8. Customer Data

Customer ID	Customer Name	Age	Mobile No	Is Premium
C101	Rahul Sharma	22	9876543210	TRUE
C102	Sneha Patil	21	9123456789	FALSE
C103	Amit Verma	23	9988776655	TRUE

Perform the following operations using Power Query Editor:

1. Change Age data type to Whole Number.
2. Change Mobile No data type to Text.
3. Change Is Premium data type to True/False.
4. Rename Customer Name to Name.
5. Rename Is Premium to Premium Customer.
6. Display Name and Age in a Clustered Bar Chart.

9. Create Custom Columns and Transform Data using Power Query Functions.

a) Example 1: Monthly Expenses

Month	Food	Transport	Rent
January	6000	2000	12000
February	5800	2200	12000
March	6200	2100	12000
April	6500	2300	12000

Perform the following operations using Power Query Editor:

1. Create a custom column Total Expense = Food + Transport + Rent.
2. Rename Food to Food Expense.
3. Display Month and Total Expense using a Line Chart.

b) Example : Library Books

Book	Copies	Price
Python	25	450
Java	18	500
SQL	20	400
C++	15	350

Perform the following operations using Power Query Editor:

1. Create a custom column Inventory Value = Copies × Price.
2. Rename Copies to Available Copies.
3. Display Book and Inventory Value using a Donut Chart.

10. Design basic visualizations using Bar, Column, Line, and Pie Charts.

a) Example 1: Monthly Sales Report

Month	Sales Amount
January	45000
February	52000
March	48000
April	60000
May	55000
June	68000

Perform the following tasks:

1. Import the data into Power BI.
2. Change Sales Amount data type to Whole Number.
3. Rename Sales Amount to Monthly Sales.
4. Design a Line Chart to show the monthly sales trend.
5. Design a Clustered Column Chart to compare monthly sales.

Visuals to Create:

• Line Chart
• Clustered Column Chart

b) Example 2: Product Sales Distribution

Product	Units Sold
Laptop	120
Mobile	180
Tablet	90
Printer	60
Monitor	75

Perform the following tasks:

1. Import the data into Power BI.
2. Change Units Sold data type to Whole Number.
3. Rename Units Sold to Total Units Sold.
4. Design a Clustered Bar Chart to compare product sales.
5. Design a Pie Chart to show the percentage contribution of each product to total sales.

Visuals to Create:

• Clustered Bar Chart
• Pie Chart

11. Format and customize visualizations using colours, labels, and themes.

a) Example 1: Sales Report

Question

Create a Clustered Column Chart to display the sales of different products. Format the chart by changing colors, adding data labels, and applying a theme.

Product	Sales
Laptop	85000
Mobile	65000
Tablet	40000
Printer	30000
Monitor	45000

b) Example 2: Student Marks Report

Question

Create a Pie Chart to display student marks. Customize the chart using colors, category labels, percentage labels, and a theme.

Student	Marks
Rahul	85
Sneha	92
Amit	78
Priya	88
Neha	67

12. Create relationships between multiple tables in the data model and represent data in visual forms.

a) Example 1: Sales and Customer Tables

Table 1: Sales

SaleID	CustomerID	Product	Amount
1	C101	Laptop	50000
2	C102	Mouse	800
3	C101	Keyboard	1500

Table 2: Customers

CustomerID	CustomerName	City
C101	Rahul	Pune
C102	Priya	Mumbai

b) Example 2: Orders and Products Tables

Table 1: Orders

OrderID	ProductID	Quantity
101	P1	2
102	P2	1
103	P1	3

Table 2: Products

ProductID	ProductName	Price
P1	Laptop	50000
P2	Printer	12000

13. Merge Two Related Tables Using a Common Key in Power BI and display useful data in clustered column chart.

Customers Table

CustomerID	CustomerName	City
C101	Amit Patil	Pune
C102	Sneha Sharma	Mumbai
C103	Rahul Joshi	Nanded
C104	Priya More	Pune

Sales Table

OrderID	CustomerID	Product	Sales
O001	C101	Laptop	100000
O002	C102	Mouse	2500
O003	C101	Keyboard	3600
O004	C103	Laptop	50000

14. Calculate Total, Average and Count of the price and quantity.

Sales Table

OrderID	CustomerID	Product	Sales	Quantity
O001	C101	Laptop	100000	1
O002	C102	Mouse	2500	2
O003	C101	Keyboard	3600	3

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Important Questions

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Practical 1 :Import Excel and CSV Data

Q1. Import data from Excel

• Create a Sales.xlsx file containing columns: OrderID, OrderDate, Product, Category, Quantity, Price, Customer.
• Import the Excel file into Power BI Desktop.
• Load the required worksheet.
• Display Category, Quantity, Price in pie chart.

Q2. Import data from CSV

• Import a Students.csv file containing: StudentID, StudentName, Gender, Course, Marks, City.
• Load the data into Power BI.
• Verify the number of rows and columns.
• Save the Power BI file as StudentAnalysis.pbix.

Q3. Import two datasets

• Import Sales.xlsx and Customers.csv into Power BI.

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Practical 2 : Clean Imported Dataset

Q4. Remove unnecessary columns and rows
Given a sales dataset:
OrderID, Date, Customer, Product, Category, Quantity, Price, Salesperson, Remarks

Perform the following:

• Remove the Remarks column.
• Remove duplicate records.
• Load the cleaned data into Power BI.

Q5. Handle missing values
Add a customer dataset containing missing values in City, Phone, and Email:

• Identify missing values.
• Replace missing City values with "Unknown".
• Remove duplicate customers.

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Practical 3 : Data Types, Rename and Split Columns

Q6. Change data types
Add a sales dataset:

• Change OrderDate to Date.
• Change Quantity to Whole Number.
• Change Price and Sales to Decimal Number.

Q7. Rename columns
Rename:

• Cust_ID → Customer ID
• Prod_Name → Product Name
• Qty → Quantity
• Ord_Date → Order Date

Q8. Split a column
Given a FullName column containing:

Rahul Patil
Sneha Sharma

• Split FullName into First Name and Last Name.
• Use Split Column by Delimiter.
• Verify the result.

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Practical 4 : Custom Columns and Transformations

Q 9. Create an Amount column

Given:

Product	Quantity	Unit Price
Laptop	2	50000
Mouse	5	500
Keyboard	3	1200

Create a custom column:

Amount = Quantity × Unit Price

Display the calculated amount in funnel chart.

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Practical 5 : Basic Visualizations

Sales Dataset

Date	Product	Product Category	Sales
05-Jan-2026	Laptop	Electronics	55000
12-Jan-2026	Mobile	Electronics	30000
20-Jan-2026	Chair	Furniture	12000
28-Jan-2026	Table	Furniture	18000
05-Feb-2026	Laptop	Electronics	60000
14-Feb-2026	Mobile	Electronics	35000

Q10. Product-wise sales

Using the Sales dataset:

• Create a Column Chart showing total sales by Product.
• Display data labels.

Q11. Sales distribution
Create a Pie Chart showing the percentage contribution of each Product Category to total sales.

Q12. Create Dashboard with Multiple visualizations
Using the Sales dataset, create:

1. Bar chart : Sales by Product
2. Column chart : Sales by Category
3. Line chart : Monthly Sales
4. Pie chart : Sales by Category