Data model best practices in Power BI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a relationship between two tables in Power BI.

Power BI

Model = CREATE_RELATIONSHIP('Sales'[CustomerID], [1])

Drag options to blanks, or click blank then click option'

A'Customers'[OrderID]

B'Products'[ProductID]

C'Sales'[ProductID]

D'Customers'[CustomerID]

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing columns from unrelated tables.

Using columns that don't match in data type.

To create a relationship, you connect the key columns from related tables. Here, 'Sales'[CustomerID] relates to 'Customers'[CustomerID].

2fill in blank

medium

Complete the DAX formula to calculate total sales amount.

Power BI

TotalSales = SUM([1])

Drag options to blanks, or click blank then click option'

A'Sales'[SalesAmount]

B'Customers'[CustomerID]

C'Sales'[Quantity]

D'Products'[Price]

Attempts:

3 left

💡 Hint

Common Mistakes

Summing quantity instead of sales amount.

Using columns from unrelated tables.

The SUM function should be applied to the 'SalesAmount' column to get total sales.

3fill in blank

hard

Fix the error in the DAX formula to calculate average sales per customer.

Power BI

AverageSales = DIVIDE(SUM('Sales'[SalesAmount]), [1])

Drag options to blanks, or click blank then click option'

ACOUNTROWS('Products')

BCOUNT('Customers'[CustomerID])

CDISTINCTCOUNT('Sales'[CustomerID])

DSUM('Sales'[Quantity])

Attempts:

3 left

💡 Hint

Common Mistakes

Using COUNTROWS on unrelated tables.

Counting total quantity instead of customers.

To find average sales per customer, divide total sales by the number of unique customers using DISTINCTCOUNT.

4fill in blank

hard

Fill both blanks to create a calculated column that flags high sales orders.

Power BI

HighSalesFlag = IF('Sales'[SalesAmount] [1] 1000, [2], "No")

Drag options to blanks, or click blank then click option'

A>

B"Yes"

C<

D"High"

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operator.

Returning a number instead of text.

The IF statement checks if SalesAmount is greater than 1000, then returns "Yes", else "No".

5fill in blank

hard

Fill all three blanks to create a measure that calculates sales growth percentage.

Power BI

SalesGrowth = DIVIDE(SUM('Sales'[SalesAmount]) - CALCULATE(SUM('Sales'[SalesAmount]), [1]), CALCULATE(SUM('Sales'[SalesAmount]), [2])) * 100

Drag options to blanks, or click blank then click option'

ASAMEPERIODLASTYEAR('Date'[Date])

CFILTER('Date', 'Date'[Year] = YEAR(TODAY()) - 1)

DFILTER('Date', 'Date'[Year] = YEAR(TODAY()))

Attempts:

3 left

💡 Hint

Common Mistakes

Using current year instead of last year for comparison.

Not subtracting last year's sales correctly.

This formula calculates the difference between current sales and last year's sales, then divides by last year's sales to get growth percentage. SAMEPERIODLASTYEAR and FILTER functions help select last year's dates.

Practice

1. What is the main reason to use a star schema in Power BI data modeling?

easy

A. It hides data from users to improve security.

B. It allows storing all data in one big table.

C. It automatically creates relationships without keys.

D. It simplifies analysis by separating facts and dimensions clearly.

Solution

1. Step 1: Understand star schema purpose

   A star schema organizes data into fact tables (measures) and dimension tables (descriptions) for clarity.

2. Step 2: Recognize benefits in Power BI

   This separation makes analysis easier and faster by simplifying relationships and queries.

3. Final Answer:

   It simplifies analysis by separating facts and dimensions clearly. -> Option D

4. Quick Check:

   Star schema = clear fact and dimension tables [OK]

Hint: Star schema means clear fact and dimension tables [OK]

Common Mistakes:

• Thinking all data should be in one table
• Believing relationships form without keys
• Confusing star schema with security features

2. Which of the following is the correct way to create a one-to-many relationship in Power BI?

easy

A. Link the primary key in the dimension table to the foreign key in the fact table.

B. Link the foreign key in the dimension table to the primary key in the fact table.

C. Create a many-to-many relationship between two fact tables.

D. Use a calculated column to join unrelated tables.

Solution

1. Step 1: Identify keys in tables

   The dimension table has a primary key; the fact table has a foreign key referencing it.

2. Step 2: Create relationship direction

   One-to-many means one dimension row links to many fact rows, so connect primary key (dimension) to foreign key (fact).

3. Final Answer:

   Link the primary key in the dimension table to the foreign key in the fact table. -> Option A

4. Quick Check:

   One-to-many = primary key to foreign key [OK]

Hint: Primary key (dimension) links to foreign key (fact) [OK]

Common Mistakes:

• Reversing keys in relationship
• Using many-to-many where one-to-many is needed
• Trying to join unrelated tables with calculated columns

3. Given a data model with a Sales fact table and a Product dimension table linked by ProductID, what happens if you add a relationship from Sales[ProductID] to Product[ProductID] but the Product table has duplicate ProductIDs?

medium

A. Power BI will create a one-to-many relationship and filter correctly.

B. Power BI will show an error and not allow the relationship.

C. Power BI will create a many-to-many relationship automatically.

D. Power BI will ignore duplicates and create the relationship anyway.

Solution

1. Step 1: Understand relationship requirements

   One-to-many relationships require the 'one' side (Product) to have unique keys.

2. Step 2: Check effect of duplicates

   Duplicates in Product[ProductID] break uniqueness, so Power BI blocks the relationship creation.

3. Final Answer:

   Power BI will show an error and not allow the relationship. -> Option B

4. Quick Check:

   Unique keys required on 'one' side [OK]

Hint: One side keys must be unique to create relationship [OK]

Common Mistakes:

• Assuming duplicates are ignored
• Expecting automatic many-to-many creation
• Thinking relationship will still filter correctly

4. You created a relationship between Customer[CustomerID] and Sales[CustomerID], but your report shows incorrect totals. Which of the following is the most likely cause?

medium

A. The Sales table is missing CustomerID values.

B. CustomerID has duplicates in the Customer table.

C. The relationship is inactive and needs to be activated.

D. The relationship is many-to-many instead of one-to-many.

Solution

1. Step 1: Check relationship status

   Inactive relationships do not filter data automatically, causing wrong totals.

2. Step 2: Confirm activation needed

   Activating the relationship or using DAX USERELATIONSHIP fixes filtering issues.

3. Final Answer:

   The relationship is inactive and needs to be activated. -> Option C

4. Quick Check:

   Inactive relationship causes wrong totals [OK]

Hint: Inactive relationships block filtering; activate them [OK]

Common Mistakes:

• Ignoring inactive relationship status
• Assuming duplicates always cause wrong totals
• Thinking missing values break relationships

5. You have a complex data model with multiple fact tables and dimension tables. To improve performance and clarity, which best practice should you apply?

hard

A. Use a star schema with clear one-to-many relationships and avoid circular dependencies.

B. Combine all tables into one large table to reduce relationships.

C. Create many-to-many relationships between all tables for flexibility.

D. Use calculated columns to join tables instead of relationships.

Solution

1. Step 1: Identify best modeling approach

   Star schema organizes data clearly with fact and dimension tables linked by one-to-many relationships.

2. Step 2: Avoid common pitfalls

   Combining tables or many-to-many relationships can cause confusion and slow performance; calculated columns for joins are inefficient.

3. Final Answer:

   Use a star schema with clear one-to-many relationships and avoid circular dependencies. -> Option A

4. Quick Check:

   Star schema + one-to-many + no circular dependencies [OK]

Hint: Star schema and one-to-many improve clarity and speed [OK]

Common Mistakes:

• Merging all tables into one
• Using many-to-many everywhere
• Relying on calculated columns for joins