Why data modeling connects related tables in Power BI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

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

Power BI

Model.createRelationship(table1[[1]], table2[ID])

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

ACustomerID

BProductName

CSalesAmount

DDate

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a column that does not exist in both tables.

Using a measure or value column instead of a key column.

The relationship is usually created on a key column like CustomerID that exists in both tables.

2fill in blank

medium

Complete the code to filter related rows using DAX.

Power BI

CALCULATE(SUM(Sales[Amount]), RELATEDTABLE([1]))

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

ACustomers

BProducts

CDates

DRegions

Attempts:

3 left

💡 Hint

Common Mistakes

Using a table unrelated to sales.

Confusing RELATEDTABLE with RELATED function.

The RELATEDTABLE function filters rows in the related Products table.

3fill in blank

hard

Fix the error in the DAX expression to correctly relate tables.

Power BI

SUMX(RELATEDTABLE([1]), [SalesAmount])

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

ASales

BCustomers

CProducts

DOrders

Attempts:

3 left

💡 Hint

Common Mistakes

Using the sales table inside RELATED causes an error.

Choosing unrelated tables that do not connect to the current table.

The RELATEDTABLE function returns a table related to the current row, so Products is the correct table to reference for sales amount aggregation.

4fill in blank

hard

Fill both blanks to create a relationship and use it in a measure.

Power BI

Relationship = CREATE_RELATIONSHIP([1][OrderID], [2][ID])

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

AOrders

BCustomers

CSales

DProducts

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up which table has OrderID and which has ID.

Using unrelated tables like Customers or Products here.

The Orders table's OrderID connects to the Sales table's ID to relate sales to orders.

5fill in blank

hard

Fill all three blanks to write a DAX measure that sums sales filtered by related customer region.

Power BI

TotalSalesRegion = CALCULATE(SUM([1][Amount]), RELATED([2][[3]]))

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

ASales

BCustomers

CRegion

DOrders

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong table for sales amount.

Referencing a column that does not exist in the related table.

The measure sums Sales[Amount] filtered by the related Customers table's Region column.

Practice

1. Why do we connect related tables in Power BI data modeling?

easy

A. To make tables look colorful in the model view

B. To combine related data for accurate analysis and reporting

C. To delete duplicate rows automatically

D. To speed up Power BI software installation

Solution

1. Step 1: Understand the purpose of connecting tables

   Connecting tables links related data so you can analyze combined information easily.

2. Step 2: Recognize the benefit in reports

   Proper connections ensure reports show accurate results by relating data correctly.

3. Final Answer:

   To combine related data for accurate analysis and reporting -> Option B

4. Quick Check:

   Connecting tables = accurate combined data [OK]

Hint: Think: Why join data? To analyze together [OK]

Common Mistakes:

• Confusing visual formatting with data relationships
• Assuming connections delete duplicates
• Believing connections affect software speed

2. Which of the following is the correct way to create a relationship between two tables in Power BI?

easy

A. Use the Format pane to link tables by color

B. Write a DAX formula to join tables in the Report view

C. Drag a key column from one table to the matching column in another table in Model view

D. Copy and paste data from one table into another

Solution

1. Step 1: Identify how relationships are created in Power BI

   Relationships are made by dragging matching key columns between tables in Model view.

2. Step 2: Eliminate incorrect methods

   DAX formulas do calculations but do not create relationships; formatting and copy-paste do not link tables.

3. Final Answer:

   Drag a key column from one table to the matching column in another table in Model view -> Option C

4. Quick Check:

   Drag key columns = create relationship [OK]

Hint: Drag keys in Model view to connect tables [OK]

Common Mistakes:

• Trying to create relationships using DAX formulas
• Confusing formatting with data linking
• Copy-pasting data instead of linking

3. Given two tables: Sales with columns OrderID, ProductID, and Amount, and Products with columns ProductID and ProductName. If you create a relationship on ProductID between these tables, what will happen when you create a report showing ProductName and total Amount?

medium

A. The report will show total sales amount grouped by product name

B. The report will show blank values for product names

C. The report will show total amount without grouping

D. The report will cause an error and not display

Solution

1. Step 1: Understand the relationship effect

   Connecting on ProductID links sales to product details, enabling grouping by product name.

2. Step 2: Predict report output

   Using ProductName and total Amount will group sales amounts by each product correctly.

3. Final Answer:

   The report will show total sales amount grouped by product name -> Option A

4. Quick Check:

   Relationship on ProductID = grouped sales by product [OK]

Hint: Relationship keys enable grouping related data [OK]

Common Mistakes:

• Expecting blank product names without relationship
• Thinking totals show without grouping
• Assuming report errors without relationship

4. You created a relationship between two tables on the CustomerID column, but your report shows incorrect totals. What is the most likely issue?

medium

A. The CustomerID columns have mismatched data types or missing values

B. You forgot to format the report colors

C. The tables are too large to connect

D. Power BI does not support relationships on ID columns

Solution

1. Step 1: Check relationship keys

   For relationships to work, key columns must have matching data types and no missing values.

2. Step 2: Identify cause of incorrect totals

   If data types differ or values are missing, relationships break, causing wrong report results.

3. Final Answer:

   The CustomerID columns have mismatched data types or missing values -> Option A

4. Quick Check:

   Matching keys needed for correct totals [OK]

Hint: Check key columns data type and completeness [OK]

Common Mistakes:

• Blaming report colors for data errors
• Assuming table size prevents relationships
• Thinking Power BI disallows ID relationships

5. You have three tables: Orders, Customers, and Regions. Orders links to Customers by CustomerID, and Customers links to Regions by RegionID. You want to create a report showing total sales by region. What is the best data modeling approach?

hard

A. Create a relationship only between Orders and Regions directly

B. Join all tables into one big table before importing to Power BI

C. Use DAX to manually merge tables in the report

D. Create relationships from Orders to Customers on CustomerID, and Customers to Regions on RegionID, then use RegionName in the report

Solution

1. Step 1: Understand table connections

   Orders relate to Customers by CustomerID; Customers relate to Regions by RegionID, forming a chain.

2. Step 2: Model relationships properly

   Create relationships along this chain so data flows correctly from Orders to Regions.

3. Step 3: Use RegionName in report

   With relationships, you can group total sales by RegionName accurately.

4. Final Answer:

   Create relationships from Orders to Customers on CustomerID, and Customers to Regions on RegionID, then use RegionName in the report -> Option D

5. Quick Check:

   Chain relationships enable accurate regional sales [OK]

Hint: Connect tables stepwise using keys for multi-table reports [OK]

Common Mistakes:

• Trying to flatten tables before import unnecessarily
• Skipping intermediate relationships
• Using DAX merges instead of model relationships