This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Prepare the data (25–30%)
   --> Transform and load the data
      --> Create Fact Tables and Dimension Tables

Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

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Practice Questions

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Question 1

A table contains SalesAmount, Quantity, ProductName, ProductCategory, CustomerName, and OrderDate. Which columns should remain in the fact table?

A. ProductName, ProductCategory
B. CustomerName, OrderDate
C. SalesAmount, Quantity
D. ProductName, CustomerName

Correct Answer: C

Explanation:
Fact tables store numeric measures that are aggregated, such as SalesAmount and Quantity. Descriptive attributes belong in dimension tables.

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Question 2

What is the primary purpose of a dimension table?

A. Store transaction-level data
B. Provide descriptive context for facts
C. Improve visual formatting
D. Store calculated measures

Correct Answer: B

Explanation:
Dimension tables provide descriptive attributes (such as names, categories, and dates) that are used to filter and group fact data.

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Question 3

Which relationship type is most appropriate between a dimension table and a fact table?

A. Many-to-many
B. One-to-one
C. One-to-many
D. Bi-directional

Correct Answer: C

Explanation:
A dimension table contains unique keys, while the fact table contains repeated foreign keys, creating a one-to-many relationship.

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Question 4

You create a Product dimension table but forget to remove duplicate ProductID values. What issue is most likely?

A. Measures will return blank values
B. Relationships cannot be created correctly
C. Visuals will fail to render
D. DAX functions will not work

Correct Answer: B

Explanation:
Dimension tables must have unique key values. Duplicates prevent proper one-to-many relationships.

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Question 5

Which schema design is recommended by Microsoft for Power BI models?

A. Snowflake schema
B. Flat table schema
C. Galaxy schema
D. Star schema

Correct Answer: D

Explanation:
The star schema is recommended for performance, simplicity, and easier DAX calculations in Power BI.

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Question 6

Where should fact and dimension tables typically be created?

A. In DAX measures
B. In Power Query during data preparation
C. In visuals after loading data
D. In the Power BI Service

Correct Answer: B

Explanation:
Fact and dimension tables should be shaped in Power Query before loading into the data model.

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Question 7

A model uses the same Date table for Order Date and Ship Date. What type of dimension is this?

A. Slowly changing dimension
B. Degenerate dimension
C. Role-playing dimension
D. Bridge table

Correct Answer: C

Explanation:
A role-playing dimension is used multiple times in different roles, such as Order Date and Ship Date.

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Question 8

Which is a valid reason not to split a dataset into fact and dimension tables?

A. The dataset is extremely small and static
B. The dataset contains numeric measures
C. The model requires relationships
D. The data will be refreshed regularly

Correct Answer: A

Explanation:
For very small or simple datasets, splitting into facts and dimensions may add unnecessary complexity.

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Question 9

What is the primary performance benefit of separating fact and dimension tables?

A. Faster visual rendering due to fewer measures
B. Reduced memory usage and simpler filter paths
C. Automatic indexing of columns
D. Improved DirectQuery support

Correct Answer: B

Explanation:
Star schemas reduce duplication of descriptive data and create efficient filter paths, improving performance.

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Question 10

Which modeling mistake often leads to the unnecessary use of bi-directional relationships?

A. Using too many measures
B. Poor star schema design
C. Too many dimension tables
D. Using calculated columns

Correct Answer: B

Explanation:
Bi-directional relationships are often used to compensate for poor model design. A clean star schema usually requires only single-direction filtering.

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Final Exam Tips for This Topic

• Measures → Fact tables
• Descriptive attributes → Dimension tables
• Use Power Query to shape tables before modeling
• Ensure unique keys in dimension tables
• Prefer star schema over flat or snowflake models
• Know when not to over-model

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