Course quiz: Modeling data in Power BI :Data Modeling in Power BI (Microsoft Power BI Data Analyst Professional Certificate) Answers 2025

Question 1

Which statements correctly describe the primary purposes of data modeling?
(Select all that apply)

❌ Data modeling is primarily done to ensure faster data processing speeds.
✅ Data modeling offers a structured representation of data for easier interpretation and business use.
✅ Data modeling aids in designing clear data organizations for effective visualization.
❌ The main goal of data modeling is to develop predictive algorithms.

Explanation:
Data modeling focuses on structure, clarity, and usability, not prediction or raw speed alone.

Question 2

What is a significant disadvantage of a Flat schema in Power BI?

✅ It leads to data redundancy and inconsistency.
❌ It is complex to implement.
❌ It lacks data visualization capabilities.

Explanation:
Flat schemas duplicate data, which increases inconsistency and maintenance effort.

Question 3

Which statements accurately describe a Star schema?
(Select all that apply)

✅ Dimension tables revolve around a central fact table.
❌ Multiple fact tables are interconnected through one dimension table.
❌ It contains only a single table.
✅ A fact table connects to several dimension tables.

Explanation:
A Star schema has one fact table surrounded by dimension tables.

Question 4

True or False: Schema selection mainly affects data collection speed.

❌ True
✅ False

Explanation:
Schema selection mainly affects query performance, usability, and clarity, not data collection speed.

Question 5

True or False: Fact tables are designed to capture descriptive attributes.

❌ True
✅ False

Explanation:
Fact tables store numeric measures; descriptive attributes belong to dimension tables.

Question 6

True or False: Snowflake schema normalization improves visual appearance.

❌ True
✅ False

Explanation:
Normalization reduces redundancy, not for visuals, but for storage and structure.

Question 7

What type of data do dimension tables typically contain?

❌ Centralized numeric data
✅ Descriptive attributes related to fact data
❌ Smaller granular tables

Explanation:
Dimension tables describe who, what, where, when.

Question 8

What does cardinality primarily refer to?

❌ Relationship type
❌ Dataset size
❌ Query speed
✅ Number of distinct values in a column

Explanation:
High cardinality = many unique values.

Question 9

Which relationship maps one record in Table A to one in Table B and vice versa?

❌ Many-to-many
✅ One-to-one
❌ One-to-many

Explanation:
One-to-one means exact pairing.

Question 10

Why increase granularity from monthly to daily analysis?

❌ Reduce dataset size
✅ Achieve more detailed insights
❌ Simplify analysis

Explanation:
Higher granularity provides deeper, more precise insights.

Question 11

What is a challenge of high granularity in a Star schema?

❌ Complex dimensions
❌ More schemas
✅ Reduced query performance due to large fact tables

Explanation:
More rows = heavier queries.

Question 12

Normalization in a Snowflake schema helps reduce:

✅ Storage requirements
❌ Hierarchy complexity
❌ Validation needs

Explanation:
Normalization removes duplicate data.

Question 13

Which statements describe the use of DAX?
(Select all that apply)

❌ Improves visual aesthetics
✅ Creates custom calculations
✅ Enables interactive data computation
❌ Establishes relationships

Explanation:
DAX is for calculations and logic, not visuals or relationships.

Question 14

When row and filter contexts exist, which is evaluated first?

❌ Row context
✅ Filter context
❌ Both equally

Explanation:
Filter context defines which data is visible.

Question 15

What is the primary function of filter context?

❌ Evaluate relationships
❌ Evaluate current row
✅ Define which rows are included in calculations

Explanation:
Filter context controls scope of calculation.

Question 16

How are variables defined in DAX?

❌ Square brackets
✅ VAR keyword
❌ FUNCTION keyword

Explanation:
DAX variables start with VAR.

Question 17

What does this DAX do?

Cloned_table = ALL(Original_table)

❌ Merge tables
✅ Create a cloned, unfiltered version
❌ Create hierarchy

Explanation:
ALL() removes filters and returns full table.

Question 18

Why are measures beneficial in Power BI?

❌ Work without data
✅ Dynamically update with filters
❌ Used for cleansing

Explanation:
Measures are dynamic and context-aware.

Question 19

How do measures enhance visualization?

❌ Refine interpretation
❌ Create tables
✅ Provide foundation for complex visuals

Explanation:
Measures drive charts, KPIs, and analytics.

Question 20

Which measure type fits ratios or percentages?

✅ Non-Additive Measures
❌ Additive Measures
❌ Semi-Additive Measures

Explanation:
Ratios cannot be summed, hence non-additive.

Question 21

When is LASTDATE especially useful?

❌ Earliest date
❌ Count dates
✅ Semi-additive calculations needing last date

Explanation:
LASTDATE works well with ending balances.

Question 22

When should MEDIAN be used instead of AVERAGE?

✅ Skewed data with outliers
❌ Summation
❌ Counting rows

Explanation:
Median is resistant to outliers.

Question 23

Where is a Quick Measure created by default?

✅ Selected table
❌ Quick Insights
❌ New table

Explanation:
Quick measures are created in the currently selected table.

Question 24

Which DAX correctly uses CROSSFILTER to count distinct products?

❌ SUM(ProductKey)
✅ DISTINCTCOUNT with CROSSFILTER
❌ COUNTROWS only

Correct Answer:

Question 25

What does metadata include?

❌ Load times
❌ Measure formulas only
✅ Table names, columns, relationships, data types

Explanation:
Metadata describes structure, not performance.

Question 26

Why change a column’s data type?
(Select all that apply)

❌ Auto-update data
✅ Better visualization alignment
✅ Memory efficiency
✅ Consistent data model

Question 27

How to reference a column without table name?

✅ [ColumnName] (same table)
❌ ColumnName only
❌ Column + table in quotes

Question 28

Benefits of performance optimization?
(Select all that apply)

❌ Less data cleaning
✅ Timely decisions
✅ Faster queries
✅ Better user experience

Question 29

Which techniques optimize performance?
(Select all that apply)

❌ Change fonts
✅ Filter unnecessary data
❌ Add borders
❌ Sorting only for readability

Question 30

What best describes cardinality?

✅ Number of distinct values
❌ Relationship type
❌ Load sequence

🧾 Summary Table