This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Model the data (25–30%)
   --> Create model calculations by using DAX
      --> Use Basic Statistical Functions

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.

Practice Questions

Question 1

Which DAX function returns the arithmetic mean of a numeric column?

A. MEDIAN
B. AVERAGEX
C. AVERAGE
D. MEAN

✅ Correct Answer: C

Explanation:
AVERAGE calculates the arithmetic mean of a numeric column. AVERAGEX is an iterator, and MEAN is not a valid DAX function.

Question 2

You need to count the number of unique customers in a sales table. Which function should you use?

A. COUNT
B. COUNTA
C. COUNTROWS
D. DISTINCTCOUNT

✅ Correct Answer: D

Explanation:
DISTINCTCOUNT counts unique values in a column, which is required when counting unique customers or products.

Question 3

What is the primary difference between COUNT and COUNTA?

A. COUNT works on tables; COUNTA works on columns
B. COUNT counts numeric values; COUNTA counts non-blank values of any type
C. COUNT ignores blanks; COUNTA counts blanks
D. COUNT is slower than COUNTA

✅ Correct Answer: B

Explanation:
COUNT counts only non-blank numeric values, while COUNTA counts all non-blank values regardless of data type.

Question 4

Which function should you use to count the number of rows returned by a table expression?

A. COUNT
B. COUNTA
C. COUNTROWS
D. DISTINCTCOUNT

✅ Correct Answer: C

Explanation:
COUNTROWS counts the number of rows in a table and is commonly used to count records after filters are applied.

Question 5

Which measure correctly calculates the median sales amount?

A.

Median Sales = MEDIANX(Sales, Sales[SalesAmount])

B.

Median Sales = MEDIAN(Sales[SalesAmount])

C.

Median Sales = AVERAGE(Sales[SalesAmount])

D.

Median Sales = SUM(Sales[SalesAmount])

✅ Correct Answer: B

Explanation:
MEDIAN calculates the middle value of a numeric column and is less sensitive to outliers than AVERAGE.

Question 6

Which function measures how spread out values are around the mean for an entire population?

A. VAR.S
B. VAR.P
C. STDEV.S
D. MEDIAN

✅ Correct Answer: B

Explanation:
VAR.P calculates population variance. VAR.S is used for samples.

Question 7

Which DAX function returns the population standard deviation?

A. STDEV.S
B. STDEVX.P
C. STDEV.P
D. VAR.P

✅ Correct Answer: C

Explanation:
STDEV.P calculates the standard deviation for an entire population dataset.

Question 8

You need to calculate the average sales amount only for products in the “Accessories” category. Which measure is correct?

A.

AVERAGE(Sales[SalesAmount], Product[Category] = "Accessories")

B.

CALCULATE(
    AVERAGE(Sales[SalesAmount]),
    Product[Category] = "Accessories"
)

C.

FILTER(
    AVERAGE(Sales[SalesAmount]),
    Product[Category] = "Accessories"
)

D.

AVERAGEX(
    Sales,
    Product[Category] = "Accessories"
)

✅ Correct Answer: B

Explanation:
CALCULATE modifies filter context, allowing the average to be evaluated only for the specified category.

Question 9

When should you prefer MEDIAN over AVERAGE?

A. When data has missing values
B. When performance is critical
C. When data contains outliers
D. When counting rows

✅ Correct Answer: C

Explanation:
The median is less affected by extreme values and provides a better measure of central tendency when outliers exist.

Question 10

Which statement about basic statistical measures is TRUE?

A. They should be created as calculated columns
B. They ignore filter context
C. They dynamically recalculate based on filters
D. They require a date table

✅ Correct Answer: C

Explanation:
Statistical measures are filter-context aware, meaning their results change dynamically based on slicers and filters.

Final PL-300 Exam Tips for Statistical Functions

• Know the differences between COUNT, COUNTA, COUNTROWS, and DISTINCTCOUNT
• Understand when to use MEDIAN vs AVERAGE
• Be comfortable with variance and standard deviation functions
• Expect filter context–based questions using CALCULATE
• Use measures, not calculated columns

Go back to the PL-300 Exam Prep Hub main page

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Model the data (25–30%)
   --> Create model calculations by using DAX
      --> Create semi-additive measures

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.

Practice Questions

1. Identifying a Semi-Additive Measure

Question:
Which of the following metrics is most likely to require a semi-additive measure?

A. Total Sales Amount
B. Quantity Sold
C. Daily Inventory Balance
D. Total Number of Orders

Correct Answer: C

Explanation:
Inventory balance represents a snapshot in time, not an accumulated activity. Summing it across dates produces incorrect totals. Sales, quantity, and orders are fully additive across time.

2. Why SUM Produces Incorrect Results

Question:
A report sums daily account balances over a month and produces inflated totals. What is the root cause?

A. Incorrect relationships
B. Using SUM instead of a time-aware calculation
C. Missing a date hierarchy
D. Incorrect data type

Correct Answer: B

Explanation:
Account balances are semi-additive. Using SUM across time double-counts values. A time-aware calculation such as retrieving the last value is required.

3. Choosing the Correct DAX Pattern

Question:
Which DAX expression correctly returns the ending balance for a selected period?

A.

SUM(FactBalances[Balance])

B.

AVERAGE(FactBalances[Balance])

C.

CALCULATE(
    SUM(FactBalances[Balance]),
    LASTDATE('Date'[Date])
)

D.

COUNT(FactBalances[Balance])

Correct Answer: C

Explanation:
CALCULATE combined with LASTDATE ensures that only the final date in the current filter context is evaluated, which is the correct behavior for ending balances.

4. Handling Missing Dates

Question:
Some dates have no balance records. Which function ensures the last available balance is returned?

A. LASTDATE
B. FIRSTDATE
C. LASTNONBLANK
D. MAX

Correct Answer: C

Explanation:
LASTNONBLANK finds the most recent date with data, making it ideal when fact data is not recorded every day.

5. Average Over Time Scenario

Question:
You need to report the average daily inventory level for a month. Which approach is correct?

A. SUM inventory values
B. Use AVERAGE on the inventory column
C. Use AVERAGEX over the Date table
D. Use COUNT of inventory rows

Correct Answer: C

Explanation:
AVERAGEX iterates over each date, evaluating inventory per day and then averaging the results. This is the correct approach for semi-additive snapshot values.

6. Beginning Balance Requirement

Question:
Which function should be used to calculate a beginning-of-period balance?

A. LASTDATE
B. FIRSTDATE
C. MAX
D. ENDOFMONTH

Correct Answer: B

Explanation:
FIRSTDATE returns the earliest date in the current filter context, making it ideal for beginning balances.

7. Identifying Incorrect Business Logic

Question:
A measure returns correct daily balances but incorrect monthly totals. What is the most likely issue?

A. The Date table is inactive
B. The measure uses a calculated column
C. The measure is additive over time
D. Cross-filter direction is incorrect

Correct Answer: C

Explanation:
Snapshot metrics should not aggregate across time. If monthly totals are incorrect, the measure is likely being summed instead of using a semi-additive pattern.

8. Relationship to Time Intelligence

Question:
Why is a proper Date table critical for semi-additive measures?

A. It improves report performance
B. It enables slicers
C. Time intelligence functions require it
D. It reduces model size

Correct Answer: C

Explanation:
Functions like LASTDATE, FIRSTDATE, and ENDOFMONTH rely on a contiguous, marked Date table to evaluate time context correctly.

9. Month-End Balance Calculation

Question:
Which DAX expression correctly returns the month-end balance?

A.

SUM(FactBalances[Balance])

B.

CALCULATE(
    SUM(FactBalances[Balance]),
    ENDOFMONTH('Date'[Date])
)

C.

AVERAGEX('Date', FactBalances[Balance])

D.

COUNT(FactBalances[Balance])

Correct Answer: B

Explanation:
ENDOFMONTH restricts the calculation to the last date of the month, which is required for month-end snapshot metrics.

10. Recognizing Semi-Additive Behavior

Question:
Which statement best describes a semi-additive measure?

A. It aggregates correctly across all dimensions
B. It should always be stored as a calculated column
C. It aggregates across some dimensions but not time
D. It cannot use CALCULATE

Correct Answer: C

Explanation:
Semi-additive measures aggregate normally across dimensions like product or region but require special logic when evaluated across time.

Final Exam Tips

• If a value represents a state, not an activity → think semi-additive
• Avoid SUM across dates for snapshot metrics
• Use CALCULATE with time functions
• Expect scenario-based questions, not pure syntax questions

Go back to the PL-300 Exam Prep Hub main page

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Model the data (25–30%)
   --> Create model calculations by using DAX
      --> Create a measure by using Quick Measures

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.

Practice Questions

1. Purpose of Quick Measures

Question:
What is the primary purpose of using a quick measure in Power BI?

A. To create calculated columns without using DAX
B. To generate common DAX measure patterns using guided input
C. To improve model refresh performance
D. To replace the need for a Date table

Correct Answer:
B

Explanation:
Quick measures provide prebuilt DAX templates that guide users through creating common calculations. They generate standard DAX measures that can be edited later. They do not eliminate the need for proper modeling or Date tables.

2. Nature of a Quick Measure

Question:
After creating a quick measure, how is it stored in the data model?

A. As a calculated column
B. As a hidden system calculation
C. As a standard DAX measure
D. As a Power Query transformation

Correct Answer:
C

Explanation:
Quick measures create regular DAX measures that appear in the Fields pane and behave exactly like manually written measures.

3. Editing Generated DAX

Question:
A user creates a quick measure for Year-to-Date Sales. Can the generated DAX be modified?

A. No, quick measures are locked
B. Only in the Model view
C. Yes, like any other DAX measure
D. Only by recreating the quick measure

Correct Answer:
C

Explanation:
Once created, a quick measure is fully editable and behaves like any other DAX measure.

4. Time Intelligence Dependency

Question:
A Year-over-Year quick measure returns incorrect results. What is the most likely cause?

A. The measure was created in the Data view
B. The Date table is not marked as a Date table
C. The visual uses too many filters
D. The measure needs to be a calculated column

Correct Answer:
B

Explanation:
Time intelligence quick measures rely on a proper Date table that is marked as such and related to the fact table. Without it, results may be incorrect.

5. Model Size Impact

Question:
What impact do quick measures have on the size of the data model?

A. They increase model size significantly
B. They add new hidden tables
C. They increase memory usage only at refresh time
D. They do not increase model size

Correct Answer:
D

Explanation:
Measures, including quick measures, are calculated at query time and do not store data, so they do not increase model size.

6. Running Total Behavior

Question:
Which DAX concept is most commonly used in a running total quick measure?

A. Row context
B. RELATED
C. CALCULATE with ALL or FILTER
D. LOOKUPVALUE

Correct Answer:
C

Explanation:
Running total quick measures typically use CALCULATE combined with FILTER and ALL to override filter context and accumulate values over time.

7. Appropriate Use Case

Question:
When is a quick measure most appropriate?

A. When implementing complex business logic
B. When optimizing memory-intensive models
C. When creating a standard calculation pattern quickly
D. When creating relationships between tables

Correct Answer:
C

Explanation:
Quick measures are ideal for standard, well-known calculation patterns such as YTD, running totals, and ratios—not for complex custom logic.

8. Common Mistake

Question:
Which mistake is commonly made when using quick measures?

A. Using them only in visuals
B. Forgetting they generate DAX that depends on filter context
C. Using them only in Power Query
D. Creating too many quick measures

Correct Answer:
B

Explanation:
Quick measures are still subject to filter context. Misunderstanding this can lead to incorrect results, especially in complex visuals.

9. Relationship Dependency

Question:
A quick measure calculates a ratio using two tables but returns blank values. What is the most likely issue?

A. The measure should be a calculated column
B. The tables lack a valid relationship
C. The data types are incorrect
D. The quick measure category is wrong

Correct Answer:
B

Explanation:
Quick measures rely on the existing data model relationships. If relationships are missing or incorrect, the measure may return blanks.

10. Exam Interpretation Question

Question:
On the PL-300 exam, a question references a “guided DAX calculation created through the user interface.” What is this most likely referring to?

A. Calculated tables
B. Power Query steps
C. Quick measures
D. Implicit measures

Correct Answer:
C

Explanation:
“Guided DAX calculation” is a common exam phrasing used to describe quick measures, which guide users through creating DAX measures.

Final Exam Tip

For PL-300:

• Expect scenario-based questions
• Focus on when to use quick measures—not how to click them
• Understand the DAX patterns they generate
• Know their dependencies and limitations

Go back to the PL-300 Exam Prep Hub main page

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Model the data (25–30%)
   --> Create model calculations by using DAX
      --> Create calculated tables or columns

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.

Practice Questions

Question 1

You need to create a column that classifies customers as “High Value” or “Standard” based on their lifetime sales. The value does not need to change based on report filters.

Which approach is MOST appropriate?

A. Create a measure
B. Create a calculated column
C. Create a calculated table
D. Use a visual-level filter

Correct Answer: B

Explanation:
The classification is row-level and static, making a calculated column the correct choice. Measures are dynamic and filter-dependent, which is unnecessary here.

Question 2

A calculation must change dynamically when slicers are applied. Which option should you use?

A. Calculated column
B. Calculated table
C. Measure
D. Power Query custom column

Correct Answer: C

Explanation:
Only measures respond to filter context. Calculated columns and tables are evaluated at refresh and do not change with slicers.

Question 3

You create a calculated column that references a measure. What happens?

A. The column updates dynamically
B. The column returns blank
C. Power BI throws an error
D. The measure is converted to row context

Correct Answer: C

Explanation:
Measures cannot be referenced directly inside calculated columns because measures require filter context, which is not available during column evaluation.

Question 4

Which scenario is BEST suited for a calculated table?

A. Creating a sales KPI
B. Creating a Date dimension
C. Calculating profit margin
D. Filtering a visual dynamically

Correct Answer: B

Explanation:
Calculated tables are commonly used to create Date tables, helper tables, and bridge tables. KPIs and margins should be measures.

Question 5

Which statement about calculated columns is TRUE?

A. They are evaluated at query time
B. They respond to slicers dynamically
C. They increase model size
D. They are not stored in memory

Correct Answer: C

Explanation:
Calculated columns are stored in the model, increasing memory usage. Measures are calculated at query time and not stored.

Question 6

You need a table that aggregates sales by product and category to support a relationship. What should you create?

A. Measure
B. Calculated column
C. Calculated table
D. Visual aggregation

Correct Answer: C

Explanation:
Aggregated datasets that need to participate in relationships should be created as calculated tables.

Question 7

Which DAX function is MOST commonly used when creating calculated tables?

A. CALCULATE
B. SUM
C. SUMMARIZE
D. DIVIDE

Correct Answer: C

Explanation:
SUMMARIZE is commonly used to group and aggregate data when creating calculated tables.

Question 8

Why should calculated columns be avoided for large datasets when possible?

A. They cannot be indexed
B. They slow down visuals
C. They increase memory consumption
D. They do not support relationships

Correct Answer: C

Explanation:
Calculated columns are stored in memory. Overusing them can significantly increase model size and degrade performance.

Question 9

Which task should generally be performed in Power Query instead of using a calculated column?

A. Creating a slicer field
B. Creating a sort-by column
C. Cleaning and transforming raw data
D. Creating relationship keys

Correct Answer: C

Explanation:
Power Query is optimized for data cleansing and transformation and should be preferred over calculated columns whenever possible.

Question 10

A calculated table is created using DAX. When is it evaluated?

A. Every time a slicer changes
B. Every time a visual loads
C. During data refresh
D. At report open

Correct Answer: C

Explanation:
Calculated tables (like calculated columns) are evaluated only during data refresh, not during report interaction.

Exam Takeaways

• ✔ Calculated columns → row-level, static logic
• ✔ Calculated tables → model support and structure
• ✔ Measures → dynamic, filter-aware calculations
• ❌ Avoid calculated columns for aggregations
• ❌ Avoid calculated tables for data cleaning

Go back to the PL-300 Exam Prep Hub main page

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Model the data (25–30%)
   --> Create model calculations by using DAX
      --> Create calculation groups

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.

Practice Questions

Question 1

What is the primary purpose of a calculation group?

A. To replace all measures in a model
B. To apply reusable calculation logic to multiple measures
C. To improve data refresh performance
D. To create row-level security rules

Correct Answer: B

Explanation:
Calculation groups allow a single calculation (such as YTD or YoY) to be reused across multiple measures using SELECTEDMEASURE().

Question 2

Which function is REQUIRED when defining a calculation item?

A. CALCULATE
B. VALUES
C. SELECTEDVALUE
D. SELECTEDMEASURE

Correct Answer: D

Explanation:
SELECTEDMEASURE() references the measure currently in context, allowing the calculation group to work dynamically across measures.

Question 3

Where are calculation groups created?

A. Power BI Desktop
B. Power Query Editor
C. Tabular Editor
D. DAX Studio

Correct Answer: C

Explanation:
Calculation groups are created using Tabular Editor, an external modeling tool. They cannot be created directly in Power BI Desktop.

Question 4

Which scenario is BEST suited for a calculation group?

A. Creating a one-time KPI
B. Creating a simple ratio measure
C. Applying YTD, MTD, and YoY logic across many measures
D. Cleaning source data

Correct Answer: C

Explanation:
Calculation groups excel when the same calculation logic must apply consistently across many measures.

Question 5

What happens if multiple calculation groups exist in a model?

A. Power BI randomly applies one
B. Only the most recent one is used
C. Calculation precedence determines the order of execution
D. All calculation groups are ignored

Correct Answer: C

Explanation:
Calculation groups are applied based on precedence. Higher precedence values are evaluated first.

Question 6

Which statement about calculation groups is TRUE?

A. They are evaluated during data refresh
B. They increase model storage size significantly
C. They modify measures at query time
D. They can replace the need for base measures

Correct Answer: C

Explanation:
Calculation groups operate at query time, modifying how measures are evaluated without storing additional data.

Question 7

Why might a calculation group cause unexpected results?

A. It automatically filters fact tables
B. It overrides measure logic due to precedence
C. It recalculates columns dynamically
D. It disables relationships

Correct Answer: B

Explanation:
Incorrect calculation precedence can cause calculation groups to override or interfere with other calculations.

Question 8

Which is a limitation of calculation groups?

A. They cannot be used with measures
B. They require Power Query
C. They are not supported for all DirectQuery sources
D. They cannot be used in visuals

Correct Answer: C

Explanation:
Some DirectQuery sources have limited or no support for calculation groups, making them unsuitable in those scenarios.

Question 9

When should calculation groups generally NOT be used?

A. Large enterprise models
B. Models with many similar measures
C. Simple reports with only a few measures
D. Time intelligence scenarios

Correct Answer: C

Explanation:
In simple models, calculation groups may add unnecessary complexity and reduce clarity for report authors and users.

Question 10

Which best practice aligns with PL-300 guidance?

A. Use calculation groups to replace all measures
B. Use calculation groups only when they improve maintainability
C. Always create calculation groups for time intelligence
D. Avoid documentation when using calculation groups

Correct Answer: B

Explanation:
The exam emphasizes judgment and maintainability. Calculation groups should be used when they meaningfully simplify and standardize the model.

Key Exam Takeaways

• ✔ Calculation groups apply reusable logic to measures
• ✔ SELECTEDMEASURE() is foundational
• ✔ Created using Tabular Editor
• ✔ Precedence matters
• ❌ Not always appropriate for simple models

Go back to the PL-300 Exam Prep Hub main page

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Model the data (25–30%)
 --> Optimize model performance
    --> Improve Performance by Identifying and Removing Unnecessary Rows and Columns

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.

Practice Questions

1. Why does removing unused columns improve Power BI model performance?

A. It reduces the number of visuals that can be created
B. It decreases the number of DAX measures required
C. It reduces memory usage in the VertiPaq engine
D. It disables automatic relationships

✅ Correct Answer: C

Explanation:
Power BI stores data in the VertiPaq in-memory engine. Each column consumes memory—even if it is hidden. Removing unused columns reduces the model’s memory footprint, leading to faster refreshes and better query performance.

2. Where should unnecessary rows ideally be removed to achieve the best performance improvement?

A. In DAX measures
B. In calculated tables
C. In report-level filters
D. In Power Query

✅ Correct Answer: D

Explanation:
Removing rows in Power Query prevents the data from ever being loaded into the model. This reduces model size and improves refresh and query performance. Filtering data after it’s loaded (DAX or visuals) does not reduce memory usage.

3. Which type of column typically has the greatest negative impact on model size?

A. Numeric columns with repeated values
B. Boolean columns
C. High-cardinality text columns
D. Date columns

✅ Correct Answer: C

Explanation:
High-cardinality text columns (e.g., IDs, comments, URLs) compress poorly in VertiPaq and significantly increase model size. Removing unused high-cardinality columns is a key performance optimization technique.

4. A column is hidden in the model but not used in any visuals or calculations. What impact does it have on performance?

A. No impact, because it is hidden
B. It improves compression automatically
C. It still consumes memory
D. It is removed at refresh time

✅ Correct Answer: C

Explanation:
Hidden columns are still loaded into the model and consume memory. Hiding a column does not improve performance—removing it does.

5. Which scenario best justifies removing historical rows from a fact table?

A. The data refresh fails intermittently
B. Reports only require the last three years of data
C. Measures return incorrect totals
D. Relationships are inactive

✅ Correct Answer: B

Explanation:
If reports only require recent data, removing older historical rows reduces table size and improves performance without affecting business requirements.

6. Why is removing unnecessary columns from fact tables especially important?

A. Fact tables rarely participate in relationships
B. Fact tables usually have the fewest rows
C. Fact tables typically have the most rows
D. Fact tables do not support compression

✅ Correct Answer: C

Explanation:
Fact tables often contain millions of rows. Any unnecessary column in a large fact table significantly increases memory usage and query processing time.

7. Which action improves performance without reducing the model size?

A. Removing unused columns in Power Query
B. Filtering rows in Power Query
C. Using report-level filters
D. Removing columns from the source system

✅ Correct Answer: C

Explanation:
Report-level filters limit what is displayed but do not remove data from the model. The data is still loaded and stored in memory.

8. Which of the following columns is most likely safe to remove if not used?

A. Date key used in relationships
B. Surrogate key not used in any relationship
C. Foreign key linking to a dimension
D. Numeric measure column

✅ Correct Answer: B

Explanation:
Surrogate keys that are not used in relationships, visuals, or calculations add no analytical value and only increase model size.

9. Why is early filtering in Power Query recommended?

A. It increases DAX calculation accuracy
B. It enables query folding and reduces data transfer
C. It hides rows from report users
D. It automatically creates relationships

✅ Correct Answer: B

Explanation:
Filtering early allows Power BI to push filtering logic back to the data source (query folding), reducing the amount of data transferred, loaded, and stored in the model.

10. A Power BI model is slow, and analysis shows many unused descriptive columns in dimension tables. What is the BEST action?

A. Hide the columns
B. Convert them to measures
C. Remove the columns in Power Query
D. Create calculated columns instead

✅ Correct Answer: C

Explanation:
Removing unused descriptive columns in Power Query permanently reduces model size and improves performance. Hiding columns does not provide performance benefits.

Final Exam Tips

• Remove, don’t hide unused columns
• Filter rows in Power Query, not visuals
• Focus on high-cardinality columns and large fact tables
• Smaller models = faster performance (this is a recurring PL-300 theme)

Go back to the PL-300 Exam Prep Hub main page

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Model the data (25–30%)
   --> Optimize model performance
      --> Identify poorly performing measures, relationships, and visuals by using 
          Performance Analyzer and DAX query view

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.

Practice Questions:

Question 1

You run Performance Analyzer on a report page and notice that a visual has a very high DAX Query duration but a low Visual Display duration. What is the most likely cause?

A. The visual contains too many fields
B. The measure used by the visual is inefficient
C. The report page has too many visuals
D. The dataset refresh frequency is too high

✅ Correct Answer: B

Explanation:
A high DAX Query time indicates that the bottleneck occurs during DAX execution, which typically points to inefficient measures, complex calculations, or inefficient filter context.

Question 2

Which Power BI tool allows you to view and execute DAX queries generated by report visuals?

A. Model View
B. Performance Analyzer
C. DAX Query View
D. Power Query Editor

✅ Correct Answer: C

Explanation:
DAX Query View enables analysts to inspect and test DAX queries independently of visuals, making it ideal for diagnosing measure-level performance issues.

Question 3

You observe that multiple visuals across different report pages are slow, even though they use different visual types. What is the most likely root cause?

A. A complex report theme
B. Inefficient shared measures
C. Excessive visual formatting
D. Dataset refresh settings

✅ Correct Answer: B

Explanation:
If multiple visuals are slow across pages, the issue is often a shared measure or relationship rather than a visual-specific configuration.

Question 4

Which Performance Analyzer metric helps you determine whether slowness is caused by rendering rather than DAX?

A. Total Load Time
B. DAX Query
C. Other
D. Visual Display

✅ Correct Answer: D

Explanation:
High Visual Display time indicates the visual itself (layout, formatting, or rendering complexity) is the primary performance bottleneck.

Question 5

Which relationship configuration is most likely to negatively impact model performance?

A. One-to-many with single direction
B. Many-to-many with bi-directional filtering
C. One-to-one with single direction
D. Inactive relationship

✅ Correct Answer: B

Explanation:
Many-to-many relationships combined with bi-directional filtering significantly increase filter propagation complexity and often degrade performance.

Question 6

You copy a DAX query from Performance Analyzer and run it in DAX Query View. Why is this useful?

A. To change the visual’s formatting
B. To validate data refresh schedules
C. To isolate and analyze query performance
D. To modify relationships automatically

✅ Correct Answer: C

Explanation:
Running queries in DAX Query View helps isolate performance issues at the DAX level, independent of visual rendering.

Question 7

A table visual renders slowly, but Performance Analyzer shows low DAX Query time. What should you investigate next?

A. Measure logic
B. Data model relationships
C. Visual complexity and row count
D. Dataset size on disk

✅ Correct Answer: C

Explanation:
Low DAX time but slow rendering suggests the issue is visual-related—such as high row counts, many columns, or heavy formatting.

Question 8

Which DAX pattern is most commonly associated with poor performance when overused?

A. Simple aggregations like SUM
B. Iterators such as SUMX and FILTER
C. Implicit measures
D. Column sorting

✅ Correct Answer: B

Explanation:
Iterator functions evaluate row by row and can significantly slow down DAX queries when used excessively or unnecessarily.

Question 9

Why can unnecessary bi-directional relationships negatively affect report performance?

A. They increase dataset size
B. They prevent refresh from completing
C. They increase filter propagation complexity
D. They disable query folding

✅ Correct Answer: C

Explanation:
Bi-directional filtering increases the number of filter paths Power BI must resolve, often leading to slower query execution.

Question 10

What is the primary purpose of Performance Analyzer in Power BI Desktop?

A. Optimize dataset refresh times
B. Rewrite inefficient DAX automatically
C. Identify which visuals are slow and why
D. Monitor gateway performance

✅ Correct Answer: C

Explanation:
Performance Analyzer helps identify slow visuals and breaks down execution time, allowing analysts to pinpoint whether the issue lies in DAX, rendering, or other overhead.

Key Exam Takeaways

• Performance Analyzer identifies where time is spent
• DAX Query View helps analyze why queries are slow
• High DAX Query time → measure or relationship issue
• High Visual Display time → visual design issue
• Bi-directional and many-to-many relationships are frequent performance culprits

Go back to the PL-300 Exam Prep Hub main page

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Model the data (25–30%)
   --> Optimize model performance
      --> Improve Performance by Reducing Granularity

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.

Practice Questions

1. A Power BI model contains a Sales table with one row per transaction, including TransactionID, DateTime, ProductID, and SalesAmount. Reports only show daily sales totals. What is the BEST way to improve performance?

A. Create a calculated column to extract the date
B. Create a measure that sums SalesAmount
C. Aggregate the Sales table to daily totals in Power Query
D. Create a calculated table using SUMMARIZE

Correct Answer: C

Explanation:
Aggregating the table to daily totals before loading the data reduces row count and model size. Power Query aggregation is more efficient than DAX-based aggregation and is a best practice for performance optimization.

2. Which type of column most commonly increases granularity without adding analytical value?

A. Date
B. Product category
C. Transaction ID
D. Sales amount

Correct Answer: C

Explanation:
Transaction IDs are typically unique and dramatically increase granularity and cardinality. If they are not used for analysis, they should be removed to improve performance.

3. A dataset refresh is slow due to millions of rows from an IoT table with second-level timestamps. Reports only analyze data by day. What should you do?

A. Convert the timestamp to text
B. Remove the timestamp column
C. Aggregate the data by date in Power Query
D. Use DirectQuery instead of Import

Correct Answer: C

Explanation:
Aggregating the data by date significantly reduces granularity and row count, improving both refresh and query performance.

4. What is the PRIMARY trade-off when reducing granularity in a fact table?

A. Increased memory usage
B. Reduced model refresh frequency
C. Loss of detailed drill-down capability
D. Slower DAX calculations

Correct Answer: C

Explanation:
Reducing granularity improves performance but can limit the ability to drill into detailed records, such as individual transactions.

5. Which approach is MOST appropriate when users need both summary-level performance and occasional transaction-level analysis?

A. Keep only transaction-level data
B. Reduce granularity and remove detail
C. Use aggregation tables with a detailed fact table
D. Create calculated measures for aggregation

Correct Answer: C

Explanation:
Aggregation tables allow Power BI to use summarized data for most queries while retaining access to detailed data when needed.

6. Why does reducing granularity improve VertiPaq engine performance?

A. It increases the number of relationships
B. It reduces the number of visuals per report
C. It lowers row count and column cardinality
D. It forces single-direction filtering

Correct Answer: C

Explanation:
VertiPaq performs best with fewer rows and lower cardinality. Reducing granularity directly improves compression and query speed.

7. Which of the following is the BEST indicator that a table’s granularity is too high?

A. Many measures use CALCULATE
B. The table has a high row count but few visuals use it directly
C. The table contains many numeric columns
D. The table uses Import mode

Correct Answer: B

Explanation:
If a table contains massive detail that is never used in visuals or analysis, its granularity is likely higher than required.

8. A fact table includes a DateTime column with values down to the second. Reports only use Year and Month. What is the BEST action?

A. Hide the DateTime column
B. Replace DateTime with a Date column
C. Convert DateTime to text
D. Create a Year-Month calculated column

Correct Answer: B

Explanation:
Replacing DateTime with a Date column reduces cardinality and improves performance. Simply hiding the column does not improve the model.

9. Which optimization should be done FIRST when addressing performance issues caused by excessive granularity?

A. Rewrite DAX measures
B. Change relationship direction
C. Reduce rows and columns during data preparation
D. Enable bidirectional filtering

Correct Answer: C

Explanation:
Model-level optimizations—such as removing unnecessary rows and reducing granularity—should always be done before tuning DAX.

10. Which statement BEST reflects a PL-300 best practice regarding granularity?

A. Always store the most detailed data possible
B. Reduce granularity only after publishing the report
C. Match data granularity to actual reporting requirements
D. Use calculated tables instead of Power Query aggregation

Correct Answer: C

Explanation:
The optimal granularity is the lowest level of detail that still supports business questions. Overly detailed data harms performance without adding value.

Go back to the PL-300 Exam Prep Hub main page

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Visualize and analyze the data (25–30%)
   --> Create reports
      --> Select an appropriate visual

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.

Practice Questions

Question 1

You need to compare total sales across multiple product categories. Each category has a single aggregated value.
Which visual is most appropriate?

A. Line chart
B. Scatter chart
C. Column chart
D. Card

Correct Answer: C

Explanation:
Column (or bar) charts are best for comparing values across discrete categories. Line charts are better for trends over time, and cards only show a single value.

Question 2

A business user wants to see how revenue changes month over month for the last three years.
Which visual should you choose?

A. Area chart
B. Line chart
C. Pie chart
D. Treemap

Correct Answer: B

Explanation:
Line charts are ideal for showing trends over time and are the standard choice for time-series analysis in Power BI.

Question 3

You want to show the percentage contribution of each region to total sales, with only four regions.
Which visual is most appropriate?

A. Table
B. Scatter chart
C. Pie chart
D. Waterfall chart

Correct Answer: C

Explanation:
Pie charts are appropriate for part-to-whole comparisons when there are only a few categories. With many categories, they become difficult to interpret.

Question 4

A report needs to highlight a single KPI: Total Revenue for the current year.
Which visual should you use?

A. Gauge
B. Card
C. Column chart
D. Matrix

Correct Answer: B

Explanation:
A card visual is designed to display a single aggregated value clearly and prominently.

Question 5

You want to analyze the relationship between advertising spend and total sales to see if higher spend correlates with higher sales.
Which visual is best?

A. Line chart
B. Scatter chart
C. Stacked column chart
D. Treemap

Correct Answer: B

Explanation:
Scatter charts are used to analyze relationships between two numerical measures and are ideal for identifying correlations and outliers.

Question 6

A user wants to see how total sales break down by category and subcategory in a hierarchical way.
Which visual should you choose?

A. Pie chart
B. Table
C. Treemap
D. Line chart

Correct Answer: C

Explanation:
Treemaps are designed to display hierarchical data and part-to-whole relationships at multiple levels.

Question 7

You want to compare actual sales versus target sales across multiple regions.
Which visual is most appropriate?

A. Stacked bar chart
B. Scatter chart
C. Line chart
D. Card

Correct Answer: A

Explanation:
A stacked bar (or column) chart allows easy comparison between actual and target values across categories.

Question 8

A report must show the distribution of order values to identify outliers.
Which visual best supports this requirement?

A. Line chart
B. Pie chart
C. Histogram
D. Card

Correct Answer: C

Explanation:
Histograms are used to show the distribution of numeric data and are effective for spotting skewness and outliers.

Question 9

You want to display sales trends for multiple regions side-by-side without cluttering a single chart.
Which Power BI feature or visual approach is most appropriate?

A. Pie charts
B. Small multiples
C. KPI visuals
D. Gauge charts

Correct Answer: B

Explanation:
Small multiples allow you to repeat the same visual across categories, making comparisons easier without overcrowding a single chart.

Question 10

Which of the following is the most important factor when selecting a visual in Power BI?

A. The number of available visuals
B. The visual that looks the most impressive
C. The business question being answered
D. The amount of data in the model

Correct Answer: C

Explanation:
Visual selection should always be driven by the business question and the insight you want to communicate, not by aesthetics or volume of data.

Go back to the PL-300 Exam Prep Hub main page

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Visualize and analyze the data (25–30%)
   --> Create reports
      --> Format and configure visuals

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.

Practice Questions

Question 1

A column chart displays revenue by product category, but users want to see the exact revenue values directly on the bars.
What should you configure?

A. Enable the legend
B. Enable data labels
C. Change the chart type
D. Adjust the axis scale

Correct Answer: B

Explanation:
Data labels display exact values directly on the visual, allowing users to see precise figures without hovering.

Question 2

A report contains a line chart showing monthly sales, but the values are difficult to interpret because they are displayed as large numbers (for example, 1250000).
What is the best formatting adjustment?

A. Change the visual to a table
B. Apply conditional formatting
C. Set display units to millions
D. Enable tooltips

Correct Answer: C

Explanation:
Using display units such as millions or thousands improves readability while preserving trend interpretation.

Question 3

A legend on a stacked bar chart overlaps with data points, making the chart difficult to read.
What is the most appropriate solution?

A. Remove the legend
B. Change the visual type
C. Move the legend position
D. Disable interactions

Correct Answer: C

Explanation:
Repositioning the legend improves readability without removing useful category information.

Question 4

A stakeholder wants negative variance values in a table to stand out visually.
Which configuration best meets this requirement?

A. Apply a report theme
B. Sort the table descending
C. Use conditional formatting with color rules
D. Increase font size

Correct Answer: C

Explanation:
Conditional formatting allows you to apply visual cues, such as red coloring, to values that meet specific conditions.

Question 5

Two visuals showing sales by region use different colors for the same regions, confusing report users.
What should you do to fix this?

A. Remove one of the visuals
B. Apply consistent category colors
C. Enable data labels
D. Disable cross-filtering

Correct Answer: B

Explanation:
Using consistent colors for the same categories across visuals improves comprehension and reduces confusion.

Question 6

A bar chart should display categories ranked from highest sales to lowest sales.
Which setting should you adjust?

A. Axis scale
B. Sort order
C. Data labels
D. Visual interactions

Correct Answer: B

Explanation:
Sorting the visual by the sales measure in descending order ensures categories appear in the correct ranking.

Question 7

A KPI visual should clearly indicate whether performance meets a target.
Which configuration best supports this requirement?

A. Add a legend
B. Enable conditional formatting
C. Change font type
D. Adjust background transparency

Correct Answer: B

Explanation:
Conditional formatting helps visually distinguish between values that meet or miss targets, improving quick interpretation.

Question 8

A report consumer wants to see percentage values instead of decimals (for example, 0.25 instead of 25%).
Where should this formatting be applied?

A. In the visual’s title settings
B. In the axis settings
C. In the field’s number formatting
D. In the report theme

Correct Answer: C

Explanation:
Number formatting for fields controls how values such as percentages and decimals are displayed across visuals.

Question 9

Selecting a bar in one visual highlights related data in another visual, but users want the second visual to filter instead of highlight.
What should you configure?

A. Drill-through settings
B. Sorting behavior
C. Visual interactions
D. Legend position

Correct Answer: C

Explanation:
Visual interactions control whether visuals filter, highlight, or ignore selections from other visuals.

Question 10

Which principle should guide all formatting and configuration decisions in Power BI visuals?

A. Use as many formatting options as possible
B. Match the formatting used in other reports exactly
C. Improve clarity and accuracy for the end user
D. Make visuals visually impressive

Correct Answer: C

Explanation:
Formatting should always support clear, accurate communication of insights, not aesthetics or complexity.

Go back to the PL-300 Exam Prep Hub main page