Category: Power BI

Business Intelligence, Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Resolve inconsistencies, unexpected or null values, and data quality issues (PL-300 Exam Prep)

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%)
  --> Profile and clean the data
    --> Resolve inconsistencies, unexpected or null values, and data quality issues

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 Null Values

You open Power Query and want to quickly identify which columns contain null values. Which feature should you use?

A. Column distribution
B. Column quality
C. Column profile
D. Query dependencies

Correct Answer: B

Explanation:
Column Quality displays the percentage of Valid, Error, and Empty (null) values, making it the fastest way to identify nulls in a column.

2. Replacing Null Values Appropriately

A numeric column contains null values that should be treated as zero for reporting purposes. What is the BEST approach?

A. Remove rows with null values
B. Replace null values with 0
C. Fill down the column
D. Change the column data type

Correct Answer: B

Explanation:
Replacing nulls with zero is appropriate when the business meaning supports it. Removing rows would result in data loss.

3. Fixing Inconsistent Text Values

A column contains values such as “USA”, “usa”, and “Usa”. What is the MOST efficient way to standardize these values?

A. Replace values manually
B. Create a conditional column
C. Apply text formatting (Uppercase)
D. Remove duplicate rows

Correct Answer: C

Explanation:
Applying a text format such as Uppercase standardizes casing efficiently across the entire column.

4. Detecting Unexpected Values

You suspect a column contains invalid numeric values outside the expected range. Which Power Query feature helps identify this?

A. Column quality
B. Column distribution
C. Column profile
D. Data type conversion

Correct Answer: C

Explanation:
Column Profile shows statistics such as minimum, maximum, and average, making it ideal for detecting outliers and unexpected values.

5. Understanding Data Profiling Scope

Why might column profiling results appear inaccurate in Power Query?

A. Profiling only works for text columns
B. Profiling is disabled by default
C. Profiling is based on a data sample
D. Profiling ignores null values

Correct Answer: C

Explanation:
By default, Power Query profiles only a sample of rows. You must enable “Column profiling based on entire dataset” for full accuracy.

6. Handling Error Values

A column contains error values caused by invalid data type conversions. What is the BEST first step?

A. Remove rows with errors
B. Replace errors with null
C. Fix the transformation causing the error
D. Change the column name

Correct Answer: C

Explanation:
Best practice is to address the root cause of errors rather than simply removing or replacing them.

7. Removing Leading and Trailing Spaces

A text column appears to have duplicate values due to extra spaces. Which transformation should you apply?

A. Clean
B. Trim
C. Replace values
D. Capitalize Each Word

Correct Answer: B

Explanation:
Trim removes leading and trailing spaces, which commonly cause hidden inconsistencies in text data.

8. Cleaning Non-Printable Characters

Some text values contain hidden characters that affect filtering and grouping. Which transformation should you use?

A. Trim
B. Replace errors
C. Clean
D. Format → Lowercase

Correct Answer: C

Explanation:
The Clean transformation removes non-printable characters that are not visible but can impact data quality.

9. Resolving Missing Values in Hierarchical Data

A dataset contains a category value only in the first row of each group, with subsequent rows being null. What is the BEST solution?

A. Replace nulls with “Unknown”
B. Fill down
C. Remove null rows
D. Merge columns

Correct Answer: B

Explanation:
Fill Down propagates the previous non-null value and is commonly used for hierarchical or grouped data structures.

10. Examining Category Inconsistencies

You want to identify inconsistent category names such as misspellings or variations. Which tool is MOST useful?

A. Column quality
B. Column distribution
C. Query parameters
D. Conditional formatting

Correct Answer: B

Explanation:
Column Distribution shows value frequency, making it easy to spot variations and inconsistencies in categorical data.

✅ PL-300 Exam Takeaways

Know which profiling tool answers which question
Understand why a transformation is used, not just how
Prefer fixing issues in Power Query instead of DAX
Expect scenario-based questions asking for the best action

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

Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Resolve Data Import Errors (PL-300 Exam Prep)

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%)
   --> Profile and clean the data
      --> Resolve Data Import Errors

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 the Cause of an Import Error

A column fails to load because Power BI cannot convert certain values to a numeric data type. What is the MOST likely cause?

A. Duplicate values in the column
B. Text values mixed with numeric values
C. The column contains too many rows
D. The column is hidden

Correct Answer: B

Explanation:
Type conversion errors commonly occur when text values (such as "N/A" or "Unknown") exist in a column expected to be numeric.

2. Viewing Only Error Rows

You want to see only the rows that caused an import error in Power Query. What should you do?

A. Enable Column distribution
B. Use Keep Errors on the column
C. Change the column data type
D. Open Advanced Editor

Correct Answer: B

Explanation:
Keep Errors filters the column to show only rows containing error values, making troubleshooting easier.

3. Best First Step When Errors Occur

A column shows error values after changing its data type. What is the BEST initial action?

A. Remove all error rows
B. Replace errors with null
C. Identify and fix the source values
D. Disable query refresh

Correct Answer: C

Explanation:
Best practice is to fix the root cause (invalid source values) before applying type conversion or replacing errors.

4. Handling Errors Without Losing Rows

You want to keep all rows but avoid breaking visuals caused by error values. What should you do?

A. Remove rows with errors
B. Replace errors with null
C. Replace values with zero
D. Delete the column

Correct Answer: B

Explanation:
Replacing errors with null preserves rows while preventing calculation and visualization failures.

5. Import Errors After Removing a Column

A query fails after a column is removed earlier in the applied steps. What is the MOST likely reason?

A. The column had duplicate values
B. A later step references the removed column
C. The column contained nulls
D. The column data type was incorrect

Correct Answer: B

Explanation:
Applied steps are sequential. If a later step references a removed or renamed column, the query will fail.

6. Merge Query Errors

A merge query fails because matching columns have different data types. What should you do?

A. Replace errors with null
B. Change both columns to the same data type
C. Remove duplicate rows
D. Use a conditional column

Correct Answer: B

Explanation:
Merge keys must have matching data types. Mismatches commonly cause merge errors.

7. Fixing Date Conversion Errors

Dates import as text and generate errors when converted to Date. The issue is caused by regional formatting differences. What is the BEST solution?

A. Replace errors with today’s date
B. Remove the column
C. Change data type using locale
D. Fill down the column

Correct Answer: C

Explanation:
Using Locale allows Power BI to correctly interpret date formats based on regional settings.

8. Understanding Replace Errors

What does the Replace Errors transformation do?

A. Removes rows with errors
B. Replaces null values only
C. Substitutes error values with a specified value
D. Fixes the underlying data issue automatically

Correct Answer: C

Explanation:
Replace Errors allows you to replace error values with a defined value (often null), but it does not fix the root cause.

9. Diagnosing Transformation Errors

Which Power Query feature helps identify which step in the query caused an error?

A. Column quality
B. Query dependencies
C. Applied Steps pane
D. Data view

Correct Answer: C

Explanation:
The Applied Steps pane shows each transformation and highlights where errors occur.

10. Best Practice for Preventing Import Errors

Which approach BEST reduces the risk of data import errors?

A. Converting data types as early as possible
B. Cleaning and validating data before type conversion
C. Removing all null values
D. Importing fewer columns

Correct Answer: B

Explanation:
Cleaning data before assigning data types prevents conversion errors and ensures stable transformations.

✅ PL-300 Exam Takeaways

Import errors usually stem from type mismatches, invalid values, or broken applied steps
Fixing the root cause is preferred over removing data
Know when to use Replace Errors, Keep Errors, and Remove Errors
Expect scenario-based questions that test transformation order and reasoning

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

Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Resolve Data Import Errors (PL-300 Exam Prep)

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%)
   --> Profile and clean the data
      --> Resolve Data Import Errors

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 the Cause of an Import Error

A column fails to load because Power BI cannot convert certain values to a numeric data type. What is the MOST likely cause?

A. Duplicate values in the column
B. Text values mixed with numeric values
C. The column contains too many rows
D. The column is hidden

Correct Answer: B

Explanation:
Type conversion errors commonly occur when text values (such as "N/A" or "Unknown") exist in a column expected to be numeric.

2. Viewing Only Error Rows

You want to see only the rows that caused an import error in Power Query. What should you do?

A. Enable Column distribution
B. Use Keep Errors on the column
C. Change the column data type
D. Open Advanced Editor

Correct Answer: B

Explanation:
Keep Errors filters the column to show only rows containing error values, making troubleshooting easier.

3. Best First Step When Errors Occur

A column shows error values after changing its data type. What is the BEST initial action?

A. Remove all error rows
B. Replace errors with null
C. Identify and fix the source values
D. Disable query refresh

Correct Answer: C

Explanation:
Best practice is to fix the root cause (invalid source values) before applying type conversion or replacing errors.

4. Handling Errors Without Losing Rows

You want to keep all rows but avoid breaking visuals caused by error values. What should you do?

A. Remove rows with errors
B. Replace errors with null
C. Replace values with zero
D. Delete the column

Correct Answer: B

Explanation:
Replacing errors with null preserves rows while preventing calculation and visualization failures.

5. Import Errors After Removing a Column

A query fails after a column is removed earlier in the applied steps. What is the MOST likely reason?

A. The column had duplicate values
B. A later step references the removed column
C. The column contained nulls
D. The column data type was incorrect

Correct Answer: B

Explanation:
Applied steps are sequential. If a later step references a removed or renamed column, the query will fail.

6. Merge Query Errors

A merge query fails because matching columns have different data types. What should you do?

A. Replace errors with null
B. Change both columns to the same data type
C. Remove duplicate rows
D. Use a conditional column

Correct Answer: B

Explanation:
Merge keys must have matching data types. Mismatches commonly cause merge errors.

7. Fixing Date Conversion Errors

Dates import as text and generate errors when converted to Date. The issue is caused by regional formatting differences. What is the BEST solution?

A. Replace errors with today’s date
B. Remove the column
C. Change data type using locale
D. Fill down the column

Correct Answer: C

Explanation:
Using Locale allows Power BI to correctly interpret date formats based on regional settings.

8. Understanding Replace Errors

What does the Replace Errors transformation do?

A. Removes rows with errors
B. Replaces null values only
C. Substitutes error values with a specified value
D. Fixes the underlying data issue automatically

Correct Answer: C

Explanation:
Replace Errors allows you to replace error values with a defined value (often null), but it does not fix the root cause.

9. Diagnosing Transformation Errors

Which Power Query feature helps identify which step in the query caused an error?

A. Column quality
B. Query dependencies
C. Applied Steps pane
D. Data view

Correct Answer: C

Explanation:
The Applied Steps pane shows each transformation and highlights where errors occur.

10. Best Practice for Preventing Import Errors

Which approach BEST reduces the risk of data import errors?

A. Converting data types as early as possible
B. Cleaning and validating data before type conversion
C. Removing all null values
D. Importing fewer columns

Correct Answer: B

Explanation:
Cleaning data before assigning data types prevents conversion errors and ensures stable transformations.

✅ PL-300 Exam Takeaways

Import errors usually stem from type mismatches, invalid values, or broken applied steps
Fixing the root cause is preferred over removing data
Know when to use Replace Errors, Keep Errors, and Remove Errors
Expect scenario-based questions that test transformation order and reasoning

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

Analytics, Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Select the appropriate column data type (PL-300 Exam Prep)

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
      --> Select the appropriate column data type

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. Choosing the Correct Type for IDs

A column contains values such as 100245, 100312, and 100587. These values are used only to uniquely identify records. What data type should you use?

A. Whole Number
B. Decimal Number
C. Text
D. Fixed Decimal Number

Correct Answer: C

Explanation:
Identifiers should be stored as Text, not numeric types, since they are not used in calculations and may contain leading zeros in the future.

2. Fixing Incorrect Aggregation Behavior

A column representing revenue does not aggregate correctly in visuals. The column’s data type is set to Text. What should you do?

A. Change the data type to Decimal Number
B. Replace null values
C. Remove duplicate rows
D. Create a calculated column

Correct Answer: A

Explanation:
Text columns cannot be aggregated. Changing the data type to Decimal Number enables proper numeric calculations.

3. When to Use Fixed Decimal Number

Which scenario is BEST suited for the Fixed Decimal Number data type?

A. Product quantity
B. Currency values requiring precision
C. Customer ID numbers
D. Percentage values used in visuals

Correct Answer: B

Explanation:
Fixed Decimal Number is designed for financial data where precision is critical, such as currency.

4. Handling Dates Imported as Text

A column contains date values stored as text due to regional formatting differences. What is the BEST way to convert them?

A. Change data type directly to Date
B. Replace values manually
C. Change data type using locale
D. Create a calculated column

Correct Answer: C

Explanation:
Using Change Type → Using Locale ensures Power BI interprets dates correctly based on regional formats.

5. Preventing Data Type Errors

You are experiencing errors after converting a column’s data type. What should you do FIRST?

A. Remove rows with errors
B. Replace errors with null
C. Clean and validate the source values
D. Disable automatic type detection

Correct Answer: C

Explanation:
Errors often occur because the data is not clean. Always clean data before converting types.

6. Relationship Creation Failure

You are unable to create a relationship between two tables on a key column. What is the MOST likely reason?

A. The column contains duplicate values
B. The column contains null values
C. The data types of the columns do not match
D. The column is hidden

Correct Answer: C

Explanation:
Relationship columns must have matching data types for Power BI to create the relationship.

7. Choosing Between Date and Date/Time

A column contains dates only, with no time component. Which data type is MOST appropriate?

A. Text
B. Date/Time
C. Date
D. Date/Time/Timezone

Correct Answer: C

Explanation:
Using Date avoids unnecessary time components and improves filtering and grouping accuracy.

8. Automatic Type Detection Issues

Power BI automatically assigns a Whole Number type to a column that should be text. Why is this a problem?

A. It increases model size
B. It prevents the column from being filtered
C. It can remove leading zeros
D. It causes duplicate values

Correct Answer: C

Explanation:
Numeric types do not preserve leading zeros, which can be critical for codes or IDs.

9. Best Practice for Applying Data Types

When should you apply data type changes in Power Query?

A. Immediately after loading the data
B. After creating relationships
C. After cleaning and transforming the data
D. After publishing the report

Correct Answer: C

Explanation:
Applying data types after cleaning prevents conversion errors and ensures consistent transformations.

10. Diagnosing Model Performance Issues

A Power BI model is larger than expected and performs poorly. One cause is unnecessary Date/Time columns where only Date is needed. What should you do?

A. Convert Date/Time columns to Date
B. Convert Date columns to Text
C. Remove the columns
D. Disable auto date/time

Correct Answer: A

Explanation:
Using Date instead of Date/Time reduces model size and improves performance when time data is not required.

✅ PL-300 Exam Takeaways

Data type selection affects calculations, relationships, and performance
IDs should almost always be Text
Clean data before converting data types
Know when to use Decimal vs Fixed Decimal
Expect subtle, behavior-based questions on the exam

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

Analytics, Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Create and transform columns (PL-300 Exam Prep)

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 and transform 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. Choosing the Correct Tool

You need to split a column named FullName into FirstName and LastName before loading the data into the model. What is the BEST approach?

A. Create two DAX calculated columns
B. Use Split Column in Power Query
C. Use a measure
D. Use a visual-level calculation

Correct Answer: B

Explanation:
Structural changes like splitting columns should be done in Power Query before the data loads into the model.

2. Fixing Inconsistent Text Data

A column contains values with inconsistent casing (e.g., “north”, “North”, “NORTH”). What transformation should you apply?

A. Replace values
B. Trim
C. Format → Uppercase
D. Conditional column

Correct Answer: C

Explanation:
Formatting text to Uppercase (or Lowercase) standardizes values efficiently and avoids manual replacements.

3. Extracting Date Components

You need a column containing only the year from an existing Date column. What is the BEST option?

A. Create a DAX measure
B. Extract → Year in Power Query
C. Convert the date to text
D. Use a conditional column

Correct Answer: B

Explanation:
Power Query provides built-in Extract options for date components such as Year, Month, and Day.

4. Creating Business Logic Columns

You want to classify sales as “High” when the amount is greater than 1,000 and “Low” otherwise. What should you use?

A. Replace values
B. Split column
C. Conditional column
D. Index column

Correct Answer: C

Explanation:
Conditional columns are designed for simple business logic and can be created through the UI without writing code.

5. Removing Hidden Characters

A text column contains hidden, non-printable characters that affect filtering. Which transformation should you apply?

A. Trim
B. Clean
C. Replace errors
D. Capitalize Each Word

Correct Answer: B

Explanation:
Clean removes non-printable characters that are not visible but can cause data issues.

6. Merging Text Values into One Column

You need to combine City and State columns into a single column called Location. What should you use?

A. Split Column
B. Replace Values
C. Merge Columns
D. Custom Column

Correct Answer: C

Explanation:
Merge Columns combines multiple columns into one using a specified delimiter.

7. When to Use a Custom Column

When should you create a Custom Column instead of a Conditional Column?

A. When no logic is required
B. When simple IF/ELSE logic is needed
C. When more complex formulas or functions are required
D. When renaming columns

Correct Answer: C

Explanation:
Custom columns use Power Query M code, allowing more complex logic than conditional columns.

8. Power Query vs DAX Calculated Columns

Which scenario is BEST suited for a DAX calculated column instead of a Power Query column?

A. Cleaning inconsistent text values
B. Splitting a column by delimiter
C. Creating a column dependent on model relationships
D. Removing leading spaces

Correct Answer: C

Explanation:
DAX calculated columns can reference model relationships, while Power Query columns cannot.

9. Creating Row Identifiers

You need to add a sequential number to each row to preserve order. What should you use?

A. Conditional column
B. Custom column
C. Index column
D. Replace values

Correct Answer: C

Explanation:
An Index Column assigns a sequential number to each row and is commonly used for sorting or tracking order.

10. Best Practice for Column Creation

Which is the BEST practice when creating and transforming columns?

A. Create all columns using DAX
B. Transform columns after publishing the report
C. Clean data before creating derived columns
D. Avoid renaming columns

Correct Answer: C

Explanation:
Derived columns should be created from clean, validated data to avoid compounding errors.

✅ PL-300 Exam Takeaways

Use Power Query for structural transformations
Use DAX calculated columns when logic depends on model context
Know when to split, merge, extract, or derive columns
Expect scenario-based questions testing tool choice and order of operations

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

Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Group and aggregate rows (PL-300 Exam Prep)

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
      --> Group and aggregate rows

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 have a sales table with one row per transaction. You need a table that shows total sales per customer, and this logic will not change. Where should you perform this aggregation?

A. Create a DAX measure using SUM()
B. Use Power Query Group By on Customer
C. Create a calculated column in DAX
D. Use a visual-level aggregation

Correct Answer: B

Explanation:
Power Query Group By is ideal for static aggregations that do not need to respond to slicers. This reduces data volume and improves performance, which aligns with PL-300 best practices.

Question 2

Which aggregation option in Power Query counts the number of rows per group, regardless of column values?

A. Count
B. Count Distinct
C. Count Rows
D. Sum

Correct Answer: C

Explanation:
Count Rows counts all rows in each group. This is commonly tested and often confused with Count Distinct.

Question 3

You group a fact table in Power Query by Product and Year. What is the resulting grain of the table?

A. One row per product
B. One row per year
C. One row per product and year
D. One row per transaction

Correct Answer: C

Explanation:
When grouping by multiple columns, the output grain is the unique combination of those columns.

Question 4

What is a key impact on the data model when you group and aggregate rows in Power Query?

A. Measures become faster but less accurate
B. Relationships are automatically removed
C. Detailed transaction-level data is no longer available
D. DAX calculations are no longer required

Correct Answer: C

Explanation:
Grouping in Power Query permanently removes lower-level detail, which can limit drill-down and analytical flexibility.

Question 5

Which scenario indicates you should NOT group data in Power Query?

A. The dataset is very large
B. Aggregation logic is fixed
C. Users need slicer-driven calculations
D. You want to reduce model size

Correct Answer: C

Explanation:
If calculations must respond dynamically to slicers or filters, aggregation should be handled with DAX measures, not Power Query grouping.

Question 6

A Power BI report requires time intelligence calculations such as Year-to-Date sales. Where should aggregation occur?

A. Power Query Group By
B. Calculated columns
C. DAX measures
D. Visual-level filters

Correct Answer: C

Explanation:
Time intelligence requires dynamic context, which only DAX measures can provide. Grouping in Power Query would prevent proper time-based calculations.

Question 7

What does the All Rows aggregation option produce in Power Query?

A. A single aggregated value
B. A calculated column
C. A nested table per group
D. A distinct count

Correct Answer: C

Explanation:
All Rows creates a nested table containing all rows for each group, often used for advanced transformations or custom calculations.

Question 8

You mistakenly use Sum instead of Count Rows during a Group By operation. What is the most likely issue?

A. Incorrect totals due to data type mismatch
B. Missing relationships in the model
C. Duplicate rows created
D. Slower refresh times

Correct Answer: A

Explanation:
Using the wrong aggregation function can produce incorrect results, especially if the column contains non-additive values.

Question 9

Which benefit is most directly associated with grouping data in Power Query?

A. Improved visual formatting
B. Reduced dataset size
C. Increased DAX flexibility
D. Automatic relationship creation

Correct Answer: B

Explanation:
Grouping reduces the number of rows loaded into the model, improving performance and memory usage.

Question 10

Which statement best reflects a PL-300 best practice regarding grouping and aggregation?

A. Always aggregate data before loading
B. Group data only when DAX cannot be used
C. Group data when the required grain is known and fixed
D. Use grouping to replace all measures

Correct Answer: C

Explanation:
Grouping is appropriate when the required grain is known, stable, and does not require interactivity. Knowing when not to group is just as important for the exam.

Final Exam Tips for This Topic

Expect decision-based questions, not just “how-to”
Be clear on Power Query vs DAX responsibilities
Understand grain, performance, and flexibility trade-offs
Watch for Count vs Count Rows vs Count Distinct
Remember: Grouping is irreversible

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

Analytics, Business Intelligence, Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Pivot, Unpivot, and Transpose Data (PL-300 Exam Prep)

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
      --> Pivot, Unpivot, and Transpose Data

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 dataset contains monthly sales stored in separate columns named Jan, Feb, Mar, and so on. You need to create a time-series visual that supports filtering by month. What should you do?

A. Pivot the Month columns
B. Unpivot the Month columns
C. Transpose the table
D. Create calculated columns in DAX

Correct Answer: B

Explanation:
Unpivoting converts repeated value columns into a single attribute–value pair, producing a normalized structure suitable for time-series analysis.

Question 2

You want to convert unique values from a column into individual columns, displaying aggregated results per category. Which transformation should you use?

A. Unpivot Columns
B. Transpose Table
C. Pivot Columns
D. Group By

Correct Answer: C

Explanation:
Pivoting turns values from one column into multiple columns, often requiring an aggregation function such as Sum or Count.

Question 3

Which transformation flips all rows into columns and columns into rows?

A. Pivot
B. Unpivot
C. Group By
D. Transpose

Correct Answer: D

Explanation:
Transpose rotates the entire table structure and is typically used when the table orientation is incorrect.

Question 4

After transposing a table, the first row now contains the desired column headers. What should you do next?

A. Unpivot Columns
B. Rename columns manually
C. Promote the first row to headers
D. Create a calculated table

Correct Answer: C

Explanation:
Promoting the first row to headers finalizes the transformation after transposing data.

Question 5

Which scenario best indicates that unpivoting is required?

A. Values in a column need to be summarized
B. Categories should become column headers
C. Repeating attribute columns must become rows
D. Data needs to be grouped by multiple keys

Correct Answer: C

Explanation:
Unpivoting is used to normalize data where multiple columns represent the same attribute (such as months or measures).

Question 6

What is a potential negative impact of pivoting data in Power Query?

A. Increased DAX complexity
B. Reduced model performance due to many columns
C. Loss of relationships
D. Automatic removal of measures

Correct Answer: B

Explanation:
Pivoting can significantly increase the number of columns, bloating the model and negatively affecting performance.

Question 7

You receive a dataset where column headers are stored in the first column instead of the first row. Which transformation is most appropriate?

A. Unpivot
B. Pivot
C. Transpose
D. Group By

Correct Answer: C

Explanation:
Transposing the table re-orients rows and columns so headers can be promoted correctly.

Question 8

Which transformation often requires you to specify an aggregation function?

A. Transpose
B. Unpivot
C. Pivot
D. Remove Columns

Correct Answer: C

Explanation:
Pivoting may result in multiple values for a new column and therefore requires aggregation (Sum, Count, Average, etc.).

Question 9

You want to keep identifier columns (such as ProductID and Region) while converting all other columns into rows. Which option is best?

A. Pivot Selected Columns
B. Unpivot Other Columns
C. Transpose Table
D. Group By

Correct Answer: B

Explanation:
Unpivot Other Columns preserves key identifier columns while normalizing all remaining columns.

Question 10

Which statement reflects a PL-300 best practice for reshaping data?

A. Always pivot data before loading
B. Use transpose for large fact tables
C. Normalize data before modeling when possible
D. Avoid unpivoting to improve performance

Correct Answer: C

Explanation:
Normalized (unpivoted) data supports better filtering, relationships, and DAX flexibility, which aligns with PL-300 modeling principles.

Final Exam Tips for This Topic

Identify whether the data is wide or tall
Pivot = rows → columns
Unpivot = columns → rows
Transpose = rotate entire table
Watch for aggregation requirements when pivoting
The exam emphasizes choosing the right transformation, not UI memorization

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

Analytics, Business Intelligence, Data Education & Training, Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Convert Semi-Structured Data to a Table (PL-300 Exam Prep)

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
      --> Convert Semi-Structured Data to a Table

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 load a JSON file into Power BI. The resulting table contains a single column where each row shows List. What is the first step to analyze the data?

A. Expand the column
B. Convert the list to a table
C. Promote headers
D. Split the column by delimiter

Correct Answer: B

Explanation:
Lists must be converted into tables before they can be expanded or analyzed as rows.

Question 2

A column in Power Query displays Record in each row. What does this indicate?

A. The column contains duplicated values
B. The column contains nested structured fields
C. The column contains multiple rows per record
D. The column contains untyped data

Correct Answer: B

Explanation:
A Record represents a nested structure with named fields that can be expanded into columns.

Question 3

Which Power Query action is used to expose fields stored inside a record?

A. Convert to Table
B. Pivot Column
C. Expand Column
D. Transpose Table

Correct Answer: C

Explanation:
Expanding a record reveals its internal fields as individual columns.

Question 4

An API response loads as a table with a column containing lists of values. What is the correct transformation sequence?

A. Expand → Promote Headers
B. Convert to Table → Expand
C. Split Column → Fill Down
D. Group By → Expand

Correct Answer: B

Explanation:
Lists must be converted into tables first, after which they can be expanded.

Question 5

After expanding nested data, you notice duplicate rows in your fact table. What is the most likely cause?

A. Incorrect data type
B. Expanding without understanding data granularity
C. Missing relationships
D. Failure to promote headers

Correct Answer: B

Explanation:
Expanding nested structures without considering the grain can duplicate rows and inflate fact tables.

Question 6

You import an Excel file where headers appear in multiple rows instead of a single row. What is the most appropriate approach?

A. Expand the column
B. Convert the table to a list
C. Transpose the table and promote headers
D. Group rows by column

Correct Answer: C

Explanation:
Transposing realigns rows and columns so headers can be promoted properly.

Question 7

Which Power Query feature is most useful when category labels appear only once and apply to multiple rows below?

A. Replace Values
B. Fill Down
C. Unpivot Columns
D. Merge Queries

Correct Answer: B

Explanation:
Fill Down propagates header or category values to related rows, common in semi-structured spreadsheets.

Question 8

Why is it recommended to expand only required fields when converting semi-structured data?

A. To reduce report refresh frequency
B. To improve visual formatting
C. To reduce model size and complexity
D. To enable DirectQuery mode

Correct Answer: C

Explanation:
Expanding unnecessary fields increases model size and can negatively impact performance and usability.

Question 9

Which transformation should be completed before creating relationships in the data model?

A. Creating measures
B. Flattening semi-structured data
C. Formatting visuals
D. Applying row-level security

Correct Answer: B

Explanation:
Relationships require clean, tabular data. Semi-structured data must be flattened first.

Question 10

Which statement best reflects a PL-300 best practice for handling semi-structured data?

A. Leave nested data unexpanded until report creation
B. Use DAX to flatten semi-structured data
C. Normalize and flatten data in Power Query
D. Always transpose semi-structured tables

Correct Answer: C

Explanation:
Power Query is the correct place to normalize and flatten semi-structured data before modeling and analysis.

Final Exam Tips for This Topic

Recognize lists vs records vs tables
Lists → Convert to table
Records → Expand
Inspect data grain before expanding
Clean data before flattening
This topic is about recognition and transformation choices, not memorizing UI clicks

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

Analytics, Business Intelligence, Data Education & Training, Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Create Fact Tables and Dimension Tables (PL-300 Exam Prep)

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.

Practice Questions

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

Analytics, Business Intelligence, Data Education & Training, Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Identify when to use reference or duplicate queries and the resulting impact (PL-300 Exam Prep)

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
      --> Identify when to use reference or duplicate queries and the resulting impact

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 have a query that cleans and standardizes sales data. You need to create several dimension tables from this cleaned dataset. Which option should you use?

A. Duplicate the query for each dimension
B. Reference the query for each dimension
C. Import the source data multiple times
D. Merge the query with itself

✅ Correct Answer: B

Explanation:
Referencing allows multiple tables to inherit the same cleaned logic from a single base query. This ensures consistency and reduces repeated transformation steps, which is a recommended best practice for production models.

Question 2

What is the primary difference between a referenced query and a duplicated query?

A. Referenced queries refresh faster
B. Duplicated queries do not support transformations
C. Referenced queries depend on the original query
D. Duplicated queries cannot be loaded to the model

✅ Correct Answer: C

Explanation:
A referenced query is dependent on its source query and will reflect any changes made to it. A duplicated query is an independent copy with no dependency.

Question 3

A change made to a base query causes multiple downstream queries to fail during refresh. What is the most likely reason?

A. The downstream queries were duplicated
B. The downstream queries were referenced
C. The model relationships were deleted
D. The data source credentials expired

✅ Correct Answer: B

Explanation:
Referenced queries rely on the base query. If a breaking change is introduced (such as removing or renaming a column), all dependent referenced queries may fail.

Question 4

When should you duplicate a query instead of referencing it?

A. When you want transformations to stay consistent
B. When creating multiple dimension tables
C. When experimenting with major changes
D. When reducing refresh dependencies

✅ Correct Answer: C

Explanation:
Duplicating a query is ideal when testing or experimenting, because changes will not affect other queries or downstream dependencies.

Question 5

Which impact is most commonly associated with excessive query duplication?

A. Improved refresh reliability
B. Reduced data volume
C. Increased maintenance effort
D. Better data lineage visibility

✅ Correct Answer: C

Explanation:
Duplicating queries can lead to repeated transformation logic, making the model harder to maintain and increasing the risk of inconsistent data shaping.

Question 6

How does Power BI’s View Lineage represent referenced queries?

A. As independent branches
B. As disconnected tables
C. As upstream and downstream dependencies
D. As hidden queries

✅ Correct Answer: C

Explanation:
Referenced queries appear as downstream dependencies in View Lineage, clearly showing how data flows from base queries to derived queries.

Question 7

You want to ensure that a change to data cleansing logic automatically applies to all derived tables. What should you do?

A. Duplicate the query
B. Reference the query
C. Disable query loading
D. Create calculated tables

✅ Correct Answer: B

Explanation:
Referencing ensures that any change to the base query propagates to all dependent queries automatically.

Question 8

Which of the following is a common mistake when using referenced queries?

A. Using them for experimentation
B. Using them for dimension creation
C. Forgetting that changes propagate downstream
D. Using them to centralize data cleaning

✅ Correct Answer: C

Explanation:
A frequent mistake is forgetting that changes to a referenced base query can unintentionally affect multiple dependent queries.

Question 9

Which approach generally results in a cleaner and more maintainable data model?

A. Duplicating all queries
B. Referencing a well-designed base query
C. Importing data separately for each table
D. Performing transformations in DAX

✅ Correct Answer: B

Explanation:
Using a base query with referenced downstream queries centralizes transformation logic and simplifies maintenance, which aligns with Microsoft’s recommended modeling practices.

Question 10

Which scenario best illustrates when NOT to use a referenced query?

A. Creating a product dimension
B. Applying consistent formatting rules
C. Testing a new transformation approach
D. Creating multiple tables from a single source

✅ Correct Answer: C

Explanation:
Referenced queries should not be used when testing or experimenting with transformations, because changes may impact other dependent queries. Duplicating is safer in this case.