- A
Use 'Unpivot Columns' to turn selected columns into attribute-value pairs.
Unpivot Columns is a valid transformation that converts selected columns into attribute-value pairs, normalizing data.
- B
Use 'Split Column' to divide a column into multiple columns based on a delimiter.
Why wrong: Option B is incorrect. 'Split Column' is a valid Power Query transformation, but it does not convert columns into attribute-value pairs or spread values across columns; it separates data based on a delimiter. The correct answers are A and C.
- C
Use 'Pivot Column' to turn unique values from a column into multiple columns.
Pivot Column is a valid transformation that turns unique values from a column into multiple columns for cross-tabulation.
- D
Use 'Merge Queries' to combine rows from multiple tables based on a key.
Why wrong: Merge Queries is a data combination operation that combines rows from multiple tables based on a key, but it is not considered a data transformation method in the context of this question; the correct transformations are Unpivot Columns and Pivot Column.
- E
Use 'Append Queries' to combine columns from two tables.
Why wrong: Append Queries stacks rows from multiple tables, not columns, and is not one of the two correct transformation methods.
PL-300 Unpivot Columns Practice Question
This PL-300 practice question tests your understanding of prepare the data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: unpivot Columns. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO of the following are valid methods to transform data in Power Query?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use 'Unpivot Columns' to turn selected columns into attribute-value pairs.
Option A is correct because the 'Unpivot Columns' transformation converts selected columns into attribute-value pairs, normalizing data for analysis. Option B is correct because 'Split Column' divides a column into multiple columns based on a delimiter, a common data preparation step. Options C, D, and E are incorrect: 'Pivot Column' is not considered a valid transformation in this context, while 'Merge Queries' combines rows (not columns) and 'Append Queries' combines rows, not columns.
Key principle: Unpivot Columns
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use 'Unpivot Columns' to turn selected columns into attribute-value pairs.
Why this is correct
Unpivot Columns is a valid transformation that converts selected columns into attribute-value pairs, normalizing data.
Related concept
Unpivot Columns
- ✗
Use 'Split Column' to divide a column into multiple columns based on a delimiter.
Why it's wrong here
Option B is incorrect. 'Split Column' is a valid Power Query transformation, but it does not convert columns into attribute-value pairs or spread values across columns; it separates data based on a delimiter. The correct answers are A and C.
- ✓
Use 'Pivot Column' to turn unique values from a column into multiple columns.
Why this is correct
Pivot Column is a valid transformation that turns unique values from a column into multiple columns for cross-tabulation.
Related concept
Unpivot Columns
- ✗
Use 'Merge Queries' to combine rows from multiple tables based on a key.
Why it's wrong here
Merge Queries is a data combination operation that combines rows from multiple tables based on a key, but it is not considered a data transformation method in the context of this question; the correct transformations are Unpivot Columns and Pivot Column.
- ✗
Use 'Append Queries' to combine columns from two tables.
Why it's wrong here
Append Queries stacks rows from multiple tables, not columns, and is not one of the two correct transformation methods.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common pitfall is confusing the purpose of 'Unpivot Columns' and 'Pivot Column'. Unpivot increases row count and decreases column count, while Pivot does the opposite. Also, 'Merge Queries' combines columns by matching keys (like SQL JOIN), while 'Append Queries' stacks rows, not columns.
Detailed technical explanation
How to think about this question
Under the hood, 'Unpivot Columns' uses the M language's Table.Unpivot function, which transforms a table by rotating columns into rows, creating an 'Attribute' column for original column names and a 'Value' column for the corresponding cell values. 'Pivot Column' uses Table.Pivot to aggregate and spread unique values from a column into multiple columns, often requiring an aggregation function like List.Sum. In real-world scenarios, unpivoting is critical for preparing data from cross-tab reports (e.g., monthly sales columns) into a normalized format for time-series analysis or Power BI visualizations.
KKey Concepts to Remember
- Unpivot Columns
- Pivot Column
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Unpivot Columns
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Unpivot Columns Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review unpivot Columns, then practise related PL-300 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this PL-300 question test?
Prepare the data — This question tests Prepare the data — Unpivot Columns.
What is the correct answer to this question?
The correct answer is: Use 'Unpivot Columns' to turn selected columns into attribute-value pairs. — Option A is correct because the 'Unpivot Columns' transformation converts selected columns into attribute-value pairs, normalizing data for analysis. Option B is correct because 'Split Column' divides a column into multiple columns based on a delimiter, a common data preparation step. Options C, D, and E are incorrect: 'Pivot Column' is not considered a valid transformation in this context, while 'Merge Queries' combines rows (not columns) and 'Append Queries' combines rows, not columns.
What should I do if I get this PL-300 question wrong?
Review unpivot Columns, then practise related PL-300 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Unpivot Columns
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Last reviewed: Jul 4, 2026
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