PL-300 Model the data Practice Question
You are modeling data from a source that includes a column 'FullName' (e.g., 'John Doe'). You want to create separate 'FirstName' and 'LastName' columns for analysis. What is the most efficient way?
⚠ Common exam trap
It's easy for candidates to choose DAX calculated columns (Option A) because they are familiar with Excel-like formulas, but they overlook that Power Query is the correct tool for data transformation in Power BI, and DAX should be reserved for measures and calculated columns that depend on the data model's context.
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
✓
In Power Query, split the column by delimiter (space) into two columns.
Splitting a column by delimiter in Power Query is the most efficient, native method for transforming data at the query level. It leverages Power Query's M language to perform the split in a single step, which is optimized for performance and can be refreshed automatically. This approach avoids the overhead of DAX calculated columns, which are computed in the storage engine and can slow down report rendering.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create calculated columns using DAX functions LEFT, RIGHT, and FIND.
Why it's wrong here
Creating calculated columns with DAX functions such as LEFT, RIGHT, and FIND generates an additional column in the VertiPaq model, which expands memory usage and degrades refresh performance because these calculations are evaluated row-by-row at query time. Moreover, DAX string functions require fixed positional arguments, so names with middle names or variable-length suffixes would be parsed incorrectly, and splitting should occur during the data transformation stage rather than the modeling layer.
- ✗
Use the 'Replace Values' feature to manually separate names.
Why it's wrong here
The 'Replace Values' feature substitutes one exact string for another; it cannot extract two separate substrings from a single name into distinct columns, nor can it handle many rows efficiently because every replacement must be manually configured in advance. Using it to split a full-name column would require you to identify each unique first and last name combination, which is impractical for large datasets and leaves the original column intact.
- ✗
Use Excel formulas in a source query.
Why it's wrong here
Excel formulas are not part of the Power Query or M language environment, so they cannot be executed inside a query step designed to shape the source data. Even if the source is an Excel workbook, relying on spreadsheet formulas forces transformations to remain in the upstream file, making the ETL process brittle and dependent on an external application instead of using Power Query's native, repeatable transformation capabilities.
- ✓
In Power Query, split the column by delimiter (space) into two columns.
Why this is correct
Splitting a column by the space delimiter in Power Query is a native M transformation that invokes the Splitter.SplitTextByDelimiter function behind the scenes, generating two separate columns for first and last names in a single step. This declarative approach is optimized for large volumes of rows, requires no manual mapping, and automatically applies to every row, making it the most efficient and maintainable solution among the choices.
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