PL-300 Model the data Practice Question
You have a Power BI data model with a table that contains duplicate rows. You want to remove duplicates in Power Query. Which transformation should you use?
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
✓
Remove Duplicates
The correct option is B, Remove Duplicates, because it is the Power Query transformation specifically designed to eliminate duplicate rows from a table by comparing all columns (or selected columns) and keeping only the first occurrence of each unique row. In this scenario, the table contains duplicate rows, so applying Remove Duplicates directly addresses the requirement without altering the underlying data values. Replace Values (A) only substitutes specific text or numbers and does not remove rows. Filter Rows (C) excludes rows based on conditions but cannot identify duplicates across all columns. Group By (D) aggregates rows and would change the table structure rather than simply removing duplicate rows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace Values
Why it's wrong here
Replace Values is a column-level transformation in Power Query that substitutes one specific value (e.g., text or number) with another, regardless of row context. It is essentially a find-and-replace operation and does not compare rows against each other, nor does it evaluate the uniqueness of records. Therefore, it cannot identify or remove duplicate rows; it only changes the contents of existing cells, leaving the row count and structure unchanged.
- ✓
Remove Duplicates
Why this is correct
Remove Duplicates is the dedicated Power Query transformation that removes rows with identical values across all selected columns, retaining only the first occurrence. By default, it considers all columns, and if a table contains duplicate records, selecting this option from the context menu or the ribbon will directly reduce the row count to only unique combinations. This operation is case-sensitive and treats nulls as values to compare, making it the correct and intended method for eliminating duplicate tabular data.
- ✗
Filter Rows
Why it's wrong here
Filter Rows is a conditional row-selection operation that keeps or removes rows based on logical predicates, such as numeric comparisons or text pattern matches. Unlike Remove Duplicates, it does not compare rows to one another; instead, each row is evaluated independently against the filter conditions. While it can exclude rows that happen to meet a criterion, it cannot guarantee that the remaining rows are unique, so it is ineffective for deduplication when the goal is to remove all duplicate records.
- ✗
Group By
Why it's wrong here
Group By is an aggregation transformation that groups rows by one or more columns and returns summary statistics (e.g., sum, count, average) for each group. This operation changes the data shape by collapsing multiple rows into a single row per group, but it does not retain the original granular rows; instead, it produces aggregated values. Although a creative custom aggregation might mimic row selection, Group By is not designed for deduplication and would lose the original record details, making it unsuitable for simply removing duplicate rows.
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