PL-300 Prepare the data Practice Question
Which THREE are types of Power Query transforms that can be used to clean data? (Choose three.)
⚠ Common exam trap
Many exam-takers confuse data preparation transforms (like merging or grouping) with data cleaning transforms, leading them to select options that are actually for data shaping or integration rather than direct data quality improvement.
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
Option A (Remove duplicates) is correct because Power Query provides a dedicated 'Remove Duplicates' transform that eliminates rows with identical values across selected columns, a core data-cleaning operation. Option C (Replace values) is correct because the 'Replace Values' transform substitutes specific text or numeric values (e.g., replacing 'N/A' with null), which is a standard cleansing step. Option E (Change data type) is correct because Power Query's 'Data Type' transform converts columns to the proper types (Text, Whole Number, Date, etc.), fixing type mismatches that would otherwise break calculations or loads. Option B (Group rows by a column) is not a cleaning transform but an aggregation/reshaping operation that summarizes data. Option D (Merge queries) is not a cleaning transform but a join operation that combines two queries, which is a data-shaping rather than cleansing activity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Remove duplicates
Why this is correct
Remove Duplicates is a row-level cleaning transform in Power Query that scans all selected columns and retains only the first occurrence of each unique combination, discarding subsequent identical rows. This directly reduces row count and eliminates redundant data that would otherwise skew aggregations, making it a genuine data-cleaning operation. Distinct from filtering or grouping, it requires no aggregation or merge logic—just deduplication based on key columns.
- ✗
Group rows by a column
Why it's wrong here
Group Rows in Power Query is an aggregation transform that collapses multiple rows into a single row per group, typically computing measures such as Sum, Count, Average, or custom aggregations. While it changes the shape of the table by reducing rows, it does not clean data in the sense of removing duplicates or correcting values; it deliberately loses granular detail to summarize data. Thus it is a shaping/summarizing operation, not a cleaning transform.
- ✓
Replace values
Why this is correct
Replace Values is a cell-level cleaning transform that substitutes one scalar value (for example null, blank, 'N/A', or an error literal) with another value across selected columns or the entire table. In Power Query, the related 'Replace Errors' action similarly swaps error values for a fallback, making this a fundamental data-cleansing step. Unlike row removal, it preserves row cardinality while repairing invalid or placeholder entries for downstream analysis.
- ✗
Merge queries
Why it's wrong here
Merge Queries in Power Query is a join operation that combines two tables based on matching columns, producing a new table with columns from both sides and options for inner, left outer, right outer, and full outer joins. Its purpose is data integration and enrichment, not cleaning—it does not remove duplicates, repair invalid entries, or correct data types within a single table. While merged results may require subsequent cleaning, the merge itself is a relational combination transform, not a cleaning one.
- ✓
Change data type
Why this is correct
Change Data Type is a column-level cleaning transform that converts a column's storage type—such as text to whole number, decimal, date, or logical—to match the intended semantic meaning. It ensures that downstream calculations, date arithmetic, and grouping behave correctly, because incorrect types are a classic source of data-quality issues. This operation modifies the column's metadata and value representation, making it an essential data-cleaning step during profiling and preparation.
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