PL-300 Prepare the data Practice Question
Which TWO are valid ways to combine data from multiple sources in Power Query? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse 'combining data from multiple sources' with 'creating relationships in the data model,' which is a separate step performed after data loading, not a Power Query transformation.
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
✓
Append Queries.
Append Queries (B) is correct because it stacks rows from two or more tables with matching or similar columns into a single table, which is the standard Power Query way to combine data vertically from multiple sources. Merge Queries (D) is correct because it joins two tables horizontally on one or more matching key columns, equivalent to a SQL JOIN, allowing data from multiple sources to be combined side by side. Pivot Column (A) is not a combining operation; it reshapes existing values in a single table from rows into columns. Create relationships in the data model (C) links tables for analysis in Power Pivot/DAX but does not itself combine or transform data within Power Query. Group By (E) aggregates rows within a single table (e.g., sum, count) and does not combine data from multiple sources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pivot Column.
Why it's wrong here
Pivot Column is a transformation that reshapes a single table by turning distinct values from a chosen column into multiple new columns, often with an aggregate like sum or count. It expands the table horizontally and reduces rows, but it does not bring data from external sources into the query. Therefore it cannot combine data from multiple sources; it only changes the shape of an existing dataset.
- ✓
Append Queries.
Why this is correct
Append Queries combines multiple tables by vertically stacking all rows from each input into a single resultant table, which is essential when consolidating datasets that share a common schema—like monthly sales exports from different regions. It aligns columns by name and fills missing values with null for any mismatched columns, making it a true row-level combination method. This is one of the two valid ways to physically combine data in Power Query.
- ✗
Create relationships in the data model.
Why it's wrong here
Creating relationships in the data model does not combine data physically; it establishes logical links between tables based on matching keys for DAX filtering and calculation. Each source table remains independent in the tabular model, and the query layer is untouched, so Power Query never sees row or column integration. Consequently, relationship creation is a modeling technique, not a data-combining transformation.
- ✓
Merge Queries.
Why this is correct
Merge Queries joins two tables by matching rows on one or more key columns, producing a single table that contains columns from both sources, similar to SQL inner, left, right, or full outer joins. This column-level combination enriches the left table with attributes from the right table while aligning rows based on the join condition. It is the other valid Power Query method for combining data from multiple sources.
- ✗
Group By.
Why it's wrong here
Group By operations aggregate rows within a single table—grouping on one or more fields and calculating totals, averages, counts, or other summaries for each group. This reduces granularity by collapsing many rows into one per group, but it never imports or merges data from another source. Therefore Group By cannot combine multiple datasets; it only performs summarization on an already-loaded or single query.
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