PL-300 Prepare the data Practice Question
You have a Power BI dataset that combines sales data from two Excel files: Sales2023.xlsx and Sales2024.xlsx. Both files have the same schema. You need to combine them into a single table without duplicating rows. What is the best approach in Power Query?
⚠ Common exam trap
Many candidates confuse Append Queries (vertical stacking) with Merge Queries (horizontal joining), or mistakenly think DAX Union is appropriate for data preparation, when Power Query is the correct tool for this task.
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 Append Queries.
Append Queries in Power Query is specifically designed to combine rows from two or more tables with the same schema into a single table, stacking them vertically without duplicating rows. This operation is performed in the Power Query Editor (M language) and is the standard approach for unioning data from multiple sources during the data preparation phase, before loading into the Power BI data model.
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 Union in DAX.
Why it's wrong here
DAX UNION is a table function that combines rows from two tables, but it operates after the data has been loaded into the model, not during the Power Query transformation stage. Using UNION forces the combination to occur in-memory during query time, which can incur performance overhead and make the data model less transparent. The recommended approach is to combine the sales tables in Power Query using Append Queries, where data is shaped and loaded once, efficiently.
- ✗
Use Group By to summarize data.
Why it's wrong here
Group By in Power Query is an aggregation operation that groups rows based on the values in one or more columns and calculates summary statistics such as totals or counts. It reduces the number of rows and removes the underlying detail, so it cannot be used to simply stack rows from two sales tables into one comprehensive dataset. Appending the two tables preserves every row and detail, which is what combining raw sales data requires.
- ✓
Use Append Queries.
Why this is correct
Append Queries in Power Query is specifically designed to combine two or more tables by stacking their rows one after another, aligning columns by name. When you have sales data from two sources with the same structure, appending creates a single table containing all records from both sources, which is exactly what the scenario requires. It runs at data refresh time in Power Query, ensuring the combined data is loaded efficiently into the Power BI data model.
- ✗
Use Merge Queries as a new query.
Why it's wrong here
Merge Queries performs a column-wise join, similar to a SQL JOIN, where two tables are combined by matching rows on a common key, producing a wider table with additional columns. It does not concatenate rows from different sources; instead, it enriches the left table with columns from the right table. Since the task requires stacking sales from two tables, merging would not increase the row count and would actually alter the table structure incorrectly.
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