PL-300 Prepare the data Practice Question
You need to combine two tables: Sales and Products, where Sales has a ProductID column and Products has a ProductKey column. The tables have a many-to-one relationship. Which Power Query transformation should you use?
⚠ Common exam trap
Many exam-takers confuse Merge Queries with Append Queries, mistakenly thinking that combining tables always means stacking rows, rather than joining on a key relationship.
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
✓
Merge Queries.
Merge Queries (C) is correct because it performs a join between two tables based on matching columns, which is exactly what is needed to combine Sales and Products using ProductID and ProductKey. This transformation supports many-to-one relationships and allows you to expand related columns from the Products table into the Sales table, enabling further data analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Group By.
Why it's wrong here
Group By is an aggregation transformation that groups rows based on column values and computes summary statistics such as sum, average, or count. It does not introduce columns from another table; instead, it collapses multiple rows into fewer rows, losing detail. Since the goal is to add product information to each sales transaction, Group By cannot merge the two tables and would only produce summaries like total sales per product, missing the required row-level enrichment.
- ✗
Append Queries.
Why it's wrong here
Append Queries stacks rows from two or more tables vertically, requiring compatible column structures. When appending Sales and Products, which have different schemas (e.g., Sales columns vs. Product columns), unmatched columns are filled with nulls, and no relationship is established based on a key like ProductID. This operation increases row count rather than linking each sale to its product details, so it fails to combine the tables in the required relational way.
- ✓
Merge Queries.
Why this is correct
Merge Queries performs a relational join by matching rows on a common key column, such as ProductID, with configurable join types (Inner, Left Outer, Full Outer, etc.). This enriches each sales record with the corresponding product attributes (name, price, category) from the Products table in a single output table. Because the requirement is to combine two tables column-wise based on a shared field, Merge Queries is the correct approach.
- ✗
Pivot Column.
Why it's wrong here
Pivot Column is a reshaping operation that rotates unique values from one column into new column headers, often using an aggregation to fill the resulting matrix. It operates on a single table and cannot import data from another table; it only changes the orientation of existing data. Applying Pivot to Sales or Products would spread out values like ProductID into multiple columns, but it would not bring in product details for each sale, so it is unsuitable for combining two tables.
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