PL-300 Prepare the data Practice Question
You are preparing data in Power BI Desktop. You have a table with a column 'CustomerID' that contains duplicate values. You need to create a relationship to another table that also has 'CustomerID'. However, the relationship requires unique values in at least one of the tables. What should you do?
⚠ Common exam trap
It's easy for candidates to think changing data types or merging tables will solve the uniqueness issue, but Power BI specifically requires unique values in at least one table for a relationship, and only removing duplicates directly addresses that requirement.
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 duplicate rows from the 'CustomerID' column in the dimension table.
To create a relationship in Power BI, at least one table must have unique values in the column used for the relationship. By removing duplicate rows from the 'CustomerID' column in the dimension table (which should contain unique identifiers), you ensure that the dimension table has unique values, allowing a one-to-many relationship to be established with the fact table. This is a standard data modeling practice in Power BI to enforce referential integrity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a composite key using multiple columns.
Why it's wrong here
A composite key created from multiple columns still requires the combined set of values to be unique for every row. If 'CustomerID' itself contains duplicates, those duplicate rows will not be uniquely identified unless the second column also differs; but then the relationship from the fact table would need to include that second column as well. That changes the grain of the dimension and does not solve the core problem of a non-unique CustomerID.
- ✗
Use the 'Merge Queries' feature to combine the tables into one.
Why it's wrong here
Merge Queries joins two tables horizontally into a single table based on matching key values; it does not validate uniqueness or create any relationship metadata in the data model. If you merge the dimension and fact tables, you will end up with repeated dimension values whenever a customer has many transactions, which is the opposite of what a clean dimension should be. The correct approach is to deduplicate the dimension table, not to merge tables.
- ✗
Change the data type of 'CustomerID' to text.
Why it's wrong here
Changing the data type of CustomerID to text only changes how Power BI interprets and displays the values; it leaves the actual data values and their frequency untouched. Duplicate CustomerID entries remain duplicates after the type conversion, so the 'one' side of the relationship is still not unique. This action introduces no constraint and removes no rows.
- ✓
Remove duplicate rows from the 'CustomerID' column in the dimension table.
Why this is correct
Removing duplicate rows from the CustomerID column in the dimension table guarantees that each customer appears exactly once, which satisfies the uniqueness requirement for the 'one' side of a one-to-many relationship. In Power Query, you can select the CustomerID column and click 'Remove Duplicates' to keep the first occurrence and delete the rest. This creates a clean dimension that can be joined correctly to the fact table via a relationship.
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