PL-300: Prepare the Data (Power Query, Get & Transform)
Prepare the Data (Power Query, Get & Transform) is one of the skill areas tested on the Power BI Data Analyst (PL-300) exam. Below are free PL-300 practice questions with worked answers and a concept diagram — each with a plain-language explanation. Practice the first 10 questions of PL-300 free, no signup.
The concept, in one picture
3 free PL-300 Prepare the Data (Power Query, Get & Transform) questions
Your team runs a task-tracking application that is embedded entirely inside Microsoft Teams. The application was built with Microsoft Power Apps. You must build a Power BI report that reads the application's data. Which Get Data connector should you pick?
- Dataflows
- ✓ Dataverse
- SQL Server database
- Microsoft Teams Personal Analytics
A canvas or model-driven Power Apps application keeps its data in Microsoft Dataverse, so the Dataverse connector is the correct way to reach that data from Power BI.
You have already published a Power BI report whose model imports an Excel file kept in a SharePoint folder, and that model defines several measures. You must build an additional Power BI report on top of the same data while doing as little rework as possible. Which kind of data source should you connect to?
- an Excel workbook
- a Power BI dataflow
- ✓ a Power BI dataset
- a SharePoint folder
Connecting a new report to the existing published Power BI dataset reuses its tables, relationships, and measures, which minimizes development effort versus re-importing the source data.
In Power Query you load two Excel tables. Customer holds Customer ID, Customer Name, Phone, Email Address, and Address ID. Address holds Address ID, Address Line 1, Address Line 2, City, State/Region, Country, and Postal Code. Customer ID is unique per customer and Address ID is unique per address. You want one row for every customer, and each row must also show that customer's City, State/Region, and Country. Which action accomplishes this?
- Append the Customer table to the Address table.
- ✓ Merge the Customer and Address tables on Address ID.
- Transpose both the Customer and Address tables.
- Group both tables by the Address ID column.
A merge performs a join on the common Address ID column, bringing the address fields onto each customer row while preserving one row per customer. Append stacks rows and would not add columns.
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