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Read each scenario carefully and choose your answer before revealing the explanation. Then check why your choice was right or wrong. Repeat until the reasoning feels automatic.

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PL-300 · topic practice

Prepare the data practice questions

Practise Microsoft Power BI Data Analyst PL-300 Prepare the data practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security

20 questionsDomain: Prepare the data

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What to know about Prepare the data

Prepare the data questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

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Common Prepare the data exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Prepare the data questions

20 questions · select your answer, then reveal the explanation

Question 1mediummultiple choice

Read the full Prepare the data explanation →

A company uses Power BI to analyze sales data from a SQL Server database. The database contains a table 'Sales' with 10 million rows. The business analysts need to create daily reports that aggregate sales by region and product category. To optimize report performance, which data preparation technique should be applied?

Trap 1: Increase the row limit in Power Query to load all rows.

Loading all rows increases data volume.

Trap 2: Remove unused columns from the query.

Removing unused columns reduces width but not row count.

Trap 3: Import the entire table and aggregate in Power BI.

Importing all rows is inefficient.

Study all Prepare the data common traps →

A
Increase the row limit in Power Query to load all rows.
Why wrong: Loading all rows increases data volume.
B
Remove unused columns from the query.
Why wrong: Removing unused columns reduces width but not row count.
C
Import the entire table and aggregate in Power BI.
Why wrong: Importing all rows is inefficient.
D
Perform aggregation in SQL before importing.
Aggregating at source reduces rows significantly.

Prepare the data practice questions

What to know about Prepare the data

Common Prepare the data exam traps

Prepare the data questions

During data refresh in Power BI, an error occurs: 'The column 'OrderID' of the table 'Orders' contains a duplicate value and this column is part of a primary key.' The table 'Orders' is imported from an Azure SQL database. What is the most likely cause of this error?

A data analyst needs to combine two queries in Power Query: 'Sales2023' and 'Sales2024', both with identical column structures. Which operation should the analyst use to append the rows from 'Sales2024' to 'Sales2023'?

A Power BI report contains a table with a column 'Date' of type date. The report users need to filter data by fiscal year, which starts on April 1. What is the best practice to support this requirement during data preparation?

When importing data from a CSV file, Power Query detects that the first row contains column headers. However, the actual data starts from row 2. The analyst notices that some rows have extra columns due to commas within quoted fields. What is the most efficient way to handle this issue?

Which TWO actions can improve data refresh performance in Power BI?

A data analyst is preparing data from multiple Excel files stored in SharePoint. Each file has the same structure but different data. Which THREE steps are necessary to combine these files into a single table in Power Query?

You are preparing data for a Power BI report. You have a table that contains a 'ProductID' column with some null values. You need to ensure that the 'ProductID' column does not contain any null values in the data model. Which Power Query transformation should you apply?

You are reviewing a Power Query query that combines data from multiple CSV files in a folder. The query uses the 'Combine Files' function. Which TWO actions can you take to improve the performance of this query?

You are preparing data from a SQL Server database. The query includes a WHERE clause that filters rows based on a date column. You want to ensure that the filter is pushed back to the database (Query Folding). Which THREE conditions must be met?

You are building a Power BI report that uses a large fact table with 100 million rows. The data source is a SQL Server view that filters data by a date range. You want to minimize the data loaded into the model while maintaining the ability to query any date range later. What should you do?

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?

Refer to the exhibit. You load the Sales table into Power BI. You need to calculate the total net sales after discount (SalesAmount * (1 - Discount)). However, some rows have Null in the Discount column. What is the correct DAX measure?

You are creating a Power BI report from a SQL Server database that contains a table Orders with columns: OrderDate, CustomerID, ProductID, Quantity, UnitPrice. You need to build a star schema. Which THREE tables should you create? (Choose three.)

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