PL-300 Prepare the data Practice Test 17 — 15 Questions

Question 1

You are preparing a Power BI report that uses data from Azure SQL Database. The data includes a date column that needs to be used in time intelligence calculations. You want to ensure that the date column is recognized as a date table in the data model. What should you do?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

In the model view, mark the table as a date table by selecting the date column.. Marking a table as a date table in the model view explicitly tells Power BI that the table contains a complete set of dates for time intelligence calculations. This ensures that DAX functions like TOTALYTD, SAMEPERIODLASTYEAR, and DATEADD work correctly by using the marked date column as the primary date reference for the model, rather than relying on auto-generated date hierarchies.

Answer

Set the data type of the date column to Date.

Answer

Use the DAX function DATEADD to create a date table.

Answer

Create a calculated column using DATEVALUE to convert the date.

Question 2

You are importing data from a CSV file that contains a column with mixed data types (numbers and text). Power BI automatically assigns the data type as Text. You need to perform numerical aggregations on this column. What should you do?

Accepted Answer

In Power Query Editor, change the data type of the column to Whole Number or Decimal Number.. Changing the column's data type to Whole Number or Decimal Number in Power Query Editor will force Power BI to interpret the numeric values as numbers, enabling aggregations like SUM or AVERAGE. Power Query Editor provides a robust transformation environment where data type changes are applied during the load process, ensuring the column is treated as numeric for all downstream calculations. This approach is more efficient and reliable than using DAX conversions, as it avoids the overhead of calculated columns and leverages Power Query's native type detection and error handling.

Answer

Split the column using a delimiter to separate numbers from text.

Answer

Create a relationship with a numeric table to enable aggregation.

Answer

Create a calculated column in DAX using VALUE() to convert the text to numbers.

Question 3

You are importing data from an Excel workbook that contains multiple sheets. Each sheet has similar structure but different data for different regions. You need to combine all sheets into a single table for analysis. What should you do?

Accepted Answer

In Power Query Editor, use Append Queries to combine the sheets.. The Append Queries operation in Power Query Editor is designed to combine rows from multiple tables or queries with similar column structures into a single table. Since each sheet in the Excel workbook contains data for different regions with the same structure, appending them stacks the rows, creating a unified dataset for analysis.

Answer

In Power Query Editor, use Merge Queries to combine the sheets.

Answer

Copy and paste the data from each sheet into a master sheet in Excel.

Answer

In Power Query Editor, use Group By to consolidate the data.

Question 4

You are preparing data for a Power BI report. The source data contains a column with values like '1,234.56' formatted as text. You need to convert this to a numeric value for calculations. What is the best approach?

Accepted Answer

In Power Query Editor, replace the comma with an empty string, then change the data type to Decimal Number.. Power Query Editor provides the most efficient and scalable method for cleaning and converting text-based numeric data. By replacing the comma with an empty string and then changing the column data type to Decimal Number, you perform the transformation directly in the data preparation layer (M language), which is optimized for performance and avoids the overhead of DAX calculated columns. This approach also ensures the data remains clean for all downstream calculations.

Answer

In Power Query Editor, split the column by comma and then use the second part.

Answer

In DAX, create a calculated column using VALUE() after removing commas.

Answer

In Power Query Editor, use the 'Clean' transform to remove non-numeric characters.

Question 5

Which TWO of the following are valid methods to combine data from multiple sources in Power BI?

Accepted Answer

Append Queries. Append Queries is a valid method to combine data from multiple sources in Power BI because it stacks rows from two or more tables into a single table, which is essential when you have similar data structures across different sources (e.g., monthly sales files). This operation is performed in Power Query Editor and corresponds to a UNION operation in SQL, making it a standard data preparation technique.

Answer

Pivot Column

Answer

Unpivot Columns

Answer

Group By

Question 6

Which TWO of the following are valid ways to create a date table in Power BI?

Accepted Answer

Using DAX function CALENDAR or CALENDARAUTO.. The DAX functions CALENDAR and CALENDARAUTO are specifically designed to generate a single-column table of dates, which can then be used as a date table. CALENDAR requires explicit start and end dates, while CALENDARAUTO automatically scans the model to determine the date range. Both are standard methods for creating a date table in Power BI.

Answer

Using the time intelligence functions like TOTALYTD.

Answer

Marking an existing table as a date table.

Answer

Enabling the auto date/time feature.

Question 7

You are a data analyst at a retail company. You are building a Power BI report to analyze sales performance across multiple stores. The source data comes from an Azure SQL Database that contains a table 'Sales' with columns: StoreID, ProductID, SaleDate, Quantity, and Amount. The database also has a 'Stores' table with StoreID and StoreName, and a 'Products' table with ProductID, ProductName, and Category. You need to create a data model that supports filtering by store, product category, and date, and also allows calculation of year-over-year sales growth. You want to minimize the model size and ensure optimal performance. The data volume is large (millions of rows). You must design the data model. What should you do?

Accepted Answer

Import Sales, Stores, and Products tables, create a separate date table using CALENDAR, and establish relationships between Sales and dimension tables.. It follows the star schema best practice: importing dimension tables (Stores, Products, a dedicated Date table) and the fact table (Sales) separately, then creating relationships. This minimizes model size by avoiding data duplication and enables efficient filtering by store, product category, and date. The separate date table is essential for accurate year-over-year calculations using DAX time intelligence functions like SAMEPERIODLASTYEAR, which require a continuous date range.

Answer

Import all tables as they are and create a single flat table by merging Sales, Stores, and Products in Power Query.

Answer

Import Sales table only and create calculated columns for StoreName and ProductName using RELATED.

Answer

Import Sales table and use the auto date/time feature for time intelligence.

Question 8

You are a Power BI developer for a financial services company. You are preparing data from multiple sources: a CSV file containing daily stock prices (ticker, date, close_price), a SQL Server database with company information (ticker, company_name, sector), and an Excel file with quarterly earnings data (ticker, quarter, earnings_per_share). The CSV file has 5 years of daily data (approx 1.3 million rows). The SQL Server table has 5000 rows. The Excel file has 20,000 rows. You need to create a data model that allows users to filter by sector, company, and date range, and to calculate moving averages of stock prices and compare earnings over time. Performance is critical. You must decide the best approach to combine and model this data. What should you do?

Accepted Answer

Import all tables, create a date table with CALENDAR, and establish relationships: StockPrices[Date] -> DateTable[Date], StockPrices[Ticker] -> Company[Ticker], Earnings[Ticker] -> Company[Ticker], and create a many-to-many relationship between Earnings and DateTable using a bridge table.. Importing all tables into the in-memory VertiPaq engine ensures optimal performance for large datasets (1.3M rows) and complex calculations like moving averages. Creating a separate date table with CALENDAR enables proper time intelligence, while the bridge table resolves the many-to-many relationship between quarterly earnings and daily dates, allowing accurate filtering by sector, company, and date range without performance degradation.

Answer

Use DirectQuery for StockPrices (CSV) and import the other tables.

Answer

Import only StockPrices and Company, and use the auto date/time feature; ignore Earnings data.

Answer

Import all tables, then in Power Query merge StockPrices with Company and Earnings into a single flat table using left outer joins.

Question 9

You are creating a Power BI dataset from a SQL Server data warehouse. The warehouse contains a fact table with 500 million rows and dimension tables. You need to minimize the data refresh time while ensuring that the dataset meets the reporting requirements. Which approach should you recommend?

Accepted Answer

Use Import mode but filter rows and reduce columns in Power Query to only those needed.. Importing only the necessary columns and rows reduces the data volume, which directly minimizes refresh time. With 500 million rows, Import mode is generally faster than DirectQuery for large fact tables in Power BI, as it avoids querying the source on every interaction. Filtering and column reduction in Power Query ensures the dataset remains lean while meeting reporting requirements.

Answer

Create a composite model using DirectQuery for the fact table and Import for dimensions.

Answer

Use DirectQuery mode for the dataset.

Answer

Configure incremental refresh on the fact table.

Question 10

You are transforming data in Power Query. A column named 'SalesAmount' contains values as text with a dollar sign and thousands separator, e.g., "$1,234.56". You need to convert this column to a decimal number for analysis. What is the most efficient sequence of transformations?

Accepted Answer

Use Replace Values to remove '$' and ',', then change data type to Decimal Number.. It explicitly removes both the dollar sign and the comma using Replace Values before changing the data type to Decimal Number, ensuring proper conversion without errors. Option A is inefficient; splitting the column is unnecessary when simple replacements work. Option B would fail because Power Query cannot automatically parse currency symbols and thousands separators from text when changing data type directly. Option D appears similar but specifies 'Decimal' instead of 'Decimal Number', which is not a valid data type in Power Query, leading to an error or incorrect result.

Answer

Split the column by delimiter and keep the numeric part, then change data type.

Answer

Change data type to Decimal Number directly; Power Query will automatically clean the values.

Answer

Use Replace Values to remove '$' and ',' then change data type to Decimal.

Question 11

You are merging two queries in Power Query. Query 'Orders' contains columns: OrderID, CustomerID, OrderDate. Query 'Customers' contains columns: CustomerID, CustomerName, Segment. You need to add the CustomerName to the Orders query. The relationship between Orders and Customers is many-to-one. Which join kind should you use?

Accepted Answer

Left Outer. The goal is to retain all rows from the Orders table while adding CustomerName from the Customers table. A Left Outer join returns all rows from the first (left) table and only matching rows from the second (right) table, filling non-matches with null. Since the relationship is many-to-one, each OrderID may have a matching CustomerID, and you want to keep every order even if a customer is missing — exactly what Left Outer does.

Answer

Inner

Answer

Right Outer

Answer

Full Outer

Question 12

You have a Power BI dataset that uses Import mode and refreshes daily. The source data includes a column 'LastModifiedDate'. You want to reduce the amount of data loaded during each refresh by only loading rows that have changed since the last refresh. Which feature should you configure?

Accepted Answer

Configure incremental refresh on the table using the 'LastModifiedDate' column.. Incremental refresh in Power BI allows you to filter data by a date/time column (such as 'LastModifiedDate') so that only rows that have changed since the last refresh are loaded. This reduces refresh time and data volume while still using Import mode. The feature requires a date/time column and a properly configured policy in the Power Query Editor.

Answer

Enable query folding in Power Query.

Answer

Use the 'Reduce data' option in Power Query Editor.

Answer

Change the storage mode to DirectQuery.

Question 13

You are connecting to a SharePoint folder that contains Excel workbooks. Each workbook has multiple sheets. You need to combine data from a specific sheet named 'Sales' across all workbooks. Which Power Query approach should you use?

Accepted Answer

Use the SharePoint folder connector, filter by .xlsx, then expand the Content column and filter by sheet name 'Sales'.. The SharePoint folder connector retrieves all files in the folder, including Excel workbooks. By filtering for .xlsx files and then expanding the Content column, you access the binary data of each workbook. You can then filter by the 'Sales' sheet name to combine data from that specific sheet across all workbooks, which is the only approach that directly handles multiple workbooks with multiple sheets.

Answer

Use the SharePoint Online List connector and select the document library.

Answer

Use the Excel connector and specify the folder path.

Answer

Use the Web connector and provide the SharePoint site URL.

Question 14

You are reviewing a Power Query query that loads data from a SQL Server database. The query includes multiple steps that perform data transformation. You want to ensure that the query is optimized by pushing as many transformations as possible to the SQL Server. What should you look for?

Accepted Answer

Check for the 'Query Folding' indicators in the query editor.. Query folding indicators in Power Query Editor show whether transformations are being pushed to the SQL Server source. When a step shows a 'folded' icon (a table with a down arrow), it means the transformation is translated into native SQL and executed on the server, reducing data transfer and improving performance. Checking these indicators directly confirms which steps are folded and which are not, allowing you to optimize the query by reordering or rewriting steps to maximize folding.

Option A is incorrect because viewing the native SQL query only shows the final aggregated SQL, not the folding status of each individual step. Option B is also incorrect as the Performance Analyzer helps measure overall report performance but does not indicate folding for each step. Option D is irrelevant because data previews do not show folding status.

Answer

View the native SQL query generated by Power Query.

Answer

Use the Performance Analyzer in Power BI Desktop.

Answer

Review the data preview for each step.

Question 15

You are preparing data from an Azure SQL Database. You need to ensure that sensitive columns (e.g., Social Security Numbers) are obfuscated in Power BI reports. Which TWO of the following approaches can you use? (Choose two.)

Accepted Answer

Configure dynamic data masking on the Azure SQL Database.. Azure SQL Database Dynamic Data Masking (DDM) obfuscates sensitive data at the database query level, so when Power BI connects to the database, the masked values are automatically returned for unauthorized users. This is a server-side approach that does not require changes to the Power BI report or data model.

Answer

Use row-level security (RLS) in Power BI to hide sensitive columns.

Answer

Use Microsoft Purview sensitivity labels to mask data.

Answer

Apply column-level security in Power BI Desktop.