PL-300 Practice Test 11 — 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?

Accepted Answer

In the model view, mark the table as a date table by selecting the date column.. Option C is correct because 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 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 3

You need to create a Power BI data model that includes a date dimension. The source data contains a table with a Date column covering 2015-2025. You want to ensure that all dates in the model have a contiguous range for time intelligence. What should you do?

Accepted Answer

Create a calculated column using CALENDAR to generate a continuous date range and use that.. Option C is correct because the CALENDAR function generates a contiguous range of dates, which is essential for accurate time intelligence calculations (e.g., YTD, QoQ). By creating a calculated column with CALENDAR, you ensure no gaps exist in the date dimension, even if the source data has missing dates. This approach allows you to mark the table as a date table and enable DAX time intelligence functions like TOTALYTD or SAMEPERIODLASTYEAR.

Answer

Hide the existing Date column and rely on auto date/time.

Answer

Add a calculated column for year and month from the existing Date column.

Answer

Use the existing Date column as the date table and mark it as a date table.

Question 4

Which TWO of the following are valid options when connecting to an on-premises SQL Server database from Power BI service? (Select TWO.)

Accepted Answer

DirectQuery mode with an on-premises data gateway. Option A is correct because DirectQuery mode in Power BI service requires an on-premises data gateway to translate cloud-based queries into native SQL queries against the on-premises SQL Server. The gateway acts as a secure bridge, enabling real-time query execution without moving data to the cloud.

Answer

Using a SharePoint Online list connector pointing to SQL Server

Answer

DirectQuery using cloud credentials without a gateway

Answer

Scheduling refresh in Power BI Desktop

Question 5

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.. Option A is correct because you need to change the data type to a numeric type in Power Query Editor to perform aggregations. Option B is wrong because splitting the column does not change the data type. Option C is wrong because creating a calculated column in DAX is less efficient and may still require type conversion. Option D is wrong because the relationship is not the issue.

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 6

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'.. Option B is correct because 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 7

You are preparing data for a Power BI report. The source data contains a column 'FullName' with values like 'John Doe'. You need to split this column into 'FirstName' and 'LastName' using Power Query. The transformation should be repeatable and not dependent on the number of spaces. What is the best approach?

Accepted Answer

Use 'Split Column by Delimiter' and choose 'Right-most delimiter'.. Option B is correct. Splitting by delimiter (space) at the rightmost occurrence reliably separates last name from first name. Option A is wrong because splitting by position is not reliable. Option C is wrong because Extract after delimiter picks the wrong part. Option D is wrong because Replace values doesn't split.

Answer

Use 'Split Column by Number of Characters' with a fixed position.

Answer

Use 'Replace Values' to replace space with a comma.

Answer

Use 'Extract Text After Delimiter' with a space.

Question 8

Refer to the exhibit. You are configuring a scheduled refresh for a Power BI dataset. The exhibit shows the refresh schedule settings. The dataset is in a workspace in a Premium capacity. The scheduled refresh runs at 5:00 AM UTC daily. However, the refresh is failing consistently. What is the most likely cause?

Accepted Answer

The data source requires a gateway to be configured for refresh.. The exhibit shows a scheduled refresh configured for a dataset in a Premium capacity workspace, but the refresh is failing consistently. The most likely cause is that the data source requires a gateway to be configured for refresh. Even in Premium capacities, if the data source is on-premises or within a private network (e.g., SQL Server, Oracle, or SharePoint on-premises), an on-premises data gateway must be installed and configured to enable the Power BI service to connect and refresh the data. Without a gateway, the scheduled refresh will fail because the cloud service cannot directly access the local data source.

Answer

The notify option should be set to 'OnFailureOrSuccess' to get alerts.

Answer

The refresh time is outside the allowed window for Premium capacities.

Answer

The dataset is in a Premium capacity, which does not support scheduled refresh.

Question 9

You are designing a Power BI solution that ingests data from multiple sources: Azure Blob Storage, Salesforce, and an on-premises Oracle database. The data must be combined into a single semantic model. The Oracle database contains sensitive customer information that must be masked before being loaded. Which approach should you use to prepare the data?

Accepted Answer

Create a Power BI dataflow that extracts, transforms, and masks data before loading into the semantic model. Option A is correct because Power BI dataflows provide a cloud-based ETL solution that can connect to Azure Blob Storage, Salesforce, and on-premises Oracle (via an on-premises data gateway), perform transformations including data masking, and then load the prepared data into a shared semantic model. This approach centralizes data preparation, ensures sensitive data is masked before any downstream consumption, and supports scheduled refreshes without requiring additional infrastructure.

Answer

Use DirectQuery to connect to all sources and rely on database-level masking

Answer

Import all data into Power BI Desktop and apply transformations in the Power Query Editor

Answer

Stage the data in an Azure SQL Database and use SQL Server Analysis Services to mask data

Question 10

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 11

You are reviewing the M query snippet above. The query imports an Excel file and filters rows where Date is on or after January 1, 2024. However, the query fails with an error. What is the most likely cause?

Accepted Answer

The file path is local and may not be accessible after deployment.. Option B is correct because the file path is hardcoded to a local drive, which may not exist on the Power BI Service or another machine. Option A is wrong because the date filter syntax is correct. Option C is wrong because the Excel.Workbook function handles .xlsx files. Option D is wrong because the data type transformations are correct.

Answer

The data type conversion cannot handle null values.

Answer

The Excel.Workbook function cannot read .xlsx files.

Answer

The date filter syntax is incorrect.

Question 12

You are preparing data for a Power BI report. The source data has a column 'OrderDate' with date values. You need to create a date hierarchy for drill-down (Year, Quarter, Month). Which TWO actions are required?

Accepted Answer

Create a separate date table with a continuous range of dates.. Options B and D are correct. You need to mark a date table as a date table to enable time intelligence and create a date hierarchy. Creating a date table with a continuous range ensures all periods are represented. Option A is wrong because auto date/time creates hidden hierarchies but may not be sufficient for custom requirements. Option C is wrong because calculated columns do not create a hierarchy. Option E is wrong because sorting by month number is useful but not required for a hierarchy.

Answer

Enable auto date/time in Power BI Desktop.

Answer

Sort the Month column by MonthNumber to ensure proper order.

Answer

Add calculated columns for Year, Quarter, and Month to the date table.

Question 13

You are importing a large dataset from a CSV file using Power Query. The file contains 50 columns, but you only need 10 for your report. What is the most efficient way to reduce the amount of data loaded into the model?

Accepted Answer

Remove the unnecessary columns in Power Query before loading.. Option A is correct because Power Query processes data before it enters the Power BI model. Removing unnecessary columns at the query stage reduces the amount of data loaded into memory, improving performance and reducing storage. This is the most efficient approach as it minimizes the dataset size from the start, unlike post-load methods that still consume resources.

Answer

Load all columns and then hide the unnecessary ones in the report.

Answer

Use a SQL query to select only the needed columns if the data source supports it.

Answer

Create a DAX calculated table that selects only the needed columns.

Question 14

You have a Power BI semantic model that uses DirectQuery to an Azure Synapse Analytics dedicated SQL pool. The model is used by a real-time dashboard. Users report that the dashboard is slow. You need to improve query performance without changing the source system. Which action should you take?

Accepted Answer

Reduce the number of visuals on the dashboard and apply page-level filters. Option B is correct because reducing the number of visuals and applying page-level filters directly reduces the number of queries sent to the Azure Synapse Analytics dedicated SQL pool via DirectQuery. Since the source system cannot be changed, the only way to improve performance is to minimize the query load from the dashboard. Page-level filters ensure that only relevant data is queried, and fewer visuals mean fewer separate queries, which collectively reduces latency.

Answer

Create aggregations on the fact table

Answer

Enable dual storage mode for all tables

Answer

Disable the 'Reduce queries' option in Power BI Desktop

Question 15

You are connecting to an Azure SQL Database from Power BI Desktop. The database contains a view that returns thousands of rows. You only need the last 100 rows for analysis. What is the most efficient way to reduce the data loaded?

Accepted Answer

Use the 'Keep Top Rows' transformation in Power Query after applying a sort. Option B is correct because using the 'Top N' filter in Power Query pushes the filter to the source if supported, reducing data transfer. Option A is wrong because it loads all rows. Option C is wrong because DirectQuery does not reduce the load; it queries the source each time. Option D is wrong because native SQL query may still load all rows unless filtered.

Answer

Write a native SQL query with a WHERE clause to limit rows

Answer

Use DirectQuery mode and add a filter in the report

Answer

Import all rows and then remove rows in Power Query