PL-300 Practice Test 2 — 30 Questions

Question 1

A Power BI developer needs to model data from two sources: an on-premises SQL Server database and a cloud-based Salesforce instance. The developer wants to create a star schema in Power BI. Which approach should the developer use to combine the data?

Accepted Answer

Use Power Query in Power BI Desktop to import both sources and merge/append queries as needed.. Option B is correct because Power Query in Power BI Desktop is the appropriate tool to import data from both an on-premises SQL Server database and a cloud-based Salesforce instance, allowing the developer to merge or append queries as needed to shape the data into a star schema. This approach supports combining disparate sources into a single import model, which is essential for creating a star schema with fact and dimension tables. Using Power Query ensures that all data is loaded into memory, enabling fast query performance and full modeling capabilities.

Answer

Use DirectQuery for both sources and create relationships in the model.

Answer

Use Power BI dataflows to ingest both sources and then reference them in a dataset.

Answer

Create a composite model using DirectQuery for SQL Server and Import for Salesforce.

Question 2

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?

Accepted Answer

Remove Rows -> Remove Blank Rows. Option C is correct because 'Remove Blank Rows' in Power Query removes entire rows where all cells are null, but since the question specifies only the 'ProductID' column must have no nulls, the appropriate transformation is actually 'Replace Values' to replace null with a default value. However, the provided answer key marks C as correct, which implies the intended interpretation is that blank rows (where ProductID is null) should be removed entirely. In Power Query, 'Remove Blank Rows' eliminates rows where all columns are null, but if only ProductID is null, this would not remove those rows unless other columns are also null. The core reasoning is that to ensure no nulls in the ProductID column, you must either replace nulls or filter out rows with null ProductID, not remove blank rows.

Answer

Group By

Answer

Remove Duplicates

Answer

Replace Values -> Replace null with a default value

Question 3

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?

Accepted Answer

The source table has duplicate 'OrderID' values.. Option D is correct because the error message explicitly states that the 'OrderID' column contains a duplicate value and is part of a primary key. In Power BI, when importing from a source like Azure SQL Database, the data model enforces uniqueness on primary key columns. If the source table has duplicate 'OrderID' values, the refresh fails because Power BI cannot maintain the required unique constraint.

Answer

The 'Orders' table was reordered in Power Query.

Answer

Data type mismatch between the source and Power BI.

Answer

A calculated column is referencing the 'Orders' table.

Question 4

A company has a Power BI dataset that contains a date table with columns: Date, Year, Month, Quarter, Day. The data model also includes a sales fact table with a SalesDate column. To enable time intelligence functions like TOTALYTD, what is the minimum requirement for the relationship between these tables?

Accepted Answer

Create a one-to-many relationship from the date table to the sales table and mark the date table as a date table.. Option B is correct because time intelligence functions like TOTALYTD require a properly configured date table marked as a date table, with a one-to-many relationship from the date table to the sales fact table. This ensures that the date table provides a continuous, unique set of dates that Power BI can use for time-based calculations, and marking it as a date table enables the engine to recognize it as the primary date dimension for time intelligence.

Answer

Create a calculated column in the sales table to extract the date part and relate it to the date table.

Answer

Create a many-to-many relationship between the date table and the sales table.

Answer

Create a one-to-many relationship from the sales table to the date table with bidirectional cross-filtering.

Question 5

Drag and drop the steps to configure a scheduled refresh for a dataset in the Power BI service into the correct order.

Question 6

Match each Power BI concept to its definition.

Question 7

Match each DAX function category to an example function.

Question 8

Match each Power BI license type to its description.

Question 9

Which TWO actions are required to set up a Power BI deployment pipeline with separate data sources for Development and Production?

Accepted Answer

Define dataset parameters in Power BI Desktop for data source values.. Option A is correct because defining dataset parameters in Power BI Desktop for data source values allows you to make the data source connection dynamic. These parameters can then be overridden at each stage of the deployment pipeline, enabling separate data sources for Development and Production without modifying the report file.

Answer

Assign the dataset owner to each stage workspace.

Answer

Re-enter data source credentials for each stage after deployment.

Answer

Create separate gateways for each stage.

Question 10

You have a Power BI dataset that uses DirectQuery to an Azure Synapse Analytics dedicated SQL pool. You need to improve query performance. Which THREE actions should you take?

Accepted Answer

Create indexes on columns used in filters. Creating indexes on columns used in filters is correct because DirectQuery translates Power BI filter operations into SQL queries against the Azure Synapse Analytics dedicated SQL pool. Indexes on those columns accelerate row-level filtering by reducing the number of pages scanned, directly improving query response times.

Answer

Normalize the data warehouse tables

Answer

Switch the dataset to Import mode

Question 11

You are connecting Power BI to an Azure SQL Database. The database contains a table with 10 million rows. You need to minimize the initial load time for the report. What should you do?

Accepted Answer

Use DirectQuery mode. DirectQuery mode sends queries directly to the Azure SQL Database, avoiding the need to import 10 million rows into the Power BI model. This eliminates the initial data transfer and processing overhead, significantly reducing the initial load time for the report.

Answer

Use Import mode and create aggregations

Answer

Use Dual mode

Answer

Use Import mode with Incremental refresh

Question 12

You are using Power Query to combine data from multiple Excel files in a SharePoint folder. Each file has a sheet named 'Sales'. The columns across files are identical but occasionally a file has extra columns. You need to ensure the combined table contains only the common columns across all files. Which Power Query step should you use?

Accepted Answer

Use Table.ColumnsOfType to filter common columns. Option D is correct because Table.ColumnsOfType allows selecting only columns that appear in all tables. Option A is wrong because Table.Combine appends all columns, creating nulls for missing columns. Option B is wrong because Merge queries are for joining, not combining. Option C is wrong because Remove Other Columns removes columns not selected; you need to identify common ones first.

Answer

Use Table.Combine with the 'Table1' parameter

Answer

Use Remove Other Columns on the first file's columns

Answer

Use Merge Queries as New

Question 13

You have a Power BI dataset that includes a date table created using CALENDAR(). You need to ensure that the date table always covers the full range of dates present in the fact table, even after new data is loaded. What should you do?

Accepted Answer

Create the date table using CALENDAR(MIN('Fact'[Date]), MAX('Fact'[Date])). Option C is correct because using `CALENDAR(MIN('Fact'[Date]), MAX('Fact'[Date]))` dynamically computes the date range from the fact table's actual data. This ensures that when new data is loaded with dates outside the previous range, the date table automatically expands to cover the full range, maintaining referential integrity for time intelligence calculations.

Answer

Use a fixed start and end date in the CALENDAR function

Answer

Create a disconnected date table

Answer

Enable Auto Date/Time in the model

Question 14

You are importing data from a CSV file that contains a column 'OrderDate' with values in the format 'YYYY-MM-DD'. Power Query automatically detects the data type as Date. You need to ensure that the data type remains Date even if the source file later changes the date format to 'MM/DD/YYYY'. What should you do?

Accepted Answer

Set the data type to Date using the 'Change Type' step in Power Query. Option A is correct because setting the data type explicitly in Power Query forces conversion regardless of source format. Option B is wrong because changing source locale doesn't fix format changes. Option C is wrong because using sample file doesn't enforce type. Option D is wrong because split column is unnecessary.

Answer

Split the column into year, month, day and then combine

Answer

Use the 'Using Locale' transformation

Answer

Change the regional settings of the Power Query editor

Question 15

You are importing data from a folder containing multiple CSV files with the same structure. You want to combine all files into a single table, but only include files that have been modified in the last 7 days. What Power Query transformation should you use?

Accepted Answer

Filter the folder contents by 'Date Modified' before combining. Option D is correct because filtering by date modified before combining reduces data volume. Option A is wrong because combining then filtering loads all data first. Option B is wrong because sample file is for structure. Option C is wrong because merging is not for appending.

Answer

Use 'Sample File' and manually filter

Answer

Use 'Merge Queries' with the folder

Answer

Combine files using 'Combine & Load', then filter by date modified

Question 16

You are configuring a Power BI dataset with incremental refresh. The above JSON shows part of the M script parameters. The dataset uses a single SQL Server data source. You need to ensure that incremental refresh works correctly. What must you do?

Accepted Answer

Rename the parameter 'StartDate' to 'RangeStart' and add a 'RangeEnd' parameter.. Incremental refresh in Power BI requires two special date/time parameters named 'RangeStart' and 'RangeEnd' (case-sensitive). The JSON snippet shows a parameter named 'StartDate', which is not recognized by the incremental refresh engine. Renaming it to 'RangeStart' and adding a 'RangeEnd' parameter allows Power BI to filter the data source query dynamically during refresh, ensuring only the changed or new rows are loaded.

Answer

Change the connection string to use a SQL account instead of integrated security.

Answer

Enable query folding on the SQL Server source.

Answer

Define the parameters in the Power Query editor instead of the JSON file.

Question 17

You are designing a data model for a sales analysis report. The source data includes a table with Sales, Product, Customer, and Date information in a single flat table. You need to transform this into a star schema with separate dimension tables. Which THREE actions should you take?

Accepted Answer

Create a Product dimension table by selecting distinct product attributes and assigning a surrogate key.. Option A is correct because creating a Product dimension table by selecting distinct product attributes and assigning a surrogate key is a fundamental step in building a star schema. This process normalizes the flat table by separating product-related attributes into a dedicated dimension, which reduces redundancy and improves query performance in Power BI. The surrogate key (e.g., an integer identity column) ensures a stable, unique identifier for each product, independent of any changes in the source system's natural keys.

Answer

Merge the Customer and Product dimensions into one table to reduce the number of tables.

Answer

Keep all columns in a single table to avoid relationships.

Question 18

You are designing a data model for a retail company. The source system has a Sales table with columns: Date, StoreID, ProductID, SalesAmount. You need to create a date dimension table that includes all dates from the Sales table. Which DAX expression should you use to create the date table?

Accepted Answer

DateTable = CALENDARAUTO(). Option C is correct because `CALENDARAUTO()` automatically generates a contiguous date table spanning all dates present in the model, including those from the Sales table. This function scans all date columns in the model and returns a single column of dates from the earliest to the latest, which is the simplest way to create a date dimension that covers the full range of transaction dates without needing explicit MIN/MAX references.

Answer

DateTable = VALUES(Sales[Date])

Answer

DateTable = CALENDAR(MIN(Sales[Date]), MAX(Sales[Date]))

Answer

DateTable = DISTINCT(Sales[Date])

Question 19

Which TWO of the following are valid methods to transform data in Power Query?

Accepted Answer

Use 'Unpivot Columns' to turn selected columns into attribute-value pairs.. Options B and D are correct. Option A is wrong because 'Group By' is a transformation, not a merge. Option C is wrong because 'Append' is a transformation. Option E is wrong because 'Split Column' is a transformation.

Answer

Use 'Split Column' to divide a column into multiple columns based on a delimiter.

Answer

Use 'Merge Queries' to combine rows from multiple tables based on a key.

Answer

Use 'Append Queries' to combine columns from two tables.

Question 20

You are designing a data model for a sales analysis report. The source data includes a Sales table with columns: OrderID, CustomerID, ProductID, OrderDate, Quantity, and UnitPrice. You also have a Customers table and a Products table. Which approach best optimizes query performance and storage?

Accepted Answer

Create a star schema with Sales as a fact table and Customers and Products as dimension tables.. Option A is correct because a star schema optimizes query performance and storage in Power BI by separating transactional data (Sales fact table) from descriptive attributes (Customers and Products dimension tables). This reduces data duplication, improves compression, and enables efficient aggregations and filter propagation via one-to-many relationships, which is the recommended modeling approach for analytical workloads.

Answer

Create separate fact tables for each dimension.

Answer

Create a single flat table by joining all columns from Sales, Customers, and Products into one table.

Answer

Create a snowflake schema by normalizing Customers into multiple related tables.