PL-300 Practice Test 3 — 25 Questions

Question 1

You are modeling data from multiple sources: a SQL Server database for sales, an Excel file for budget, and a SharePoint list for product targets. You need to combine these into a single Power BI report. What is the recommended approach for handling data refresh?

Accepted Answer

Use Power Query to combine data from all sources into a single dataset, then schedule a daily refresh in the Power BI service.. Option C is correct because Power Query (Get Data) in Power BI Desktop is designed to connect to and combine data from multiple heterogeneous sources—SQL Server, Excel, and SharePoint—into a single dataset. After publishing to the Power BI service, you can configure a scheduled refresh (via an on-premises data gateway for on-premises sources) to keep the dataset up to date automatically, which is the recommended approach for recurring refreshes.

Answer

Import each source into separate Power BI Desktop files and manually update.

Answer

Use Excel Online as the single source and import all data into it first.

Answer

Create separate datasets for each source and use composite models with DirectQuery.

Question 2

Which TWO DAX functions can be used to filter data in a measure?

Accepted Answer

FILTER. Option D (FILTER) is correct because it is a DAX function specifically designed to return a filtered subset of a table based on a logical condition, making it a primary tool for row-level filtering within measures. Option E (CALCULATE) is correct because it modifies the filter context of a measure by applying or removing filters, effectively filtering data through its filter arguments. Both functions directly control which rows are included in calculations.

Answer

VALUES

Answer

SUM

Answer

ALL

Question 3

You have a Power BI data model with a fact table and multiple dimension tables. You notice that many-to-many relationships cause ambiguous results. What is the best practice to resolve this?

Accepted Answer

Add a bridge table with appropriate relationships. In Power BI, many-to-many relationships between fact and dimension tables can produce ambiguous results because the model cannot determine a unique filter propagation path. The best practice is to introduce a bridge table that resolves the many-to-many relationship into two one-to-many relationships, ensuring unambiguous filter context and correct aggregations.

Answer

Change the relationship to one-to-one

Answer

Use a bidirectional cross-filter direction

Answer

Create a calculated table to merge the dimensions

Question 4

Which TWO of the following are valid methods to handle slowly changing dimensions (SCD) Type 2 in Power Query? (Select two.)

Accepted Answer

Use a lookup table that tracks changes with start and end dates. Option C is correct because in Power Query, a common method to implement SCD Type 2 is to use a lookup table that tracks changes with start and end dates. This allows you to maintain historical versions of dimension attributes by recording the effective date range for each row, enabling accurate time-based analysis.

Answer

Merge the dimension table with a fact table

Answer

Update the existing row with new attributes

Answer

Overwrite the existing attributes

Question 5

You have a Power BI data model with a table named Sales that includes columns: ProductID, SalesAmount, and SalesDate. You need to create a measure that calculates the total sales amount for the current quarter and compares it to the previous quarter. Which THREE DAX functions should you use?

Accepted Answer

PREVIOUSQUARTER. A is correct because PREVIOUSQUARTER is a time intelligence function that returns a table of dates for the previous quarter relative to the current filter context. When used inside CALCULATE, it shifts the evaluation of the SUM(Sales[SalesAmount]) to the prior quarter, enabling direct comparison with the current quarter's total.

Answer

DATEADD

Answer

DATESQTD

Question 6

You have a Power BI model with a table 'Sales' and a table 'Date'. The relationship between them is many-to-one (Sales[OrderDate] -> Date[Date]). You notice that when you filter by Date[Year], some sales rows are not included. What is the most likely cause?

Accepted Answer

The relationship is inactive.. Option B is correct because an inactive relationship does not automatically propagate filters between tables. Even though the relationship is defined, it must be explicitly activated using a DAX function like USERELATIONSHIP for filters to flow from Date to Sales. Without that, filtering by Date[Year] will not restrict Sales rows, causing some rows to appear missing if the user expects the filter to apply.

Answer

The relationship has Referential Integrity enforced.

Answer

The cross filter direction is set to Single.

Answer

The Date table has blank rows.

Question 7

Which THREE of the following are benefits of using a calculated column over a measure in Power BI?

Accepted Answer

They can be referenced by other calculated columns.. Option A is correct because calculated columns are evaluated row by row during data refresh and stored in the model, making them available for use by other calculated columns through direct references. Measures, in contrast, are evaluated dynamically at query time and cannot be referenced by other measures in the same way. This allows calculated columns to serve as building blocks for further calculations within the data model.

Answer

They use less memory than measures.

Answer

They are more efficient for large datasets than measures.

Question 8

You have a fact table 'Orders' with columns: OrderID, CustomerID, OrderDate, SalesAmount. You also have a 'Calendar' table with Date, Year, Month, Quarter. The Calendar table is marked as a date table. You want to calculate total sales for the current month. Which DAX measure should you use?

Accepted Answer

TOTALMTD(SUM(Orders[SalesAmount]), Calendar[Date]). Option D is correct because TOTALMTD is a dedicated time intelligence function that calculates the sum of SalesAmount for the current month-to-date, using the Calendar[Date] column as the date reference. Since the Calendar table is marked as a date table, TOTALMTD automatically respects the current filter context (e.g., from a slicer or report page) to return only the month-to-date total.

Answer

TOTALYTD(SUM(Orders[SalesAmount]), Calendar[Date])

Answer

SUM(Orders[SalesAmount])

Answer

CALCULATE(SUM(Orders[SalesAmount]), DATESMTD(Calendar[Date]))

Question 9

You are designing a star schema for a sales analysis report. The source data includes Order Details, Products, Customers, and Dates. Which table should be the fact table?

Accepted Answer

Order Details. In a star schema, the fact table stores quantitative, measurable data (metrics) and foreign keys linking to dimension tables. Order Details contains sales transactions with measures like quantity and revenue, making it the correct fact table for sales analysis.

Answer

Dates

Answer

Customers

Answer

Products

Question 10

You are designing a data model for a report that shows sales by region and product category. The source data includes a table 'Sales' with columns: Region, Category, SalesAmount. You also have separate tables 'Regions' and 'Categories' that contain additional attributes. You need to create a star schema. What should you do with the 'Region' and 'Category' columns in the 'Sales' table?

Accepted Answer

Remove them from the Sales table and use foreign keys to link to the dimension tables. In a star schema, dimension tables (Regions, Categories) contain descriptive attributes, and the fact table (Sales) stores foreign keys referencing those dimensions. Removing the Region and Category columns from the Sales table and replacing them with foreign keys (e.g., RegionID, CategoryID) normalizes the model, reduces data redundancy, and enables efficient filtering and slicing by region and category attributes. This approach aligns with best practices for Power BI data modeling, ensuring optimal query performance and maintainability.

Answer

Keep them in the Sales table as attributes for simplicity

Answer

Merge the Regions and Categories tables into the Sales table

Answer

Mark the Sales table as a date table

Question 11

You are designing a data model in Power BI that includes a fact table called 'Sales' and dimension tables 'Customer', 'Product', and 'Date'. The 'Sales' table contains columns: 'SalesID', 'CustomerID', 'ProductID', 'DateKey', 'Quantity', and 'Amount'. You need to ensure that the model follows star schema best practices and that filters from the 'Customer' table propagate correctly to the 'Sales' table. What should you do?

Accepted Answer

Create a one-to-many relationship from Customer (one side) to Sales (many side) based on CustomerID.. Option D is correct because in a star schema, the dimension table (Customer) should have a one-to-many relationship to the fact table (Sales) based on the common key (CustomerID). This ensures that filters applied to the Customer table propagate correctly to the Sales table, maintaining referential integrity and enabling efficient query performance.

Answer

Merge the Customer and Sales tables into a single flat table.

Answer

Set the cross-filter direction to Both on the relationship between Customer and Sales.

Answer

Create a many-to-many relationship between Customer and Sales using SalesID.

Question 12

Which TWO of the following are true about the Power BI composite model?

Accepted Answer

A composite model can combine DirectQuery and Import tables.. Option C is correct because a composite model in Power BI allows mixing DirectQuery and Import tables within the same data model. This enables you to leverage the performance of in-memory Import storage for some tables while using DirectQuery to access large or real-time data sources without duplicating data.

Answer

Composite models do not support many-to-many relationships.

Answer

All tables in a composite model must use the same storage mode.

Answer

Calculated tables are not supported in composite models.

Question 13

Refer to the exhibit. You are implementing row-level security (RLS) in Power BI. The JSON policy above is applied to the 'Sales' table. The user is in the 'SalesRegion' role. Which rows will the user see?

Accepted Answer

Rows where Region is 'North' only.. Option C is correct because the JSON policy defines a filter that restricts the 'Sales' table to rows where the 'Region' column equals 'North'. The user is in the 'SalesRegion' role, which applies this filter. RLS in Power BI evaluates the filter expression for each row, returning only those that satisfy the condition, so only rows with Region = 'North' are visible.

Answer

Rows where Region is 'North' and 'South'.

Answer

No rows, because there is a conflict.

Answer

Rows where Region is 'South' only.

Question 14

You have a table with columns 'Product', 'Category', and 'Sales'. You want to create a hierarchy that allows users to drill down from Category to Product. Which is the correct order to create the hierarchy?

Accepted Answer

Category -> Product. In Power BI, a hierarchy is built from the highest (most aggregated) level to the lowest (most granular) level. To allow drill-down from Category to Product, Category must be the top level and Product the child level. Option C correctly orders Category → Product, enabling users to expand from category-level totals to individual product sales.

Answer

Product -> Category -> Sales

Answer

Product -> Category

Answer

Sales -> Product -> Category

Question 15

You have a Power BI model with a table 'Sales' and a related 'Product' table. You want to count the number of distinct products sold. Which DAX expression should you use?

Accepted Answer

DISTINCTCOUNT(Sales[ProductID]). Option C is correct because DISTINCTCOUNT(Sales[ProductID]) returns the number of unique ProductID values in the Sales table, which directly answers the requirement to count distinct products sold. This function counts each distinct value in the specified column, ignoring duplicates, and is the standard DAX measure for distinct count calculations.

Answer

COUNTROWS(Sales)

Answer

COUNT(Sales[ProductID])

Answer

COUNTA(Sales[ProductID])

Question 16

You need to create a calculated column that categorizes sales amounts into 'Low', 'Medium', and 'High' based on thresholds. Which DAX function should you use?

Accepted Answer

SWITCH. The SWITCH function is the correct choice because it evaluates an expression against a list of values and returns the corresponding result, making it ideal for categorizing sales amounts into 'Low', 'Medium', and 'High' based on thresholds. Unlike nested IF statements, SWITCH is more readable and efficient for multiple conditions, as it allows you to define ranges using logical operators within the expression parameter.

Answer

LOOKUPVALUE

Answer

CALCULATE

Answer

IF

Question 17

You have a Power BI data model with a table 'Orders' that has columns: 'OrderID', 'OrderDate', 'CustomerName', 'Region', 'Product', 'Quantity', 'UnitPrice'. You want to create a measure that calculates total sales amount. Which DAX expression should you use?

Accepted Answer

Total Sales = SUMX(Orders, Orders[Quantity] * Orders[UnitPrice]). Option D is correct because SUMX is an iterator function that evaluates the expression `Orders[Quantity] * Orders[UnitPrice]` for each row in the Orders table and then sums the results. This is necessary because DAX does not support direct multiplication of two columns inside SUM; SUM expects a single column reference, not an expression. SUMX performs row-by-row evaluation, which correctly computes the total sales amount as the sum of (Quantity × UnitPrice) across all orders.

Answer

Total Sales = COUNTROWS(Orders)

Answer

Total Sales = AVERAGE(Orders[Quantity]) * AVERAGE(Orders[UnitPrice])

Answer

Total Sales = SUM(Orders[Quantity] * Orders[UnitPrice])

Question 18

Which THREE of the following are valid data types in Power BI?

Accepted Answer

Whole Number. Whole Number is a valid data type in Power BI for storing integers without decimal places. It is used for counts, IDs, and other discrete values, and it supports operations like aggregation and indexing efficiently.

Answer

Currency

Answer

Time

Question 19

Which TWO of the following are best practices for designing a Power BI data model?

Accepted Answer

Use a star schema to reduce model complexity.. Option A is correct because a star schema organizes data into dimension and fact tables, reducing redundancy and simplifying queries. This design improves query performance by minimizing the number of table joins and enabling efficient aggregation, which is a core best practice in Power BI data modeling.

Answer

Store dimension attributes directly in fact tables for faster queries.

Answer

Enable bi-directional cross-filtering on all relationships.

Answer

Create calculated columns instead of Power Query transformations when possible.

Question 20

Which TWO actions can improve performance of a Power BI DirectQuery model?

Accepted Answer

Ensure proper indexing in the source database. Option C is correct because proper indexing in the source database reduces the query execution time for DirectQuery models. DirectQuery sends queries to the source database in real-time, so efficient indexes on columns used in filters, joins, and aggregations minimize table scans and improve retrieval speed.

Answer

Switch the model to Import mode

Answer

Enable bidirectional cross-filtering on all relationships

Answer

Use calculated columns with complex logic