PL-300 Practice Test 13 — 15 Questions

Question 1

You are modeling data in Power BI that includes a table named SurveyResponses with columns: ResponseID, QuestionID, RespondentID, and AnswerText. Each respondent answers multiple questions. You need to create a measure that counts the number of unique respondents who answered a specific question. Which DAX measure should you use?

Accepted Answer

DISTINCTCOUNT(SurveyResponses[RespondentID]). Option A is correct because DISTINCTCOUNT(SurveyResponses[RespondentID]) counts the number of unique RespondentID values in the table, which directly gives the count of unique respondents who answered a specific question when used in a filter context (e.g., with a slicer or visual grouping by QuestionID). This is the standard DAX pattern for counting distinct entities in a column.

Answer

COUNT(SurveyResponses[RespondentID])

Answer

COUNTROWS(SurveyResponses)

Answer

COUNTA(SurveyResponses[RespondentID])

Question 2

You are analyzing a DAX query as shown in the exhibit. You need to determine the result set. The model contains tables: Date, Product, and Sales with relationships. Which statement accurately describes the output?

Accepted Answer

The query returns total sales per year and category for Amount > 100. Option A is correct because the DAX query uses SUMMARIZE to group sales by 'Year' from the Date table and 'Category' from the Product table, then filters the Sales table to include only rows where Amount > 100. The result is a table of total sales (sum of Amount) for each combination of year and category that meets the filter condition.

Answer

The query returns total sales for each year, ignoring category

Answer

The query returns sales amounts only for products with Amount > 100

Answer

The query returns total sales for each category, ignoring year

Question 3

You have a Power BI model with a table 'Employees' that includes columns: 'EmployeeID', 'ManagerID', 'Name'. You need to create a hierarchy that shows the reporting structure. What should you do first?

Accepted Answer

Create a self-join relationship from Employees[EmployeeID] to Employees[ManagerID].. To create a hierarchy that shows the reporting structure in Power BI, you first need to establish a self-join relationship between the Employees table and itself, linking EmployeeID to ManagerID. This allows Power BI to traverse the parent-child relationship and build a natural hierarchy (e.g., using Parent-Child functions like PATH). Without this relationship, the model cannot understand how employees relate to their managers.

Answer

Merge the Employees table with itself using Merge Queries.

Answer

Add a calculated column that concatenates EmployeeID and ManagerID.

Answer

Create a many-to-many relationship between Employees and itself.

Question 4

You are building a star schema model in Power BI. You have a fact table of sales transactions and dimension tables for Date, Customer, Product, and Store. The Date table contains a column 'FiscalYear' that you want to use for time intelligence calculations. What is the best practice for handling the Date relationship?

Accepted Answer

Mark the Date table as a date table using the Calendar icon in the Table tools ribbon and set a relationship on the Date column.. Option D is correct because marking the Date table as a date table (via the Calendar icon in Table tools) and creating a relationship on the Date column is the best practice for time intelligence in Power BI. This ensures that DAX time intelligence functions (e.g., TOTALYTD, SAMEPERIODLASTYEAR) work correctly by using a single, continuous date column that aligns with the fact table's date column. It also avoids the need for composite keys or separate fiscal tables, maintaining a clean star schema.

Answer

Create a separate fiscal date table and relate it to the fact table using the FiscalYear column.

Answer

Use the built-in DATESYTD function directly on the OrderDate column from the fact table.

Answer

Create a composite key using FiscalYear and Quarter columns in the Date table and relate to the fact table.

Question 5

You have a Power BI data model with a 'Date' table that contains continuous dates from January 1, 2020, to December 31, 2025. The 'Sales' table has a relationship with the 'Date' table. You need to create a measure that calculates the total sales for the last 12 months from the current context. Which DAX function should you use?

Accepted Answer

DATESINPERIOD. Option D is correct. DATESINPERIOD allows you to specify a range of dates (e.g., last 12 months) relative to a end date. Option A is incorrect because SAMEPERIODLASTYEAR returns the same period one year ago, not the last 12 months. Option B is incorrect because DATEADD shifts dates by an interval. Option C is incorrect because PREVIOUSMONTH returns the previous month only.

Answer

PREVIOUSMONTH

Answer

DATEADD

Answer

SAMEPERIODLASTYEAR

Question 6

You are designing a Power BI model that includes a fact table with sales data and a dimension table for customers. Each customer can have multiple addresses, but you only need the primary address for analysis. The source system has a 'CustomerAddress' table with a 'IsPrimary' flag. What is the best approach to bring this into the model?

Accepted Answer

In Power Query, filter the CustomerAddress table to only include rows where IsPrimary = True, then merge with Customer.. Option C is correct because it uses Power Query to filter the CustomerAddress table to only primary addresses before merging with the Customer dimension. This ensures that only the necessary rows are imported into the model, reducing data volume and avoiding complex DAX or relationship overhead. The result is a clean, single-row-per-customer dimension that directly supports analysis without runtime filtering.

Answer

Use a DAX measure to filter the address table dynamically.

Answer

Import the entire CustomerAddress table and create an active relationship on the CustomerID column.

Answer

Create a calculated table using SUMMARIZE to get the primary address per customer.

Question 7

Which TWO actions are best practices for optimizing Power BI data models?

Accepted Answer

Replace text-based relationship columns with integer keys.. Option A is correct because replacing text-based relationship columns with integer keys (surrogate keys) reduces storage size and improves join performance. Power BI's VertiPaq engine compresses integer columns far more efficiently than text columns, leading to faster query execution and smaller memory footprint.

Answer

Use calculated columns instead of measures where possible.

Answer

Hide columns that are not used in reports.

Answer

Use many-to-many relationships instead of bridge tables.

Question 8

Refer to the exhibit. You write this Power Query M code to import data. What does the final step do?

Accepted Answer

Filters rows where Amount > 100. The final step in the Power Query M code filters the table to keep only rows where the 'Amount' column value is greater than 100. This is achieved by the `Table.SelectRows` function with the condition `each [Amount] > 100`, which evaluates each row and retains those meeting the predicate. The result is a reduced dataset containing only transactions with an Amount exceeding 100.

Answer

Sorts rows by Amount descending

Answer

Groups rows by Amount

Answer

Removes the Amount column

Question 9

You have a Power BI model with a table named 'Orders' that contains columns: OrderID, CustomerID, OrderDate, and TotalAmount. You need to create a measure that calculates the total sales amount for orders placed in the last 30 days, but only for customers who have placed more than 5 orders in total. What is the most efficient DAX measure?

Accepted Answer

TotalSalesLast30Days = CALCULATE(SUM(Orders[TotalAmount]), KEEPFILTERS(Orders[OrderDate] > TODAY() - 30), KEEPFILTERS(CALCULATE(COUNTROWS(Orders), ALLEXCEPT(Orders, Orders[CustomerID])) > 5)). Option B is correct because it uses KEEPFILTERS to apply both the date filter and the customer-level condition within a single CALCULATE, ensuring that the row context for the customer filter is properly expanded via ALLEXCEPT without causing context transition issues. This approach is more efficient than using multiple FILTER iterators, as it leverages CALCULATE's filter modification capabilities to apply the conditions at the correct granularity.

Answer

TotalSalesLast30Days = CALCULATE(SUM(Orders[TotalAmount]), FILTER(Orders, Orders[OrderDate] > TODAY() - 30), FILTER(Orders, CALCULATE(COUNTROWS(Orders), ALLEXCEPT(Orders, Orders[CustomerID])) > 5))

Answer

TotalSalesLast30Days = SUMX(FILTER(Orders, Orders[OrderDate] > TODAY() - 30 && CALCULATE(COUNTROWS(Orders), ALLEXCEPT(Orders, Orders[CustomerID])) > 5), Orders[TotalAmount])

Answer

TotalSalesLast30Days = CALCULATE(SUM(Orders[TotalAmount]), DATESINPERIOD(Orders[OrderDate], TODAY(), -30, DAY))

Question 10

You have a Power BI model containing a table 'Orders' with columns: OrderID, CustomerID, OrderDate, Amount. You need to create a measure that calculates the total amount from the previous year. Which DAX function should be used?

Accepted Answer

PREVIOUSYEAR. The PREVIOUSYEAR function is specifically designed to return a table of dates for the previous year relative to the current context in the filter context. It is the most direct and appropriate DAX function for calculating total amount from the previous year when working with a standard calendar date column.

Answer

DATEADD

Answer

PARALLELPERIOD

Answer

SAMEPERIODLASTYEAR

Question 11

A data model contains a Date table and a Sales table. You need to create a measure that calculates total sales for the previous year. Which DAX function should you use?

Accepted Answer

SAMEPERIODLASTYEAR. Option B is correct because SAMEPERIODLASTYEAR returns a set of dates shifted back one year. Option A (DATEADD) can also be used but is more general. Option C (PREVIOUSYEAR) is not a valid DAX function. Option D (PARALLELPERIOD) returns a parallel period but not necessarily the previous year.

Answer

DATEADD

Answer

PARALLELPERIOD

Answer

PREVIOUSYEAR

Question 12

You are modeling data for a sales analysis. You have a fact table 'Sales' and a dimension table 'Date'. You need to create a relationship between 'Sales[OrderDate]' and 'Date[Date]'. The 'Date' table contains dates from January 1, 2020 to December 31, 2025. The 'Sales' table contains orders from January 1, 2021 to June 30, 2024. Which relationship cardinality should you use?

Accepted Answer

Many-to-one (*:1). The correct cardinality is Many-to-one (*:1) because the 'Sales' table contains many rows for each date (multiple orders can occur on the same date), while the 'Date' table has unique dates. This creates a classic star-schema relationship where the many side (Sales) points to the one side (Date). In Power BI, the cardinality is defined from the perspective of the filter propagation direction, so *:1 means many rows in Sales match one row in Date.

Answer

Many-to-many (*:*)

Answer

One-to-many (1:*)

Answer

One-to-one (1:1)

Question 13

You are creating a Power BI report that uses a table named Orders with columns: OrderID, OrderDate, ShipDate, and Status. You need to create a calculated table that contains one row per month with the total number of orders shipped in that month. Which TWO steps should you take?

Accepted Answer

Create a date table using CALENDAR or CALENDARAUTO. The correct answers are A and D. First, create a date table (A) for time intelligence, then use SUMMARIZECOLUMNS (D) to group by month and count orders. Option B (CALENDARAUTO) is a function to create a date table, but the step is incomplete. Option C (VALUES) returns distinct values, not aggregated. Option E (GROUPBY) is similar but less common.

Answer

Use CALENDARAUTO in a measure

Answer

Use the GROUPBY function

Answer

Use the VALUES function to get unique months

Question 14

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

Accepted Answer

Using the 'Mark as Date Table' feature on an existing table with dates.. Option B is correct: using CALENDARAUTO auto-detects date range from model. Option D is correct: marking a table as date table is a standard method. Option A is incorrect because manual typing is not practical. Option C is incorrect because there is no 'Create Date Table' button. Option E is incorrect because there is no 'Generate Date Table' in the modeling tab.

Answer

Using the 'Create Date Table' button in the ribbon.

Answer

Using the 'Generate Date Table' option in Power Query.

Answer

Manually typing dates in Excel and importing.

Question 15

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 D is correct because using Power Query to combine data and scheduling a single refresh is efficient. Option A is wrong because live connections are not possible with all sources. Option B is wrong because it duplicates work. Option C is wrong because Excel Online is not supported for direct refresh.

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.