PL-300 Practice Test 17 — 15 Questions

Question 1

You need to create a relationship between two tables in Power BI. Table A has a column 'ProductID' with unique values. Table B has a column 'ProductID' with duplicate values. Which relationship cardinality should you choose?

Accepted Answer

One-to-many (Table A to Table B). Option A is correct. Since Table A has unique values and Table B has duplicates, the relationship is one-to-many (Table A to Table B). Option B is wrong because many-to-one would imply Table A has duplicates. Option C is wrong because one-to-one requires uniqueness on both sides. Option D is wrong because many-to-many requires a junction table.

Answer

Many-to-one (Table A to Table B)

Answer

One-to-one

Answer

Many-to-many

Question 2

Which THREE of the following are valid reasons to use a composite model (mixed storage mode) in Power BI?

Accepted Answer

To enable real-time data from a DirectQuery source while using imported historical data.. Options B, C, and E are correct. Option B: Composite models allow combining DirectQuery and Import. Option C: Aggregations can be used for large datasets. Option E: Real-time data from DirectQuery with aggregated historical data. Option A is wrong because composite models do not automatically improve relationship performance. Option D is wrong because calculated tables are not supported in DirectQuery sources.

Answer

To improve relationship performance between tables.

Answer

To create calculated tables based on DirectQuery sources.

Question 3

You are building a Power BI model with a fact table and multiple dimension tables. You want to enforce a rule that each fact row must have a matching row in a related dimension. Which modeling technique should you use?

Accepted Answer

Assume referential integrity in the relationship settings.. Option C is correct because referential integrity ensures every fact foreign key matches a dimension primary key. Option A is wrong because cardinality does not enforce integrity. Option C is correct. Option D is wrong because cross-filter direction affects filtering, not integrity.

Answer

Set the relationship cardinality to many-to-many.

Answer

Set the cross-filter direction to both.

Answer

Use a calculated column to check for existence.

Question 4

You are designing a star schema for a sales data model. Which table should be defined as a dimension table?

Accepted Answer

Date. Option B is correct because Date is a classic dimension used for time-based analysis. Option A is wrong because SalesAmount is a measure. Option C is wrong because TransactionID is a fact table key. Option D is wrong because OrderQuantity is a measure.

Answer

TransactionID

Answer

OrderQuantity

Answer

SalesAmount

Question 5

Which THREE are best practices for designing a star schema in Power BI?

Accepted Answer

Create a separate date dimension table. Options A, C, and D are correct. Descriptive keys, separate date dimension, and aggregations at grain are best practices. Option B is wrong because natural keys are preferred when available. Option E is wrong because multiple relationships complicate the model.

Answer

Create multiple relationships between fact and dimension

Answer

Use composite keys in fact tables

Question 6

You are designing a Power BI data model for a retail chain. You have three fact tables: Sales, Inventory, and Returns. Which modeling approach is best for cross-filtering between these facts?

Accepted Answer

Use role-playing dimensions. Option B is correct because role-playing dimensions allow sharing common dimensions like Date, Product, Store. Option A causes ambiguity. Option C is not a standard approach. Option D is unnecessary if dimensions are shared.

Answer

Use calculated columns to link facts

Answer

Merge all fact tables into one

Answer

Create separate dimensions for each fact table

Question 7

You are building a Power BI data model with a sales table containing millions of rows. You need to design a date dimension table to support time intelligence calculations. What is the best practice for creating the date table?

Accepted Answer

Create a date table using DAX CALENDARAUTO function. Option C is correct because using DAX CALENDARAUTO ensures a contiguous set of dates covering all dates in the data model, which is essential for accurate time intelligence. Option A is wrong because importing from Excel may introduce gaps. Option B is wrong because Power Query M language can also create date tables, but DAX is typically more efficient for this purpose. Option D is wrong because using a date column directly from the fact table without a separate date table limits time intelligence functions.

Answer

Use the date column from the sales table as the primary date dimension

Answer

Import a date table from an Excel file

Answer

Create a date table using Power Query M language

Question 8

Which THREE considerations are important when implementing row-level security (RLS) in Power BI? (Select exactly 3.)

Accepted Answer

Roles can use DAX expressions to define filters. Options A, B, and D are correct. Option A: RLS filters data at the row level based on the user's role. Option B: Roles can be defined using DAX expressions that filter tables. Option D: RLS is enforced in DirectQuery mode, but performance depends on the source. Option C is wrong because RLS does not restrict access to measures; measures are still visible unless explicitly hidden. Option E is wrong because RLS is not applied when users view data in Power BI Desktop; it is only applied when published to the service.

Answer

RLS is automatically applied when using Analyze in Excel

Answer

RLS can restrict access to specific measures

Question 9

You have a Power BI dataset that includes a table 'Orders' with columns: OrderDate, ShipDate, CustomerID, and SalesAmount. You need to create a calculated column that shows the number of days between OrderDate and ShipDate. Which DAX expression should you use?

Accepted Answer

DATEDIFF(Orders[OrderDate], Orders[ShipDate], DAY). Option A is correct. DATEDIFF computes the difference between two dates in the specified unit (DAYS). Option B returns a date, not a number. Option C returns the end of the month, not a difference. Option D returns the year, not the difference.

Answer

YEAR(Orders[OrderDate])

Answer

ENDOFMONTH(Orders[OrderDate])

Answer

DATEADD(Orders[OrderDate], 1, DAY)

Question 10

You have a Power BI semantic model that imports sales data from Azure SQL Database. The model includes a date table and a fact table with a column 'SalesAmount'. You need to create a measure that calculates the total sales amount only for the last 30 days relative to the report date. Which DAX expression should you use?

Accepted Answer

CALCULATE(SUM(Sales[SalesAmount]), DATESBETWEEN('Date'[Date], TODAY()-30, TODAY())). Option B is correct because TOTALMTD is a time intelligence function that calculates month-to-date, not last 30 days. Option A uses DATESBETWEEN with a fixed range of 30 days from today, which matches the requirement. Option C returns the first 30 days of the current month. Option D sums all sales in the last 30 days but does not filter by date.

Answer

TOTALMTD(SUM(Sales[SalesAmount]), 'Date'[Date])

Answer

CALCULATE(SUM(Sales[SalesAmount]), DATESINPERIOD('Date'[Date], TODAY(), -30, DAY))

Answer

SUMX(FILTER(Sales, Sales[Date] >= TODAY()-30), Sales[SalesAmount])

Question 11

Which TWO of the following are valid reasons to create a separate date table in a Power BI semantic model? (Select two.)

Accepted Answer

To enable time intelligence functions like TOTALYTD.. Options A and C are correct. A date table is required for time intelligence functions (A) and for filtering by date attributes not present in the fact table (C). Option B is incorrect because relationships are one-to-many. Option D is incorrect because multiple active relationships cause ambiguity. Option E is incorrect because measures can be calculated without a date table, but time intelligence functions need it.

Answer

To calculate measures that require date arithmetic.

Answer

To create many-to-many relationships between fact tables.

Answer

To allow multiple active relationships from the same fact table to the date table.

Question 12

Refer to the exhibit. You have a Sales table. You create a measure: TotalSales = SUM(Sales[SalesAmount]). You then create another measure: SalesAboveAverage = CALCULATE([TotalSales], FILTER(Sales, Sales[SalesAmount] > AVERAGE(Sales[SalesAmount]))). What is the value of SalesAboveAverage?

Accepted Answer

550. Option D is correct. The average of SalesAmount is (100+200+300+150+250)/5 = 200. The rows with SalesAmount > 200 are: 300 and 250. Sum = 550. Option A is wrong because it includes all rows. Option B is wrong because it uses total sum. Option C is wrong because it includes 200? No, 200 is not > 200. So correct is 550.

Answer

1000

Answer

800

Answer

450

Question 13

You have a Power BI model with a fact table 'Sales' and dimensions 'Customer', 'Product', 'Date'. You need to enforce that every fact row has a valid CustomerKey. Which approach enforces referential integrity in Power BI?

Accepted Answer

Set the relationship between Sales and Customer to 'Enforce Referential Integrity'. Option C is correct because marking the relationship as 'Enforce Referential Integrity' in Power BI ensures that only valid keys are used. Option A is wrong because combining tables changes the model structure. Option B is wrong because hiding columns does not enforce integrity. Option D is wrong because the assumption is not enforced natively.

Answer

Merge Sales and Customer tables into a single table

Answer

Hide CustomerKey columns in both tables

Answer

Assume all CustomerKey values are valid

Question 14

Which TWO DAX functions can be used to create a calculated table in Power BI?

Accepted Answer

FILTER. Options A and C are correct. SUMMARIZECOLUMNS and FILTER return tables. Option B is wrong because SUMX returns a scalar. Option D is wrong because SELECTEDVALUE returns a scalar. Option E is wrong because CALCULATE returns a scalar.

Answer

SELECTEDVALUE

Answer

CALCULATE

Answer

SUMX

Question 15

You need to create a hierarchy in Power BI that allows drilling down from Year to Quarter to Month. What is the correct approach?

Accepted Answer

Create a hierarchy in the Date table using Year, Quarter, Month columns. Option C is correct because creating a hierarchy in the Date table using these columns enables drill-down. Option A does not create a hierarchy. Option B is a DAX function. Option D is not a hierarchy.

Answer

Create separate measures for each level

Answer

Use the Drillthrough feature

Answer

Use the DATESYTD function