PL-300 Model the data Practice Test 6 — 15 Questions

Question 1

Refer to the exhibit. You apply the above RLS rule to a semantic model. The rule is intended to restrict sales data by the user's region, which is stored in the user's email domain (e.g., user@west.contoso.com). However, the rule does not filter any rows. What is the most likely issue?

Accepted Answer

The filter expression compares the full UPN to the Region column, which likely contains only the region name.. Option B is correct because USERPRINCIPALNAME() returns the full UPN, which includes the domain. The filter expression compares the entire UPN to the Region column, which likely contains only the region part (e.g., 'West'). Option A is wrong because the rule is applied. Option C is wrong because the table exists. Option D is wrong because USERPRINCIPALNAME() is valid.

Answer

The Sales table does not have a Region column.

Answer

USERPRINCIPALNAME() is not available in the current Power BI version.

Answer

The RLS rule is not applied to the semantic model.

Question 2

You have a Power BI model with a large fact table (100 million rows) and several dimension tables. You need to improve query performance for a report that uses a date slicer. The report currently takes 10 seconds to load. What is the most effective optimization?

Accepted Answer

Partition the fact table by date range.. Partitioning the fact table by date range allows Power BI to perform query folding and only scan the relevant partitions when a date slicer is applied. This reduces the amount of data loaded into memory and processed, directly addressing the 100-million-row fact table and the 10-second load time. Partitioning is a native optimization for large tables in DirectQuery or Import mode when using date-based filtering.

Answer

Remove unused columns from the fact table.

Answer

Increase the Power BI service capacity.

Answer

Create an aggregated table that summarizes data at the month level.

Question 3

You are modeling data from a source that includes a column 'FullName' (e.g., 'John Doe'). You want to create separate 'FirstName' and 'LastName' columns for analysis. What is the most efficient way?

Accepted Answer

In Power Query, split the column by delimiter (space) into two columns.. Option D is correct because splitting a column by delimiter in Power Query is the most efficient, native method for transforming data at the query level. It leverages Power Query's M language to perform the split in a single step, which is optimized for performance and can be refreshed automatically. This approach avoids the overhead of DAX calculated columns, which are computed in the storage engine and can slow down report rendering.

Answer

Create calculated columns using DAX functions LEFT, RIGHT, and FIND.

Answer

Use the 'Replace Values' feature to manually separate names.

Answer

Use Excel formulas in a source query.

Question 4

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

Accepted Answer

SWITCH(TRUE(), Sales[Amount] < 100, "Low", Sales[Amount] < 500, "Medium", "High"). Option A is correct because it uses SWITCH(TRUE(), ...) to evaluate multiple conditions sequentially, returning the first matching result. This is the standard pattern for categorizing continuous values into discrete buckets in DAX, as SWITCH evaluates conditions in order and stops at the first TRUE condition.

Answer

IF(Sales[Amount] < 100, "Low", IF(Sales[Amount] < 500, "Medium", "High"))

Answer

SELECTEDVALUE(Sales[Amount]) & "Category"

Answer

SWITCH(Sales[Amount], 100, "Low", 500, "Medium", "High")

Question 5

You have a star schema with a Sales fact table and a Date dimension. You need to calculate year-to-date sales that reset each fiscal year starting July 1. Which measure is correct?

Accepted Answer

TOTALYTD(SUM(Sales[Amount]), 'Date'[Date], ALL('Date'), "6-30"). Option D is correct because TOTALYTD allows a custom year-end date. Option A uses standard calendar year. Option B has incorrect syntax. Option C uses DATESYTD without specifying fiscal year-end.

Answer

TOTALYTD(SUM(Sales[Amount]), 'Date'[Date])

Answer

TOTALYTD(SUM(Sales[Amount]), 'Date'[Date], "6-30")

Answer

CALCULATE(SUM(Sales[Amount]), DATESYTD('Date'[Date], "6-30"))

Question 6

You need to create a relationship between two tables in Power BI. Both tables contain a column named 'ProductID', but the values in one table are integers and in the other are text. What should you do first?

Accepted Answer

Ensure both columns have the same data type, either by changing the data type in Power Query or in the model view.. Option B is correct because Power BI requires matching data types to create a relationship. Option A is wrong because it is not necessary. Option C is wrong because changing the relationship type does not fix data type mismatch. Option D is wrong because merging is not the first step.

Answer

Merge the two tables into one in Power Query.

Answer

Create a new calculated column that converts the integer to text using FORMAT.

Answer

Set the relationship to 'Many-to-many' to bypass the type mismatch.

Question 7

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 B is correct because the role filter overrides the table-level filter for users in that role. Option A is wrong because the role filter takes precedence. Option C is wrong because the role filter applies only to the role. Option D is wrong because RLS filters rows, not columns.

Answer

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

Answer

No rows, because there is a conflict.

Answer

Rows where Region is 'South' only.

Question 8

You have a table with columns 'Date', 'Sales', and 'Quantity'. You need to create a measure that calculates the average sales per transaction. Which DAX measure should you use?

Accepted Answer

AVERAGE(Sales[Sales]). Option B is correct because AVERAGE of Sales gives average per row. Option A totals sales, not average. Option C averages Quantity, not Sales. Option D divides total sales by total quantity, which is not average per transaction.

Answer

AVERAGE(Sales[Quantity])

Answer

DIVIDE(SUM(Sales[Sales]), SUM(Sales[Quantity]))

Answer

SUM(Sales[Sales])

Question 9

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 10

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 11

You have two tables: 'Orders' and 'Customers'. You want to create a relationship where each order is linked to one customer, but a customer can have many orders. Which cardinality should you choose?

Accepted Answer

Many-to-one (*:1) from Orders to Customers. Option A is correct because one-to-many from Customers to Orders is the natural direction. Option B is reverse. Option C is wrong because many-to-many is not appropriate. Option D is wrong because one-to-one is too restrictive.

Answer

Many-to-many (*:*)

Answer

One-to-many (1:*) from Orders to Customers

Answer

One-to-one (1:1)

Question 12

You have a Power BI model with a 'Date' table marked as a date table. You need to create a measure that calculates the running total of sales over the last 12 months. Which DAX function should you use?

Accepted Answer

DATESINPERIOD. Option C, DATESINPERIOD, is correct because it allows you to define a dynamic window of dates—specifically, the last 12 months ending with the latest date in the current filter context. When used with a measure like CALCULATE(SUM(Sales[Amount]), DATESINPERIOD('Date'[Date], MAX('Date'[Date]), -12, MONTH)), it shifts the date range backward by 12 months from the last visible date, making it ideal for a rolling 12-month total. The 'Date' table being marked as a date table ensures that time intelligence functions respect the continuous date range.

Answer

PREVIOUSYEAR

Answer

DATESYTD

Answer

DATEADD

Question 13

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 14

Which THREE factors should you consider when designing a star schema in Power BI?

Accepted Answer

A separate date table should be created for time intelligence.. A separate date table is required for time intelligence functions in Power BI because DAX time intelligence functions (e.g., TOTALYTD, SAMEPERIODLASTYEAR) rely on a continuous, contiguous date range with no gaps. Power BI automatically marks a table as a date table only if it contains a complete set of dates from the earliest to the latest transaction, enabling functions like DATEADD and DATESBETWEEN to work correctly across all granularities.

Answer

Use natural keys instead of surrogate keys in dimension tables.

Answer

Use a snowflake schema to reduce data redundancy.

Question 15

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

Accepted Answer

FILTER. Options A and B are correct. FILTER returns a table that can be used as a filter. CALCULATE modifies filter context. Option C is wrong because SUM is an aggregation function. Option D is wrong because ALL removes filters. Option E is wrong because VALUES returns distinct values but does not filter by condition.

Answer

VALUES

Answer

SUM

Answer

ALL

PL-300 Model the data Practice Test 6 — 15 Questions

Exhibit

PL-300 Model the data Practice Test 6 — 15 Questions

Exhibit