PL-300 Practice Exam 60 — 20 Questions

Question 1

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.

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Increase the Power BI service capacity.

Answer

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

Question 2

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

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DATESYTD

Answer

DATEADD

Question 3

You have a Power BI model with a date table. You need to ensure that time intelligence functions like TOTALYTD work correctly. What is the most important requirement for the date table?

Accepted Answer

The date table must be marked as a date table in Power BI. Option A is correct because Power BI requires that a date table be explicitly marked as a date table via the 'Mark as Date Table' option in the Table tools ribbon. This action tells Power BI that the table contains unique, non-blank date values that can be used by time intelligence functions like TOTALYTD, ensuring they reference the correct date column for calculations such as year-to-date aggregation.

Answer

All date columns must be visible

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The date table must have a single date column

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The date table must contain continuous dates without gaps

Question 4

Refer to the exhibit. What is the result of this DAX expression?

Accepted Answer

Sum of Amount only for the West region. The DAX expression uses the CALCULATE function to modify the filter context, applying a filter that restricts the calculation to only the 'West' region. The ALL function is not used to remove filters, so the expression returns the sum of Amount exclusively for the West region, not for all regions or other regions.

Answer

Sum of Amount for all regions except West

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Sum of Amount for regions other than West

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Sum of Amount for all regions

Question 5

You have the DAX expression shown. What does this expression return?

Accepted Answer

Total sales for the year of the latest date in the current filter context. The DAX expression uses CALCULATE to modify the filter context for 'Sales[Amount]' by applying a filter that selects only dates where the year equals the year of the latest date present in the current filter context. The MAX function returns the maximum date from the Date table within the current filter context, and YEAR extracts its year. This effectively returns total sales for the year containing the most recent date in the current filter context, not necessarily the previous year or the absolute maximum year in the Date table.

Answer

Total sales for the previous year

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Total sales for the maximum year in the Date table

Answer

Total sales for the current year

Question 6

You are designing a Power BI data model for a sales analysis. The model includes a table named Sales with columns: OrderID, OrderDate, CustomerID, ProductID, Quantity, UnitPrice. You need to create a star schema. Which TWO tables should you create as dimension tables?

Accepted Answer

Product. In a star schema, dimension tables contain descriptive attributes that provide context for the measures stored in the fact table. Product is a classic dimension because it holds product details (e.g., name, category) that describe the sales facts. Customer is also a dimension because it stores customer attributes (e.g., name, region) used for slicing and dicing sales data.

Answer

Calendar

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Sales

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Orders

Question 7

Which TWO of the following are best practices for designing a star schema in Power BI?

Accepted Answer

Dimension tables should have a primary key and descriptive columns.. Option A is correct because dimension tables in a star schema are designed to store descriptive attributes (e.g., product name, category) and must have a primary key column that uniquely identifies each row. This primary key is referenced by foreign keys in fact tables, enabling efficient filtering and slicing in Power BI. Without a primary key, relationships cannot be properly enforced, leading to ambiguous or incorrect query results.

Answer

Fact tables should contain calculated columns for business logic.

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Merge all tables into a single flat table for simplicity.

Answer

Use many-to-many relationships between fact and dimension tables.

Question 8

You have a Power BI model with a fact table 'Sales' and a dimension table 'Product'. The Product table contains columns: ProductID, ProductName, Category, and Subcategory. You want to create a hierarchy for drill-down in reports: Category > Subcategory > ProductName. What is the correct way to define this hierarchy?

Accepted Answer

In Model view, right-click 'Category' > 'Create hierarchy', then add Subcategory and ProductName as levels.. Option A is correct because Power BI's Model view allows you to create a hierarchy by right-clicking a column (e.g., Category) and selecting 'Create hierarchy', then adding Subcategory and ProductName as child levels. This defines a natural drill-down path for visuals, enabling users to navigate from Category to Subcategory to ProductName without modifying the data model or using workarounds.

Answer

In Power Query, merge the Category, Subcategory, and ProductName columns into one.

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Create a calculated column using CONCATENATE to combine the levels.

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In Report view, add all three columns to a visual's Values well.

Question 9

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.

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

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Use Excel formulas in a source query.

Question 10

Refer to the exhibit. You have the model shown. The active relationship is between OrderDate and Date. You create the measure TotalShipments. What does this measure calculate?

Accepted Answer

Sum of Amount filtered by ShipDate. The measure TotalShipments uses the active relationship between OrderDate and Date, but the CALCULATE function or filter context in the measure applies a filter on ShipDate. Because the active relationship is based on OrderDate, any filter on ShipDate requires the measure to use the inactive relationship (via USERELATIONSHIP or by default crossing the active relationship). The correct answer is D because the measure sums Amount filtered by ShipDate, leveraging the inactive relationship to evaluate shipments based on their actual ship date.

Answer

Sum of Amount where ShipDate is not blank

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Sum of all Amount regardless of date

Answer

Sum of Amount filtered by OrderDate

Question 11

Which TWO of the following are best practices when designing star schemas in Power BI? (Select two.)

Accepted Answer

Store numeric measures in fact tables.. Option A is correct because fact tables in a star schema are designed to store quantitative, numeric measures (e.g., sales amount, quantity) that can be aggregated. This aligns with the star schema principle of separating measures (facts) from descriptive context (dimensions), enabling efficient compression and fast aggregations in Power BI's VertiPaq engine.

Answer

Use calculated columns in fact tables for row-level security.

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Include many columns in fact tables for filtering.

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Normalize dimension tables to reduce redundancy.

Question 12

You have a Power BI data model with a table named Employees that contains: EmployeeID, ManagerID, Department, and Salary. You need to create a parent-child hierarchy for the reporting structure. Which THREE actions should you perform?

Accepted Answer

Enable the 'Group on' feature in the visual to expand/collapse. Option A is correct because enabling the 'Group on' feature in a visual (such as a matrix or table) allows users to expand and collapse parent-child hierarchies, making the reporting structure interactive. This feature works with the hierarchy path created by calculated columns (Option B) and the self-referencing relationship (Option C) to display the drill-down behavior.

Answer

Create a separate table for managers

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Set cross filter direction to Both on the relationship

Question 13

Which THREE of the following are valid considerations when using Power BI DirectQuery mode?

Accepted Answer

Queries are sent to the underlying data source in real time.. Option C is correct because in DirectQuery mode, Power BI does not import data; instead, it sends native queries to the underlying data source (e.g., SQL Server, Oracle) each time a visual is rendered or a filter is applied. This ensures that the data displayed is always current, as the query is executed in real time against the source system.

Answer

Data is imported into the Power BI dataset.

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You cannot create relationships between tables.

Question 14

Refer to the exhibit. You are configuring a Power BI data model for a workspace containing a semantic model with sensitive salary data. The JSON policy above is applied. A viewer reports that they can see the EmployeeSalaries table in the report. What is the most likely reason?

Accepted Answer

The JSON policy is for row-level security (RLS), not object-level security (OLS).. Option C is correct because the JSON policy shown uses the `tables` array with `filter` expressions, which is the syntax for row-level security (RLS) policies in Power BI. RLS restricts data rows within a table but does not hide the table itself from the report view. Object-level security (OLS) requires a different policy structure using `excludedTables` or `setObjectLevelSecurity` in XMLA endpoints or Tabular Editor, not a JSON RLS policy. Therefore, the viewer can still see the EmployeeSalaries table because OLS has not been configured.

Answer

The EmployeeSalaries table is not in the semantic model.

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The excludedTables property hides the table only in Power Query, not in reports.

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The policy has not been applied to the semantic model.

Question 15

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.

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Use a snowflake schema to reduce data redundancy.

Question 16

Which THREE of the following are benefits of using a star schema in Power BI? (Select three.)

Accepted Answer

Simplified DAX formulas. Option A is correct because star schemas reduce the number of table joins and filter context complexity, which simplifies DAX formulas. Measures and calculated columns in Power BI rely on unambiguous relationships; a star schema ensures each fact table connects to dimension tables via one-to-many relationships, allowing DAX to propagate filters automatically without complex cross-filtering logic.

Answer

Supports many-to-many relationships natively

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Increased data redundancy

Question 17

You are creating a Power BI report that requires a many-to-many relationship between 'Product' and 'Customer' tables. What is the recommended way to implement this?

Accepted Answer

Create a bridge table with ProductID and CustomerID and create relationships to both tables. Option B is correct because in Power BI, a many-to-many relationship between two tables (e.g., Product and Customer) is best implemented using a bridge table that contains the foreign keys (ProductID and CustomerID) from both tables. This bridge table resolves the many-to-many cardinality by creating two one-to-many relationships: one from Product to the bridge table and one from Customer to the bridge table. This approach ensures proper filtering and avoids ambiguity in the data model, which is a standard best practice in dimensional modeling.

Answer

Combine Product and Customer into a single table

Answer

Create a direct many-to-many relationship using the 'many-to-many' cardinality option in Power BI

Answer

Use a calculated column to concatenate IDs

Question 18

You are a Power BI developer for a healthcare organization. You have a semantic model that contains a table named PatientVisits with columns: VisitID, PatientID, VisitDate, DiagnosisCode, and Charges. You have a separate table named Diagnosis with columns: DiagnosisCode, DiagnosisDescription, and Category. The Diagnosis table contains duplicate rows for the same DiagnosisCode but with different descriptions due to data entry errors. You need to create a relationship between PatientVisits and Diagnosis on DiagnosisCode. However, the duplicate values in Diagnosis prevent creating a one-to-many relationship. You must ensure that the relationship can be created without removing any rows from the Diagnosis table. What should you do?

Accepted Answer

Create a calculated table in DAX using SUMMARIZE(Diagnosis, Diagnosis[DiagnosisCode], "Description", FIRSTNONBLANK(Diagnosis[DiagnosisDescription], 1)) and use that table for the relationship.. Option A is correct: creating a calculated table that groups by DiagnosisCode and takes the first description resolves duplicates. Option B is wrong because removing duplicates in Power Query would remove rows permanently. Option C is wrong because you cannot create a relationship on a dash. Option D would create a many-to-many relationship, which is not ideal.

Answer

In Power Query, remove duplicate rows based on DiagnosisCode, keeping the first occurrence.

Answer

Create a many-to-many relationship between PatientVisits and Diagnosis.

Answer

Create a composite key in PatientVisits by concatenating VisitID and DiagnosisCode, and relate to Diagnosis on that key.

Question 19

You are a data analyst for a retail company. You have a Power BI semantic model that includes a fact table named Sales with columns: Date, ProductID, StoreID, Quantity, and Amount. You also have dimension tables: Product, Store, and Date. The Date table is marked as a date table. You need to create a measure that calculates the running total of sales amount over the last 12 months, including the current month. The measure should be dynamic based on the filter context. Which DAX expression should you use?

Accepted Answer

CALCULATE(SUM(Sales[Amount]), DATESINPERIOD(Date[Date], MAX(Date[Date]), -12, MONTH)). Option A uses DATESINPERIOD with a -12 month offset, which correctly calculates a rolling 12-month total. Option B uses PARALLELPERIOD for parallel period comparison, not rolling total. Option C uses DATESBETWEEN with a fixed start date, which is not dynamic. Option D uses TOTALYTD which calculates year-to-date, not rolling 12 months.

Answer

CALCULATE(SUM(Sales[Amount]), DATESBETWEEN(Date[Date], DATE(2024,1,1), MAX(Date[Date])))

Answer

CALCULATE(SUM(Sales[Amount]), PARALLELPERIOD(Date[Date], -12, MONTH))

Answer

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

Question 20

You are building a Power BI report for a multinational corporation. The data model includes a fact table named Orders with columns: OrderID, CustomerID, OrderDate, ProductID, Quantity, UnitPrice, and Discount. The Customer dimension contains columns: CustomerID, CustomerName, Country, and Segment. The Product dimension contains: ProductID, ProductName, Category, Subcategory, and Price. You need to create a calculated column in the Orders table that calculates the net amount after discount for each order line (Quantity * UnitPrice * (1 - Discount)). You also need to ensure that the column is stored in the model for high-performance filtering. Which DAX expression should you use?

Accepted Answer

NetAmount = Orders[Quantity] * Orders[UnitPrice] * (1 - Orders[Discount]). Option A uses the correct syntax and creates a calculated column that will be materialized in the model. Option B is a measure, not a calculated column. Option C is a measure. Option D uses SUMX, which is for measures and would not create a stored column.

Answer

NetAmount = SUMX(Orders, Orders[Quantity] * Orders[UnitPrice] * (1 - Orders[Discount]))

Answer

NetAmount = CALCULATE(SUM(Orders[Quantity]) * SUM(Orders[UnitPrice]) * (1 - SUM(Orders[Discount])))

Answer

NetAmount = Orders[Quantity] * Orders[UnitPrice] - Orders[Discount]