PL-300 Model the data Practice Test 16 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The query returns total sales per year and category for Amount > 100. The DAX query uses SUMMARIZECOLUMNS 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 2

You are reviewing the partition configuration for a Power BI Import model as shown in the exhibit. The table Sales is partitioned by year. You need to modify the model to improve incremental refresh performance. What change should you make?

Accepted Answer

Configure incremental refresh policy. Configuring an incremental refresh policy (Option B) is the correct approach because it automatically manages partition creation and refresh for the Sales table based on a date/time column. This improves performance by refreshing only the most recent data (e.g., last 5 years) while keeping historical partitions unchanged, reducing refresh time and resource consumption compared to manual yearly partitions.

Answer

Increase the number of partitions to monthly

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Remove all partitions and load data as a single table

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Change the storage mode to DirectQuery

Question 3

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

Use SUMMARIZECOLUMNS with 'Date'[Month] and COUNTROWS of Orders. To create a calculated table that shows total orders shipped per month, you can directly group by the month of ShipDate using SUMMARIZECOLUMNS. Since the Orders table already contains a ShipDate column, a separate date table is not required for this aggregation. Option C (creating a date table) is unnecessary here; you can simply extract the month from ShipDate within the grouping. Option D correctly uses SUMMARIZECOLUMNS with 'Date'[Month] and COUNTROWS of Orders. Note: If the 'Date' table is not already present, you would need to create it, but the question asks for steps to create a calculated table that contains one row per month with totals. The most direct step is to use SUMMARIZECOLUMNS with the month column from the Orders table (or a date table if available). However, among the given options, only D is a required step.

Answer

Use CALENDARAUTO in a measure

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Use the GROUPBY function

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Create a date table using CALENDAR or CALENDARAUTO

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Use the VALUES function to get unique months

Question 4

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.. 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.

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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 5

You are modeling data from an Azure SQL Database into Power BI. The source table 'Sales' contains 10 million rows. You need to ensure that the data model supports fast query performance for a report that shows sales by month and product category. The report uses a slicer for year. What is the best practice for improving performance?

Accepted Answer

Create an aggregate table in Power BI that pre-aggregates sales by month and product category.. Creating an aggregate table in Power BI that pre-aggregates sales by month and product category drastically reduces the number of rows the report must scan, from 10 million to a much smaller set of aggregated rows. This enables fast query performance for the slicer and visual-level filters, as Power BI can leverage the aggregate table via its aggregation feature, which automatically redirects queries to the pre-summarized data when possible.

Answer

Disable the auto-date/time feature.

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Increase the data load frequency to every 15 minutes.

Answer

Use DirectQuery mode to query the source database directly.

Question 6

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

Accepted Answer

A composite model can combine DirectQuery and Import tables.. 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.

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

Answer

Calculated tables are not supported in composite models.

Question 7

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.. 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.

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

Question 8

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.. 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 9

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.. 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.

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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 10

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.. 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 11

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.. 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.

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Use Excel formulas in a source query.

Question 12

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.. In Power BI, relationships require matching data types on both sides of the key columns. If one 'ProductID' column is integer and the other is text, the relationship engine cannot resolve the join because the data types are incompatible. Changing both columns to the same data type—either in Power Query (recommended for performance) or in the model view—resolves this mismatch and allows a valid relationship to be created.

Answer

Merge the two tables into one in Power Query.

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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 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.. 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 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. 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 15

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

Accepted Answer

Replace text-based relationship columns with integer keys.. 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.

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Hide columns that are not used in reports.

Answer

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