PL-300 Practice Test 49 — 15 Questions

Question 1

A Power BI developer is designing a semantic model that will be used by multiple departments. The developer wants to ensure that the model follows best practices for performance and usability. Which TWO actions should the developer take?

Accepted Answer

Use a star schema with dimension and fact tables.. A star schema organizes data into dimension and fact tables, reducing data redundancy and improving query performance by enabling efficient aggregations and filter propagation. This design aligns with Power BI best practices for semantic models, as it minimizes the number of tables and relationships, leading to faster DAX calculations and better compression.

Answer

Use calculated columns instead of measures for aggregations.

Answer

Combine all data into a single flat table to simplify the model.

Answer

Enable bidirectional cross-filtering between all tables.

Question 2

Drag and drop the steps to configure Row-Level Security (RLS) in Power BI Desktop into the correct order.

Question 3

Match each Power BI component to its purpose.

Question 4

A Power BI report contains a table with a column 'Date' of type date. The report users need to filter data by fiscal year, which starts on April 1. What is the best practice to support this requirement during data preparation?

Accepted Answer

Create a separate date table in Power Query with a fiscal year column.. Option A is correct because creating a separate date table in Power Query with a fiscal year column is the best practice for handling fiscal year filtering. This approach ensures the date dimension is independent of fact tables, supports star schema design, and allows you to define fiscal year logic (starting April 1) directly in M code during data preparation, which is more efficient and maintainable than using DAX calculated columns or tables.

Answer

Split the date column into year, month, and day columns.

Answer

Use a DAX calculated table to generate fiscal year dates.

Answer

Add a calculated column in the existing table using DAX.

Question 5

You are building a Power BI report that uses a large fact table with 100 million rows. The data source is a SQL Server view that filters data by a date range. You want to minimize the data loaded into the model while maintaining the ability to query any date range later. What should you do?

Accepted Answer

Configure Incremental refresh with Detect data changes on the date column.. Option B is correct because Incremental refresh allows you to partition the fact table by date, loading only recent data into the model while keeping the ability to query historical data on demand. By enabling 'Detect data changes', Power BI can efficiently identify and refresh only the partitions that have changed, minimizing data load and refresh time. This approach maintains query performance for any date range because historical partitions remain available in the model without being re-imported.

Answer

Switch to DirectQuery mode and rely on the source database for filtering.

Answer

Use Import mode and apply a date filter in Power Query to limit rows.

Answer

Create a calculated table with only the required columns.

Question 6

Which TWO actions can improve data refresh performance in Power BI?

Accepted Answer

Disable load for intermediate queries used only for reference.. Option C is correct because disabling load for intermediate queries used only as reference steps prevents Power BI from materializing those tables in the data model. This reduces memory consumption and refresh time, as the engine skips loading data that isn't needed for reports or further transformations.

Answer

Merge all queries into a single query.

Answer

Add calculated columns in Power Query instead of DAX.

Answer

Keep all columns from the source data to avoid re-importing.

Question 7

You have a Power BI dataset that combines sales data from two Excel files: Sales2023.xlsx and Sales2024.xlsx. Both files have the same schema. You need to combine them into a single table without duplicating rows. What is the best approach in Power Query?

Accepted Answer

Use Append Queries.. Option C is correct because Append Queries in Power Query is specifically designed to combine rows from two or more tables with the same schema into a single table, stacking them vertically without duplicating rows. This operation is performed in the Power Query Editor (M language) and is the standard approach for unioning data from multiple sources during the data preparation phase, before loading into the Power BI data model.

Answer

Use Union in DAX.

Answer

Use Group By to summarize data.

Answer

Use Merge Queries as a new query.

Question 8

You are creating a Power BI report from a SQL Server database that contains a table Orders with columns: OrderDate, CustomerID, ProductID, Quantity, UnitPrice. You need to build a star schema. Which THREE tables should you create? (Choose three.)

Accepted Answer

Date dimension table with date attributes.. In a star schema, dimension tables contain descriptive attributes (e.g., dates, products, customers) and are connected to a central fact table. For the Orders table, a Date dimension (B) is essential for time-based analysis, a Product dimension (C) provides product details, and a Customer dimension (D) stores customer attributes. These three dimensions normalize the data and enable efficient slicing and dicing in Power BI.

Answer

OrderDetails table with line items.

Answer

Sales fact table with measures.

Question 9

Refer to the exhibit. You load the Sales table into Power BI. You need to calculate the total net sales after discount (SalesAmount * (1 - Discount)). However, some rows have Null in the Discount column. What is the correct DAX measure?

Accepted Answer

Net Sales = SUMX(Sales, Sales[SalesAmount] * (1 - COALESCE(Sales[Discount], 0))). Option B is correct because it uses SUMX to iterate over each row of the Sales table, applying the calculation row-by-row, and uses COALESCE to replace any NULL in the Discount column with 0, ensuring the discount is correctly treated as zero for rows without a discount. This avoids the aggregation errors that occur when using SUM on the Discount column directly.

Answer

Net Sales = SUM(Sales[SalesAmount]) * (1 - SUM(Sales[Discount]))

Answer

Net Sales = SUMX(Sales, Sales[SalesAmount] * (1 - Sales[Discount]))

Answer

Net Sales = SUMX(Sales, DIVIDE(Sales[SalesAmount], 1 - Sales[Discount]))

Question 10

A company uses Power BI to analyze sales data from a SQL Server database. The database contains a table 'Sales' with 10 million rows. The business analysts need to create daily reports that aggregate sales by region and product category. To optimize report performance, which data preparation technique should be applied?

Accepted Answer

Perform aggregation in SQL before importing.. Option D is correct because performing aggregation in SQL before importing reduces the data volume from 10 million rows to a much smaller aggregated result set. This minimizes memory consumption and speeds up report rendering in Power BI, as the heavy lifting is done on the SQL Server engine rather than in Power Query or the Power BI data model.

Answer

Increase the row limit in Power Query to load all rows.

Answer

Remove unused columns from the query.

Answer

Import the entire table and aggregate in Power BI.

Question 11

A data analyst is preparing data from multiple Excel files stored in SharePoint. Each file has the same structure but different data. Which THREE steps are necessary to combine these files into a single table in Power Query?

Accepted Answer

Create a connection to the SharePoint folder.. Option B is correct because connecting to the SharePoint folder allows Power Query to access all files in the folder as a single data source. This is the foundational step for the 'Combine Files' workflow, which then uses a sample file to infer the schema and automatically applies transformations to all matching files.

Answer

Use the 'Merge Queries' feature to join the files.

Answer

Individually import each file and append them manually.

Question 12

A company has a Power BI dataset that imports data from a SQL Server database. The dataset includes a table with 10 million rows. The data model uses a single table and does not include any calculated columns or measures. The report users report that the dataset refresh takes too long. Which action should you take to improve refresh performance?

Accepted Answer

Remove unused columns from the table in Power Query.. Removing unused columns from the table in Power Query reduces the amount of data loaded into the Power BI dataset. With 10 million rows, every unnecessary column adds significant I/O and memory overhead during refresh. This directly improves refresh performance by minimizing the data volume transferred and processed.

Answer

Increase the scheduled refresh frequency to every 15 minutes.

Answer

Enable Query Folding on all steps in Power Query.

Answer

Change the storage mode to DirectQuery.

Question 13

Drag and drop the steps to create a calculated table in Power BI Desktop using DAX into the correct order.

Question 14

Match each DAX function to its description.

Question 15

Match each visualization type to its typical use case.