PL-300 Practice Test 15 — 15 Questions

Question 1

A company has a dataset with a table 'Orders' containing columns: OrderDate, CustomerID, Amount. They want to create a visual that shows the total amount per month. Which of the following is the best approach?

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

Accepted Answer

Create a line chart with OrderDate on the axis and use the date hierarchy to drill down to month.. It leverages Power BI's built-in date hierarchy, which automatically groups OrderDate by year, quarter, month, and day. By placing OrderDate on the axis of a line chart and drilling down to the month level, you get an accurate monthly aggregation of Amount without needing any manual data transformation or calculated columns. This approach is efficient, maintains the underlying data model's integrity, and allows for easy drill-up/drill-down navigation.

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Create a pie chart with OrderDate as the legend.

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Create a table visual with OrderDate and Amount, then group by month in the visual.

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Create a bar chart with a calculated column 'Month' extracted from OrderDate and use that as the axis.

Question 2

You are configuring a Power BI dataset with incremental refresh. The above JSON shows part of the M script parameters. The dataset uses a single SQL Server data source. You need to ensure that incremental refresh works correctly. What must you do?

Accepted Answer

Rename the parameter 'StartDate' to 'RangeStart' and add a 'RangeEnd' parameter.. Incremental refresh in Power BI requires two special date/time parameters named 'RangeStart' and 'RangeEnd' (case-sensitive). The JSON snippet shows a parameter named 'StartDate', which is not recognized by the incremental refresh engine. Renaming it to 'RangeStart' and adding a 'RangeEnd' parameter allows Power BI to filter the data source query dynamically during refresh, ensuring only the changed or new rows are loaded.

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Change the connection string to use a SQL account instead of integrated security.

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Enable query folding on the SQL Server source.

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Define the parameters in the Power Query editor instead of the JSON file.

Question 3

You are importing data from a folder containing multiple Excel files with the same structure. You use Power Query's 'Combine Files' feature. Which TWO statements about this process are correct?

Accepted Answer

It uses the first file as a template for transformation.. When you use Power Query's 'Combine Files' feature, it uses the first file as a template to infer the schema and transformations. This sample file query defines how each subsequent file is processed, ensuring consistent column types and transformations across all files.

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It automatically removes duplicate rows across files.

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You can change the transformation order after combining.

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It automatically creates relationships between files.

Question 4

You are a Power BI analyst for a multinational retail company. The company's sales data is stored in an Azure SQL Database with tables: Sales (SalesID, Date, ProductID, Quantity, Amount), Products (ProductID, ProductName, Category), and Calendar (Date, Year, Month, Day). The Sales table contains 500 million rows. You are creating a Power BI report to analyze daily sales trends over the past 5 years. The report must allow users to drill from year to month to day using a hierarchy. The performance of the report is critical; users expect visuals to load within 5 seconds. The current model imports all tables without any optimization, and the date hierarchy visual takes over 30 seconds to render. You need to redesign the data model to meet the performance requirement. What should you do?

Accepted Answer

Create an aggregated table in Power Query that groups sales by date, and create a date hierarchy in the Calendar table.. Pre-aggregating sales data by date in Power Query reduces the granularity from 500 million rows to at most 1,825 rows (one per day for 5 years), which drastically improves query performance for the date hierarchy. Creating a date hierarchy in the Calendar table allows drill-down. Option B is wrong because calculated columns do not reduce row count and still require processing all 500 million rows, so performance remains poor. Option C is wrong because DirectQuery without aggregation still pushes large queries to the database, which can be slow. Option D is wrong because incremental refresh only reduces the amount of data loaded during refresh, but does not improve query performance on the existing data; the full model still contains 500 million rows.

Answer

Add calculated columns for Year, Month, and Day in the Sales table to avoid using the Calendar table.

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Switch the import mode to DirectQuery to avoid loading all data into memory.

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Implement incremental refresh policy on the Sales table to reduce the amount of data loaded.

Question 5

You have a Power BI dataset that includes a date table created using CALENDAR(). You need to ensure that the date table always covers the full range of dates present in the fact table, even after new data is loaded. What should you do?

Accepted Answer

Create the date table using CALENDAR(MIN('Fact'[Date]), MAX('Fact'[Date])). Using `CALENDAR(MIN('Fact'[Date]), MAX('Fact'[Date]))` dynamically computes the date range from the fact table's actual data. This ensures that when new data is loaded with dates outside the previous range, the date table automatically expands to cover the full range, maintaining referential integrity for time intelligence calculations.

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Use a fixed start and end date in the CALENDAR function

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Create a disconnected date table

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Enable Auto Date/Time in the model

Question 6

You are preparing data for a Power BI report. The source data contains a column with mixed data types: some values are numbers, others are text. When loading into Power Query, the entire column is typed as text. What is the likely cause?

Accepted Answer

Power Query detected that the column contains text values in some rows, so it set the data type to text. Power Query's column type detection logic examines the entire column during import. If any row contains a non-numeric value (e.g., text), Power Query defaults the entire column to text to avoid data loss or conversion errors. This is the standard behavior when mixed data types are present, regardless of the source format.

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The column was imported as text because the data source is a CSV file

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The 'Detect data type' option was disabled in Power Query settings

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The source data is stored as text in the database

Question 7

You are creating a Power BI dataset from a SQL Server data warehouse. The warehouse contains a fact table with 500 million rows and dimension tables. You need to minimize the data refresh time while ensuring that the dataset meets the reporting requirements. Which approach should you recommend?

Accepted Answer

Use Import mode but filter rows and reduce columns in Power Query to only those needed.. Importing only the necessary columns and rows reduces the data volume, which directly minimizes refresh time. With 500 million rows, Import mode is generally faster than DirectQuery for large fact tables in Power BI, as it avoids querying the source on every interaction. Filtering and column reduction in Power Query ensures the dataset remains lean while meeting reporting requirements.

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Create a composite model using DirectQuery for the fact table and Import for dimensions.

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Use DirectQuery mode for the dataset.

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Configure incremental refresh on the fact table.

Question 8

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.

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Store dimension attributes directly in fact tables for faster queries.

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Enable bi-directional cross-filtering on all relationships.

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Create calculated columns instead of Power Query transformations when possible.

Question 9

You are importing data from an Excel workbook that contains multiple sheets. Each sheet has similar structure but different data for different regions. You need to combine all sheets into a single table for analysis. What should you do?

Accepted Answer

In Power Query Editor, use Append Queries to combine the sheets.. The Append Queries operation in Power Query Editor is designed to combine rows from multiple tables or queries with similar column structures into a single table. Since each sheet in the Excel workbook contains data for different regions with the same structure, appending them stacks the rows, creating a unified dataset for analysis.

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In Power Query Editor, use Merge Queries to combine the sheets.

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Copy and paste the data from each sheet into a master sheet in Excel.

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In Power Query Editor, use Group By to consolidate the data.

Question 10

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.

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Increase the scheduled refresh frequency to every 15 minutes.

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Enable Query Folding on all steps in Power Query.

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Change the storage mode to DirectQuery.

Question 11

You have a table with a column 'FullName' that contains names in the format 'Last, First'. You need to split this column into 'LastName' and 'FirstName' columns. Which Power Query transformation should you use?

Accepted Answer

Split Column by Delimiter using comma.. The 'Split Column by Delimiter' transformation in Power Query is specifically designed to divide a single text column into multiple columns based on a specified delimiter, such as a comma. In this case, the 'FullName' column contains names in the 'Last, First' format, so splitting by a comma delimiter will correctly separate the last name and first name into two distinct columns.

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Pivot the FullName column.

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Group By the FullName column and aggregate.

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Extract first characters using 'Extract' transformation.

Question 12

Which TWO actions can improve performance of a Power BI DirectQuery model?

Accepted Answer

Ensure proper indexing in the source database. Proper indexing in the source database reduces the query execution time for DirectQuery models. DirectQuery sends queries to the source database in real-time, so efficient indexes on columns used in filters, joins, and aggregations minimize table scans and improve retrieval speed.

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Switch the model to Import mode

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Enable bidirectional cross-filtering on all relationships

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Use calculated columns with complex logic

Question 13

You are designing a data model for a report that shows sales by region and product category. The source data includes a table 'Sales' with columns: Region, Category, SalesAmount. You also have separate tables 'Regions' and 'Categories' that contain additional attributes. You need to create a star schema. What should you do with the 'Region' and 'Category' columns in the 'Sales' table?

Accepted Answer

Remove them from the Sales table and use foreign keys to link to the dimension tables. In a star schema, dimension tables (Regions, Categories) contain descriptive attributes, and the fact table (Sales) stores foreign keys referencing those dimensions. Removing the Region and Category columns from the Sales table and replacing them with foreign keys (e.g., RegionID, CategoryID) normalizes the model, reduces data redundancy, and enables efficient filtering and slicing by region and category attributes. This approach aligns with best practices for Power BI data modeling, ensuring optimal query performance and maintainability.

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Keep them in the Sales table as attributes for simplicity

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Merge the Regions and Categories tables into the Sales table

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Mark the Sales table as a date table

Question 14

You are working on a Power BI project for a marketing department. You have a CSV file with customer survey responses. The file contains columns: CustomerID, SurveyDate, Response (text with ratings from 1 to 5), Comments (free text). The file is 10 MB. You need to load the data into Power BI and create a measure that calculates the average rating. However, when you load the file, you notice that the Response column is imported as text instead of whole number. Also, there are some rows with missing values in the Response column. You need to ensure the data is correctly typed and handle missing values appropriately. What is the best approach?

Accepted Answer

Change the data type of Response to whole number in Power Query, then filter out or replace null values.. Power Query is the designated tool for data type transformations and null handling during the load phase. Changing the Response column's data type to Whole Number in Power Query automatically converts valid text numbers and flags errors, while filtering out or replacing null values ensures clean data before the data model is built. This approach follows the best practice of performing data cleansing in Power Query rather than in DAX, which would add unnecessary overhead and complexity.

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Use the 'Column from Examples' feature to create a new column with numeric values.

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In Power Query, split the Response column by delimiter and then use the first part.

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Use a DAX calculated column to convert text to number.

Question 15

You have a Power BI data model with a fact table and multiple dimension tables. You notice that many-to-many relationships cause ambiguous results. What is the best practice to resolve this?

Accepted Answer

Add a bridge table with appropriate relationships. In Power BI, many-to-many relationships between fact and dimension tables can produce ambiguous results because the model cannot determine a unique filter propagation path. The best practice is to introduce a bridge table that resolves the many-to-many relationship into two one-to-many relationships, ensuring unambiguous filter context and correct aggregations.

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Change the relationship to one-to-one

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Use a bidirectional cross-filter direction

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Create a calculated table to merge the dimensions