PL-300 Timed Practice Test 5 — 10 Questions

Question 1

You are connecting Power BI to an Azure SQL Database. The database contains a table with 10 million rows. You need to minimize the initial load time for the report. What should you do?

Accepted Answer

Use DirectQuery mode. DirectQuery mode sends queries directly to the Azure SQL Database, avoiding the need to import 10 million rows into the Power BI model. This eliminates the initial data transfer and processing overhead, significantly reducing the initial load time for the report.

Answer

Use Import mode and create aggregations

Answer

Use Dual mode

Answer

Use Import mode with Incremental refresh

Question 2

You are using Power Query to combine data from multiple Excel files in a SharePoint folder. Each file has a sheet named 'Sales'. The columns across files are identical but occasionally a file has extra columns. You need to ensure the combined table contains only the common columns across all files. Which Power Query step should you use?

Accepted Answer

Use Table.ColumnsOfType to filter common columns. Option D is correct because Table.ColumnsOfType allows selecting only columns that appear in all tables. Option A is wrong because Table.Combine appends all columns, creating nulls for missing columns. Option B is wrong because Merge queries are for joining, not combining. Option C is wrong because Remove Other Columns removes columns not selected; you need to identify common ones first.

Answer

Use Table.Combine with the 'Table1' parameter

Answer

Use Remove Other Columns on the first file's columns

Answer

Use Merge Queries as New

Question 3

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])). Option C is correct because 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.

Answer

Use a fixed start and end date in the CALENDAR function

Answer

Create a disconnected date table

Answer

Enable Auto Date/Time in the model

Question 4

You are importing data from a CSV file that contains a column 'OrderDate' with values in the format 'YYYY-MM-DD'. Power Query automatically detects the data type as Date. You need to ensure that the data type remains Date even if the source file later changes the date format to 'MM/DD/YYYY'. What should you do?

Accepted Answer

Set the data type to Date using the 'Change Type' step in Power Query. Option A is correct because setting the data type explicitly in Power Query forces conversion regardless of source format. Option B is wrong because changing source locale doesn't fix format changes. Option C is wrong because using sample file doesn't enforce type. Option D is wrong because split column is unnecessary.

Answer

Split the column into year, month, day and then combine

Answer

Use the 'Using Locale' transformation

Answer

Change the regional settings of the Power Query editor

Question 5

You are importing data from a folder containing multiple CSV files with the same structure. You want to combine all files into a single table, but only include files that have been modified in the last 7 days. What Power Query transformation should you use?

Accepted Answer

Filter the folder contents by 'Date Modified' before combining. Option D is correct because filtering by date modified before combining reduces data volume. Option A is wrong because combining then filtering loads all data first. Option B is wrong because sample file is for structure. Option C is wrong because merging is not for appending.

Answer

Use 'Sample File' and manually filter

Answer

Use 'Merge Queries' with the folder

Answer

Combine files using 'Combine & Load', then filter by date modified

Question 6

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.

Answer

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

Answer

Enable query folding on the SQL Server source.

Answer

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

Question 7

You are designing a data model for a sales analysis report. The source data includes a table with Sales, Product, Customer, and Date information in a single flat table. You need to transform this into a star schema with separate dimension tables. Which THREE actions should you take?

Accepted Answer

Create a Product dimension table by selecting distinct product attributes and assigning a surrogate key.. Option A is correct because creating a Product dimension table by selecting distinct product attributes and assigning a surrogate key is a fundamental step in building a star schema. This process normalizes the flat table by separating product-related attributes into a dedicated dimension, which reduces redundancy and improves query performance in Power BI. The surrogate key (e.g., an integer identity column) ensures a stable, unique identifier for each product, independent of any changes in the source system's natural keys.

Answer

Merge the Customer and Product dimensions into one table to reduce the number of tables.

Answer

Keep all columns in a single table to avoid relationships.

Question 8

You are designing a data model for a retail company. The source system has a Sales table with columns: Date, StoreID, ProductID, SalesAmount. You need to create a date dimension table that includes all dates from the Sales table. Which DAX expression should you use to create the date table?

Accepted Answer

DateTable = CALENDARAUTO(). Option C is correct because `CALENDARAUTO()` automatically generates a contiguous date table spanning all dates present in the model, including those from the Sales table. This function scans all date columns in the model and returns a single column of dates from the earliest to the latest, which is the simplest way to create a date dimension that covers the full range of transaction dates without needing explicit MIN/MAX references.

Answer

DateTable = VALUES(Sales[Date])

Answer

DateTable = CALENDAR(MIN(Sales[Date]), MAX(Sales[Date]))

Answer

DateTable = DISTINCT(Sales[Date])

Question 9

Which TWO of the following are valid methods to transform data in Power Query?

Accepted Answer

Use 'Unpivot Columns' to turn selected columns into attribute-value pairs.. Options B and D are correct. Option A is wrong because 'Group By' is a transformation, not a merge. Option C is wrong because 'Append' is a transformation. Option E is wrong because 'Split Column' is a transformation.

Answer

Use 'Split Column' to divide a column into multiple columns based on a delimiter.

Answer

Use 'Merge Queries' to combine rows from multiple tables based on a key.

Answer

Use 'Append Queries' to combine columns from two tables.

Question 10

You are designing a data model for a sales analysis report. The source data includes a Sales table with columns: OrderID, CustomerID, ProductID, OrderDate, Quantity, and UnitPrice. You also have a Customers table and a Products table. Which approach best optimizes query performance and storage?

Accepted Answer

Create a star schema with Sales as a fact table and Customers and Products as dimension tables.. Option A is correct because a star schema optimizes query performance and storage in Power BI by separating transactional data (Sales fact table) from descriptive attributes (Customers and Products dimension tables). This reduces data duplication, improves compression, and enables efficient aggregations and filter propagation via one-to-many relationships, which is the recommended modeling approach for analytical workloads.

Answer

Create separate fact tables for each dimension.

Answer

Create a single flat table by joining all columns from Sales, Customers, and Products into one table.

Answer

Create a snowflake schema by normalizing Customers into multiple related tables.