PL-300 Practice Test 10 — 10 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 importing data from a CSV file that contains a column with mixed data types (numbers and text). Power BI automatically assigns the data type as Text. You need to perform numerical aggregations on this column. What should you do?

Accepted Answer

In Power Query Editor, change the data type of the column to Whole Number or Decimal Number.. Changing the column's data type to Whole Number or Decimal Number in Power Query Editor will force Power BI to interpret the numeric values as numbers, enabling aggregations like SUM or AVERAGE. Power Query Editor provides a robust transformation environment where data type changes are applied during the load process, ensuring the column is treated as numeric for all downstream calculations. This approach is more efficient and reliable than using DAX conversions, as it avoids the overhead of calculated columns and leverages Power Query's native type detection and error handling.

Answer

Split the column using a delimiter to separate numbers from text.

Answer

Create a relationship with a numeric table to enable aggregation.

Answer

Create a calculated column in DAX using VALUE() to convert the text to numbers.

Question 3

You are reviewing the deployment configuration for a Power BI dataset. The exhibit shows a JSON snippet of the dataset settings. You need to ensure that data is refreshed twice a day at 6:00 AM and 6:00 PM UTC. However, the refresh fails at both scheduled times. What is the most likely cause?

Accepted Answer

The data source uses Integrated Security (SSPI) which is not supported for scheduled refresh.. Scheduled refresh in Power BI requires a gateway to connect to on-premises data sources, and when the data source uses Integrated Security (SSPI), the gateway cannot delegate credentials for scheduled refresh. SSPI relies on the user's interactive Windows authentication context, which is not available during unattended scheduled refresh operations. This causes the refresh to fail at both scheduled times.

Answer

The refresh schedule uses UTC but the data source is in a different time zone.

Answer

The dataset has DirectQuery enabled, which prevents Import mode refresh.

Answer

The gateway ID is incorrect.

Question 4

You are building a Power BI report for a manufacturing company. You have a large fact table with 50 million rows in Azure SQL Database. You need to minimize the data refresh time and ensure that only new or changed rows are loaded. The source table has a LastModifiedDate column. What should you do?

Accepted Answer

Configure incremental refresh on the table using the LastModifiedDate column.. Incremental refresh in Power BI allows you to load only new or changed rows from a large fact table by filtering on a date/time column such as LastModifiedDate. This minimizes data refresh time by avoiding a full reload of all 50 million rows, and it leverages the source system's ability to efficiently query only the modified data. Power Query pushes the filter logic to Azure SQL Database via query folding, ensuring optimal performance.

Answer

Enable query folding in Power Query to push filters to the source.

Answer

Schedule a full refresh every hour.

Answer

Create a Power BI dataflow that performs a full load and then use that dataflow as a source.

Question 5

A company has a Power BI semantic model that uses DirectQuery to a SQL Server database. The model includes a large fact table with 100 million rows. Users are experiencing slow report performance. Which TWO actions should the developer take to improve query performance?

Accepted Answer

Create indexes on columns used in filters and relationships.. In DirectQuery mode, incremental refresh is not supported (option A is incorrect). Creating indexes on columns used in filters and relationships (option B) speeds up query execution on SQL Server. Removing unused columns from the fact table (option C) reduces the amount of data transferred per query. Hiding columns (option D) does not affect the data retrieved by queries. Adding calculated columns (option E) increases query overhead and degrades performance.

Answer

Configure incremental refresh to limit data retrieved per query.

Answer

Hide columns that are not needed in reports.

Answer

Add calculated columns to precompute aggregations.

Question 6

You are reviewing the relationships in a Power BI data model as shown in the exhibit. The model has tables: Sales, Product, Customer, and Category. You need to evaluate the performance impact of the current configuration. Which relationship is most likely to cause performance issues?

Accepted Answer

The relationship between Product and Category. The relationship between Product and Category is most likely to cause performance issues because it is a many-to-many relationship without a bridge table. In Power BI, many-to-many relationships require the engine to materialize cross-join-like intermediate tables in memory, increasing query complexity and reducing performance. This is especially problematic when filtering or aggregating across these tables, as the VertiPaq engine must resolve ambiguity by creating additional internal tables.

Answer

All relationships are equally efficient

Answer

The relationship between Sales and Customer

Answer

The relationship between Sales and Product

Question 7

A company has a Power BI dataset that contains a date table with columns: Date, Year, Month, Quarter, Day. The data model also includes a sales fact table with a SalesDate column. To enable time intelligence functions like TOTALYTD, what is the minimum requirement for the relationship between these tables?

Accepted Answer

Create a one-to-many relationship from the date table to the sales table and mark the date table as a date table.. Time intelligence functions like TOTALYTD require a properly configured date table marked as a date table, with a one-to-many relationship from the date table to the sales fact table. This ensures that the date table provides a continuous, unique set of dates that Power BI can use for time-based calculations, and marking it as a date table enables the engine to recognize it as the primary date dimension for time intelligence.

Answer

Create a calculated column in the sales table to extract the date part and relate it to the date table.

Answer

Create a many-to-many relationship between the date table and the sales table.

Answer

Create a one-to-many relationship from the sales table to the date table with bidirectional cross-filtering.

Question 8

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.

Answer

Use Excel formulas in a source query.

Question 9

You have a Power BI data model with a Sales table and a Product table. You want to create a measure that calculates the percentage of total sales for each product category. Which DAX pattern should you use?

Accepted Answer

DIVIDE(SUM(Sales[Amount]), CALCULATE(SUM(Sales[Amount]), ALL(Product[Category]))). It uses DIVIDE for safe division and CALCULATE with ALL(Product[Category]) to remove the filter context on the Product[Category] column, allowing the measure to compute the percentage of total sales for each product category. This pattern ensures that the denominator represents the total sales across all categories, while the numerator respects the current filter context for the specific category.

Answer

SUM(Sales[Amount]) / CALCULATE(SUM(Sales[Amount]), REMOVEFILTERS(Sales))

Answer

SUM(Sales[Amount]) / SUM(Sales[Amount])

Answer

DIVIDE(SUM(Sales[Amount]), CALCULATE(SUM(Sales[Amount]), ALLSELECTED()))

Question 10

You are using the above KQL query as a source in Power Query for a Power BI semantic model. The query runs successfully but takes a long time to execute. You need to improve performance. What should you do?

Accepted Answer

Use the 'Run KQL command' option in Power Query to pass the query directly.. Using the 'Run KQL command' option in Power Query sends the entire KQL query directly to Azure Data Explorer (or Kusto) for execution, allowing the Kusto engine to process and filter data at the source. This minimizes data transfer and leverages Kusto's optimized query engine, significantly improving performance compared to pulling all data into Power Query for transformation.

Answer

Add additional transformations in Power Query to reduce rows.

Answer

Enable query folding to push the query to the Kusto source.

Answer

Disable query folding to improve performance.