PL-300 Practice Test 10 — 10 Questions

Question 1

Which TWO are required components of a Power BI Paginated Report?

Accepted Answer

A dataset. In Power BI Paginated Reports, every report must be bound to at least one dataset (option C), which defines the fields and query results the report's tables, matrices, and other regions render. That dataset in turn must be backed by a data source (option D), the connection object that specifies the provider and connection string used to retrieve the data. Together, the data source and dataset form the minimum required data-retrieval components of a paginated report. A subscription (option A) is an optional delivery mechanism for scheduled report distribution, not a required report component. A map visual (option B) is just one optional report item type, and a query parameter (option E) is an optional filtering/parameterization feature, neither of which is mandatory for a paginated report to exist.

Answer

A subscription

Answer

A map visual

Answer

A query parameter

Question 2

You are a business analyst at a manufacturing company. You receive weekly CSV files from different plants. Each file contains columns: PlantID, Date, ProductID, UnitsProduced, and ScrapUnits. However, the files sometimes have missing values in the ScrapUnits column, and occasionally there are duplicate rows (same PlantID, Date, ProductID). You need to prepare a clean dataset for reporting. The requirements are:
1. Combine all CSV files from a folder into a single table.
2. Replace null values in ScrapUnits with 0.
3. Remove duplicate rows based on PlantID, Date, and ProductID, keeping the first occurrence.
4. Ensure the data types are appropriate (e.g., Date as date, UnitsProduced as whole number).

Which sequence of Power Query steps should you use?

Accepted Answer

Connect to folder, combine files, replace nulls, remove duplicates, set data types.. It follows the proper Power Query order: connect to the folder and combine the CSV files first, then replace null values in ScrapUnits with 0, then remove duplicate rows based on PlantID, Date, and ProductID keeping the first occurrence, and finally set the data types (Date as date, UnitsProduced as whole number). Replacing nulls before removing duplicates ensures that duplicate detection and the retained first row are based on complete data rather than nulls that could later change values. Setting data types last avoids type-conversion errors on text values that may still contain nulls or duplicates. Option A replaces nulls before combining, which is inefficient and may not apply consistently across all files; Option B sets data types before replacing nulls, which can cause conversion errors; Option C removes duplicates before replacing nulls, so null ScrapUnits values could affect which row is kept.

Answer

Connect to folder, replace nulls in each file, combine files, remove duplicates, set data types.

Answer

Connect to folder, combine files, set data types, replace nulls, remove duplicates.

Answer

Connect to folder, combine files, remove duplicates, replace nulls, set data types.

Question 3

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 on intermediate queries that are only used for reference prevents those staging tables from being materialized into the dataset, reducing the amount of data processed and stored during refresh. Option D is correct because filtering rows at the source (for example, using query folding or a WHERE clause in the source query) reduces the volume of data transferred and loaded, which directly speeds up refresh. Option A is not correct because merging all queries into one can create unnecessary complexity and may break query folding rather than improve performance. Option B is not correct because calculated columns in Power Query are computed during refresh and can actually slow it down compared to DAX calculated columns, which are computed at query time. Option E is not correct because keeping all source columns increases data volume and memory usage, which harms rather than improves refresh performance.

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 4

You have a Power BI data model with a 'Sales' fact table and a 'Date' dimension. You need to create a calculated column in the 'Sales' table that shows the fiscal year based on a 'Date' column. The fiscal year starts on July 1. Which DAX expression should you use?

Accepted Answer

SWITCH(TRUE(), MONTH(Sales[Date]) >= 7, YEAR(Sales[Date]), YEAR(Sales[Date])-1). It uses SWITCH with TRUE() to evaluate a logical condition: if the month of the date is July or later (MONTH >= 7), it returns the current year; otherwise, it returns the previous year. This correctly implements a fiscal year starting on July 1, as required.

Answer

YEAR(Sales[Date])

Answer

YEAR(Sales[Date]) + 1

Answer

FORMAT(Sales[Date], "YYYY")

Question 5

You are loading data from a folder containing multiple Excel files with identical structure. Some files have inconsistent column names due to manual edits. You need to ensure that all data is loaded correctly without errors. What should you do in Power Query?

Accepted Answer

Use the 'Combine Files' feature with a sample file, then in the transformation step, promote headers and rename columns using a mapping table.. The 'Combine Files' feature in Power Query uses a sample file to infer the schema, and then you can apply transformations like promoting headers and renaming columns using a mapping table to handle inconsistent column names across files. This ensures all data loads without errors by standardizing the column names before combining.

Answer

Use 'Merge Queries' to join the files based on row position.

Answer

Change the data source to a SharePoint folder and use 'Load to Data Model' directly.

Answer

In Power Query, use 'Enter Data' to manually create the schema.

Question 6

You are connecting to an Azure SQL database using DirectQuery. The database has a large table with millions of rows. Users need to see aggregated data quickly. What should you implement to improve query performance?

Accepted Answer

Create aggregations in Power BI on the large table.. Creating aggregations in Power BI on the large table allows the DirectQuery model to pre-aggregate data at the source or in Power BI, reducing the volume of data queried and improving response times for aggregated results. This is a key performance optimization for DirectQuery models with large tables, as it avoids scanning millions of rows for every query.

Answer

Increase the memory limit of the Power BI Desktop.

Answer

Use a composite model with a smaller imported table.

Answer

Add indexes to the database table.

Question 7

Which TWO actions can improve the performance of a Power BI report that uses DirectQuery?

Accepted Answer

Push transformations to the data source.. Option B is correct because with DirectQuery, every visual interaction is translated into a query against the source, so pushing transformations (filters, joins, aggregations) to the data source lets the source engine do the heavy lifting and reduces the amount of data Power BI must process. Option C is correct because reducing the number of columns in the query shrinks the result set returned to Power BI, lowering network transfer and memory/processing overhead, which directly improves DirectQuery report performance. Option A is not correct because calculated columns are computed and stored in the model, and in DirectQuery they can force row-by-row processing or unsupported query patterns rather than improving source-side performance. Options D and E are not correct because bidirectional cross-filtering (cross-filter direction set to both) adds extra query complexity and can degrade performance rather than improve it.

Answer

Create calculated columns instead of measures.

Answer

Use bidirectional cross-filtering.

Answer

Increase cross-filter direction to both.

Question 8

You have a Power BI semantic model with a date table that has a 1:* relationship to a Sales table. You need to create a measure that shows the number of sales transactions for the last 30 days. The date table is marked as a date table. Which DAX expression should you use?

Accepted Answer

CALCULATE(COUNTROWS(Sales), DATESINPERIOD('Date'[Date], MAX('Date'[Date]), -30, DAY)). DATESINPERIOD('Date'[Date], MAX('Date'[Date]), -30, DAY) returns a 30-day rolling window ending at the last date in the current filter context, which is exactly what a 'last 30 days' measure requires when the model has a marked date table. Wrapping it in CALCULATE with COUNTROWS(Sales) then counts the Sales rows whose related Date values fall in that window. Option B uses DATESBETWEEN with TODAY()-30 and TODAY(), which hard-codes the window to the actual current date rather than the model's latest date and can return blank or wrong results if the data is not current. Option C uses PREVIOUSMONTH, which returns the entire prior calendar month, not a 30-day period. Option D uses DATEADD with -30 DAY, which shifts the current filter context back 30 days rather than returning a 30-day range, so it does not produce a rolling 30-day count.

Answer

CALCULATE(COUNTROWS(Sales), DATESBETWEEN('Date'[Date], TODAY()-30, TODAY()))

Answer

CALCULATE(COUNTROWS(Sales), PREVIOUSMONTH('Date'[Date]))

Answer

CALCULATE(COUNTROWS(Sales), DATEADD('Date'[Date], -30, DAY))

Question 9

You have a Power BI report that uses a live connection to an Azure Analysis Services (AAS) tabular model. You need to add a new measure to the report. What should you do?

Accepted Answer

Add the measure to the AAS tabular model using SQL Server Management Studio (SSMS) or Visual Studio.. With a live connection to an Azure Analysis Services tabular model, Power BI Desktop is only a thin client and cannot create or edit model objects such as measures, calculated columns, or calculated tables. Measures must be authored in the source tabular model itself, so option C is correct: you add the measure to the AAS model using SSMS (via an MDX/DAX query window or Tabular Model Scripting Language) or Visual Studio with the Analysis Services projects extension, then it becomes available to the live-connected report. Option A is wrong because calculated columns cannot be created in Power BI Desktop against a live-connected model. Option B is wrong because Power BI Desktop cannot modify the AAS model directly in a live connection. Option D is wrong because calculated tables also cannot be added in Power BI Desktop when using a live connection.

Answer

Use DAX to create a new calculated column in Power BI Desktop.

Answer

Modify the AAS model directly from Power BI Desktop.

Answer

Create a new calculated table in Power BI Desktop.

Question 10

You are preparing data for a Power BI report. The source data contains a 'CustomerName' column with values like 'John, Doe'. You need to split this column into two columns: 'FirstName' and 'LastName'. The comma is used as a delimiter, but some names have a space after the comma. Which split method should you use?

Accepted Answer

Split by delimiter using comma, then use 'Trim' to remove extra spaces. Splitting by comma and then trimming extra spaces handles the inconsistent spacing after the comma (e.g., 'John, Doe' vs 'John, Doe'). Power Query's 'Split Column by Delimiter' using comma will separate the values, and the subsequent 'Trim' step removes leading/trailing spaces from the resulting columns, ensuring clean 'FirstName' and 'LastName' values without manual cleanup.

Answer

Split by number of characters using a fixed width

Answer

Split by delimiter using semicolon

Answer

Split by delimiter using comma, using 'Left-most delimiter'