PL-300 Practice Test 50 — 15 Questions

Question 1

When importing data from a CSV file, Power Query detects that the first row contains column headers. However, the actual data starts from row 2. The analyst notices that some rows have extra columns due to commas within quoted fields. What is the most efficient way to handle this issue?

Accepted Answer

Use 'Split Column by Delimiter' and choose 'Comma' with the option to split at each occurrence.. Option C is correct because the 'Split Column by Delimiter' feature in Power Query, when configured to split at each occurrence of the comma delimiter, correctly handles commas that appear inside quoted fields. Power Query's M engine respects the standard CSV quoting rules (RFC 4180), so quoted commas are not treated as delimiters. This approach is the most efficient as it requires no manual cleanup and preserves data integrity.

Answer

Remove the top row and then split columns manually.

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Change the file encoding from UTF-8 to ANSI.

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Use 'Replace Values' to replace commas with semicolons.

Question 2

You are importing data from an Excel workbook that contains multiple worksheets. One worksheet has a column named 'Sales Amount' that contains values with different currencies (USD, EUR, JPY). You need to split the data into separate columns for each currency. Which Power Query transformation should you use?

Accepted Answer

Split Column by delimiter. The correct transformation is Split Column by delimiter because the 'Sales Amount' column contains values with different currencies (USD, EUR, JPY) that are likely stored as text with a currency symbol or code prefix (e.g., '$100', '€200', '¥300'). Splitting by a delimiter (such as a space or the first character) allows you to separate the currency code from the numeric value into distinct columns, enabling further data type conversion and analysis per currency. This is a standard data cleansing technique in Power Query for parsing multi-currency data from a single column.

Answer

Unpivot Columns

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Merge Columns

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Group By

Question 3

A data analyst needs to combine two queries in Power Query: 'Sales2023' and 'Sales2024', both with identical column structures. Which operation should the analyst use to append the rows from 'Sales2024' to 'Sales2023'?

Accepted Answer

Append Queries. The Append Queries operation in Power Query is designed to combine rows from two or more tables with identical column structures, stacking the rows of 'Sales2024' beneath those of 'Sales2023'. This is the correct method because it preserves all columns and adds data vertically, which matches the requirement to append rows.

Answer

Merge Queries

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Group By

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Pivot Column

Question 4

A Power BI dataset is configured to use Import storage mode. The dataset includes a fact table with 100 million rows and several dimension tables. The report is slow when users interact with visuals. You need to improve query performance without changing the storage mode. Which action should you take?

Accepted Answer

Create aggregations on the fact table.. Creating aggregations on the fact table allows Power BI to pre-summarize data at higher granularity levels, reducing the amount of data scanned during query execution. Since the dataset uses Import mode, aggregations leverage the in-memory columnar storage to serve queries from pre-computed tables, significantly improving visual response times without altering the storage mode.

Answer

Increase the scheduled refresh frequency.

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Reduce the number of dimension tables.

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Enable 'Load to report' for all tables.

Question 5

You are building a Power BI data model from a CSV file that contains sales transactions. The CSV file has a column named 'TransactionDate' that stores dates as text in the format 'YYYYMMDD'. You need to create a date table that includes all dates from the transaction data. Which Power Query step should you use to convert the TransactionDate column to a date data type?

Accepted Answer

Parse -> Date. Option A is correct because the 'Parse' feature in Power Query, specifically the 'Parse -> Date' transformation, is designed to convert text columns with custom date formats like 'YYYYMMDD' into a proper date data type. This method intelligently interprets the text pattern without requiring locale-specific settings, making it the most direct and reliable approach for this format.

Answer

Change Type -> Using Locale -> Date

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Split Column -> By Number of Characters

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Detect Data Type

Question 6

You are preparing data from a SQL Server database. The query includes a WHERE clause that filters rows based on a date column. You want to ensure that the filter is pushed back to the database (Query Folding). Which THREE conditions must be met?

Accepted Answer

The transformations applied must be supported for query folding.. Option A is correct because query folding in Power Query requires that all transformations applied to the source data are natively supported by the source database engine. If a transformation (e.g., certain date calculations or custom columns) cannot be translated into a SQL statement, Power Query will break folding and perform the operation locally, negating the performance benefit.

Answer

All columns from the table must be loaded to the data model.

Answer

The query must use a DirectQuery connection.

Question 7

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

Question 8

Drag and drop the steps to publish a Power BI Desktop report to the Power BI service into the correct order.

Question 9

A sales manager wants to create a report that shows year-over-year growth for each product category. They have a date table and a sales table with daily sales. Which DAX measure should they use to calculate the previous year's sales?

Accepted Answer

Sales PY = CALCULATE(SUM(Sales[Amount]), SAMEPERIODLASTYEAR(Date[Date])). Option C is correct because SAMEPERIODLASTYEAR is the most straightforward and reliable DAX function for calculating the previous year's sales when you have a proper date table. It automatically shifts the current filter context back by one year, ensuring that the measure returns the exact same period (e.g., same month and day range) from the prior year, which is ideal for year-over-year growth calculations.

Answer

Sales PY = CALCULATE(SUM(Sales[Amount]), DATEADD(Date[Date], -1, YEAR))

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Sales PY = CALCULATE(SUM(Sales[Amount]), PARALLELPERIOD(Date[Date], -1, YEAR))

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Sales PY = CALCULATE(SUM(Sales[Amount]), PREVIOUSYEAR(Date[Date]))

Question 10

A Power BI report contains a page with many visuals that use a large DirectQuery dataset. The report is slow to load. Which design change would most improve the initial page load time?

Accepted Answer

Reduce the number of visuals on the page. Reducing the number of visuals on the page directly decreases the number of queries sent to the DirectQuery source during initial page load. Each visual in a DirectQuery model typically generates at least one separate query against the source database, so fewer visuals means fewer concurrent queries and less data transferred, which significantly improves load time.

Answer

Increase the default page-level filter to limit data

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Use a slicer to let users choose what to see

Answer

Create aggregations on the dataset

Question 11

A Power BI report shows a bar chart with sales by region. When users click on a region, they expect a line chart on the same page to filter to that region's sales over time. However, the line chart does not respond to the click. What is the most likely cause?

Accepted Answer

The interaction between the bar chart and line chart is set to 'None'.. Option A is correct because in Power BI, visual interactions control how one visual affects another when clicked. By default, cross-filtering and cross-highlighting are enabled, but if the interaction between the bar chart and line chart is explicitly set to 'None', clicking a region on the bar chart will not filter or highlight the line chart. This is the most common cause when a visual fails to respond to clicks on another visual.

Answer

The line chart uses a continuous axis that cannot be filtered.

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The bar chart is not set as a slicer.

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The line chart has a legend with too many items.

Question 12

A Power BI report uses a composite model with a DirectQuery source and an imported table. The imported table has many rows, and the report is taking a long time to refresh. What is the most likely cause?

Accepted Answer

The imported table is set to Dual storage mode, causing dependency on the DirectQuery source. In a composite model, when an imported table is set to Dual storage mode, it behaves as both Import and DirectQuery. During refresh, Power BI must check the DirectQuery source for consistency, which can significantly slow down the refresh process, especially with large imported tables. This dependency on the DirectQuery source is the most likely cause of the long refresh time.

Answer

Measures are using CALCULATE with multiple filter conditions

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The DirectQuery table is set to Import storage mode

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The report contains many complex visuals that need to be rendered

Question 13

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?

Accepted Answer

Create a line chart with OrderDate on the axis and use the date hierarchy to drill down to month.. Option B is correct because 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.

Answer

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 14

You need to create a measure that calculates the year-over-year growth percentage for sales. Which DAX function should you use?

Accepted Answer

SAMEPERIODLASTYEAR. The SAMEPERIODLASTYEAR function is the correct choice for calculating year-over-year growth because it shifts the current filter context back by one year, returning a set of dates exactly one year prior. This allows you to compute the prior year's sales and then derive the growth percentage using a formula like (Current Sales - Prior Year Sales) / Prior Year Sales.

Answer

DATEADD

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PARALLELPERIOD

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PREVIOUSYEAR

Question 15

A Power BI admin applies the Azure Policy shown in the exhibit. What is the effect of this policy?

Accepted Answer

Only users in the finance workspace can read datasets. The Azure Policy in the exhibit is configured to deny the 'Read' permission on datasets for all users except those in the 'finance' workspace. This means only users who are members of the finance workspace can read datasets, while all other users are denied read access. Therefore, option D is correct because it accurately describes that only users in the finance workspace can read datasets.

Answer

No one can read any datasets

Answer

Users cannot read datasets in the finance workspace

Answer

All users can read datasets in all workspaces