Medium PL-300 practice questions

You are finalizing a Power BI report that will be consumed by users who rely on keyboard navigation and screen readers. On the main page, several decorative shapes and a company logo image are being read aloud by the screen reader, and users must tab through them before reaching the key visuals. You need to ensure that keyboard users can move directly to the meaningful visuals in a logical sequence without the decorative elements interrupting them. What should you do?

You import a Product dimension table into Power Query. The source has no reliable unique identifier because the natural business key (ProductCode) contains occasional duplicates caused by legacy data entry, and you must relate this dimension to a large Sales fact table. You want a guaranteed unique key to use as the one-side of the relationship without altering the existing ProductCode values. What is the most appropriate step to take in Power Query?

You have a clustered column chart showing monthly Total Revenue by Product Category. The regional director notices that revenue dropped sharply from Q2 to Q3 and asks which factors contributed most to the decline across all your dimensions (Region, Channel, and Salesperson). You want Power BI to automatically rank and visualize the segments that drove the decrease without manually building additional visuals. Which built-in capability should you use?

A sales manager is reviewing a clustered column chart that shows total revenue by month. Revenue jumped sharply from April to May, and the manager wants Power BI to automatically break down which categories and regions contributed most to that specific month-over-month increase, without manually building additional visuals. Which built-in feature should you use?

A retail analyst has a line chart showing daily online sales over the past two years. Management wants Power BI to automatically highlight unexpected spikes and dips in the daily sales trend, along with possible explanations for each unusual value. Which built-in analytics feature should the analyst enable to meet this requirement with the least manual effort?

You are combining two annual sales extracts in Power Query. The 2023 file uses a column named 'CustomerID', while the 2024 file names the same column 'Customer_ID'. All other columns share identical names. After using Append Queries to stack the two tables, you notice the combined table contains two separate columns—'CustomerID' and 'Customer_ID'—each populated for only one year and null for the other. What is the most appropriate way to correct this before loading?

You maintain a Power BI model in DirectQuery mode over an Azure SQL Database. The model has a single many-to-one relationship between a large Sales fact table and a Product dimension. You have verified that every ProductKey in Sales exists in the Product table (the source enforces a foreign-key constraint). Query performance on visuals that combine Sales and Product is slower than expected. Which relationship setting should you enable to improve DirectQuery performance while keeping results correct?

You are building a sales report page that must show either a clustered column chart or a matrix in the same screen area, letting users switch between them with a button. Both visuals already exist and are stacked in the same location. You want the button to hide one visual and show the other without navigating to a different page. What is the most reliable way to configure this toggle?

A sales report page contains several slicers (Region, Product Category, and Fiscal Year) plus multiple visuals. Business users complain that after exploring the data they cannot quickly return the page to its default, unfiltered state. You want to add a single button that instantly clears all slicer selections and resets the page to its original view, without navigating away or reloading the report. What should you do?

You are building a sales report in Power BI. A [Total Sales] measure already exists and correctly sums the SalesAmount column. On a matrix visual, products are placed on rows and you need a measure named [% of All Products] that shows each product's sales as a percentage of the grand total across ALL products, so the value stays constant regardless of which product row it appears on. Which DAX definition should you use?

You maintain a sales model with a Sales fact table and a Date dimension. A report page is already filtered to show only the year 2023 via a page-level filter. A business user wants a card visual that always displays total sales for the full year 2024, regardless of any year filters applied elsewhere on the page. Which DAX measure will produce the correct result?

You publish a semantic model to a workspace backed by Power BI Pro (shared capacity). The model imports data from an on-premises SQL Server through a data gateway. Business users need the data updated frequently throughout the business day, and you configure scheduled refresh. When you try to add refresh times, you find you cannot schedule more than eight refreshes in a single day. What is the most appropriate action to allow more frequent scheduled refreshes?

Your organization has a widely reused sales semantic model published to a Power BI workspace. The Center of Excellence has formally reviewed and validated the model as the authoritative source for company reporting. They want it to appear with a special badge in the OneLake data hub and the Datasets list so report authors trust it as the official version. As a workspace admin, what should you do to apply the highest level of trust indicator, and who must ultimately authorize it?

You inherited a Power BI Desktop report that connects to an on-premises SQL Server. After the previous analyst left, the report fails to refresh with an authentication error because it still uses their stored Windows credentials. You need to update the connection to use a service account without rebuilding any queries. In Power BI Desktop, which action should you take?

You are building a Power BI report for a manufacturing operations team that monitors a production database. The team requires that report visuals reflect changes in the source SQL Server database within seconds, and the dataset is far too large to fit into the model's memory. The organization does not use Microsoft Fabric. Which storage mode should you choose when connecting to the data?

You are building a report for the finance department from a SQL Server table containing 500,000 rows of historical monthly ledger entries. The data is updated only once per month during a scheduled batch job. Report users complain that similar reports connected live to the database render slowly, and there is no requirement for real-time data. Which storage mode should you choose to give users the fastest possible query and visual performance?

You are analyzing a scatter chart in Power BI Desktop that plots each customer's total spend on the X axis against order frequency on the Y axis. Management wants Power BI to automatically identify natural groupings of customers with similar spending and frequency behavior so the marketing team can target distinct segments. You want to do this directly on the existing scatter chart without writing DAX or manually defining ranges. What should you do?

You import a CSV file of product records into Power Query. A column named 'ProductCode' contains mostly numeric values in the first several hundred rows, but later rows include codes like 'X1050' and 'AB22'. Power Query automatically applied a Whole Number data type, and after loading you notice many ProductCode values appear as errors. What is the best way to prevent these import errors?

You are profiling a 'ProductPrice' column in Power Query Editor. A stakeholder reports that a few products seem to have unusually high prices. You want to quickly see the exact minimum, maximum, and average values for the column, along with the count of distinct and unique values, without writing any code. Which Data Preview feature should you enable to display these summary statistics?

In Power Query Editor, you have a Customers table with an Email column containing values like "jsmith@contoso.com" and "mlee@fabrikam.net". You need to create a new column that contains only the domain portion (e.g., "contoso.com") for grouping customers by company. You are not confident writing the extraction expression manually. Which Power Query feature lets you build this transform by typing sample output values so the engine infers the M code?

You are cleaning a 2-million-row sales table in Power Query Editor. You enable the Column quality and Column distribution features to check for data issues, but you notice the statistics seem to reflect only a small subset of rows. You need the profiling results to reflect all rows in the source before you finalize your cleaning steps. What should you do?

You are cleaning a 4-million-row sales table in Power Query Editor. In the status bar you enabled 'Column quality' and 'Column profile', but a colleague warns you that the error and empty percentages you see may not reflect the true state of the whole table. By default, what is the profiling based on, and what single action lets you validate the metrics against every row?

You are cleaning a sales table in Power Query. After setting the 'UnitPrice' column to a Decimal Number type, the Column Quality pane shows 3% Error and 0% Empty. You need to investigate exactly which source values could not be converted before deciding how to handle them. What should you do first?

You import an employee table into Power Query that contains separate FirstName and LastName columns. The business wants a single FullName column formatted as 'LastName, FirstName' (for example, 'Smith, Anna'), while keeping the original two columns intact for other reports. Which Power Query approach produces the required result with the least manual effort?

You are building a Power BI report in Power Query. Your sales team exports one CSV file per month, each with identical columns (Date, ProductID, Quantity, Amount). You have 24 separate query connections, one per file, and need a single Sales fact table containing all rows from every month. Which transformation should you use to combine these queries most efficiently?