PL-300 Practice Test 19 — 15 Questions

Question 1

You are troubleshooting a Power BI DirectQuery model that connects to a SQL Server database. A slicer on 'Region' is slow to respond. The region column has 50 distinct values. What is the best optimization?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Create an index on the Region column in the source database. The correct option is A: create an index on the Region column in the source database. In a DirectQuery model, slicer interactions are translated into SQL queries sent to SQL Server, so filtering on Region benefits directly from a supporting index that lets the database seek matching rows instead of scanning the table, which is the most targeted fix for the slow slicer. Option B does not apply because Power BI Desktop caching does not govern DirectQuery query performance against the source. Option C would improve speed but changes the model's data freshness and architecture rather than optimizing the existing DirectQuery scenario. Option D is not a valid optimization because the 50 distinct values are legitimate business data and reducing them would alter the data model's correctness.

Answer

Increase the cache size in Power BI Desktop

Answer

Switch the model to Import mode

Answer

Reduce the number of distinct values in Region

Question 2

You want to create a measure that calculates the percentage of total sales for each product category. What DAX function should you use to get the grand total?

Accepted Answer

ALL. ALL, because it removes all filters from the specified table or column and returns the grand total across the entire dataset, which is exactly what is needed to compute each category's percentage of total sales (e.g., SUM(Sales[Amount]) / CALCULATE(SUM(Sales[Amount]), ALL(Product[Category]))). ALL is the standard DAX function for obtaining an unfiltered grand total in ratio measures. VALUES does not remove filters; it returns a single-column table of distinct values, so it cannot produce a grand total. ALLSELECTED respects filters applied outside the query (such as slicers) rather than giving the true grand total, and REMOVEFILTERS is a filter modifier used inside CALCULATE that removes filters but is not the conventional function for retrieving a grand total in this scenario.

Answer

VALUES

Answer

ALLSELECTED

Answer

REMOVEFILTERS

Question 3

Your organization uses Microsoft Power BI with Microsoft Purview for data governance. You have a dataset that contains customer data classified as 'Highly Confidential' under a sensitivity label. The compliance team requires that when this dataset is shared with external users, a Microsoft Purview data loss prevention (DLP) policy must block the sharing and notify the compliance team. You need to configure this. What should you do?

Accepted Answer

Create a DLP policy in Microsoft Purview that applies to Power BI and blocks sharing of content with the 'Highly Confidential' label.. The correct option is D: create a DLP policy in Microsoft Purview that applies to Power BI and blocks sharing of content with the 'Highly Confidential' label. Microsoft Purview DLP natively supports Power BI as a workload, so a policy scoped to Power BI can detect items carrying the specified sensitivity label and block external sharing while sending notifications to the compliance team. Option A is wrong because Defender for Cloud Apps session policies govern SaaS session activity, not Power BI item-level label-based sharing. Option B is wrong because Microsoft Sentinel is a SIEM/SOAR platform for monitoring and alerting, not for enforcing DLP blocking. Option C is wrong because disabling sharing at the workspace level is a blunt control that does not target the 'Highly Confidential' label or notify compliance.

Answer

Use Microsoft Defender for Cloud Apps to create a session policy that blocks sharing.

Answer

Configure Microsoft Sentinel to monitor and block sharing events.

Answer

In the Power BI admin portal, disable sharing for workspaces containing 'Highly Confidential' content.

Question 4

You have the above measure in a Power BI model. The 'Date' table is marked as a date table and has a relationship to Sales[OrderDate]. The measure returns blank for all months. What is the most likely cause?

Accepted Answer

The Date table does not contain all dates in the continuous range. The Date table does not contain all dates in the continuous range. Time-intelligence functions like DATESYTD require a Date table with an unbroken, continuous sequence of dates covering the full period being analyzed; if any dates are missing, the function returns blank for the affected months. A wrong filter direction (B) would typically produce incorrect aggregation rather than blank results, and DATESYTD syntax errors (C) would raise an error, not silently return blank. The table is already marked as a date table per the scenario, so D is not the cause.

Answer

The relationship is set to filter in the wrong direction

Answer

The measure uses incorrect syntax for DATESYTD

Answer

The 'Date' table is not marked as a date table

Question 5

You have a report that uses a live connection to a Power BI dataset. You want to add a table visual that shows sales by product, but you notice that you cannot add a calculated column. What is the reason?

Accepted Answer

Live connections do not support calculated columns; they must be created in the source dataset.. The correct answer is D: with a live connection to a Power BI dataset, the report is bound directly to the published dataset's model, so calculated columns cannot be authored in the report and must instead be created in the source dataset (using Power BI Desktop or the dataset's modeling features). This is because live connections only allow report-level visuals and measures, not modifications to the underlying tabular model. Option A is incorrect because row-level security is unrelated to preventing calculated columns. Option B is incorrect because table visuals are fully supported over live connections. Option C is incorrect because there is no 'Allow calculations' setting in dataset settings that enables calculated columns for live-connected reports.

Answer

Live connections require row-level security which prevents calculated columns.

Answer

Table visuals are not supported with live connections.

Answer

You need to enable 'Allow calculations' in the dataset settings.

Question 6

You need to ensure that a Power BI report is accessible to users with visual impairments. Which feature should you configure?

Accepted Answer

Add alt text to each visual.. The correct option is C: Add alt text to each visual. Alt text provides a textual description of each visual that screen readers can announce, which is the primary accessibility feature in Power BI for users with visual impairments. High-contrast themes (A) improve visual perception for low-vision users but do not convey the meaning of visuals to screen readers, and data labels (B) only expose values visually. Custom tab order (D) helps keyboard navigation but does not describe visual content to assistive technology.

Answer

Apply a high-contrast theme.

Answer

Enable data labels on all visuals.

Answer

Set custom tab order for visuals.

Question 7

You have a Power Query query that loads data from an OData source. You need to reduce the amount of data loaded into the data model. What is the best practice?

Accepted Answer

Apply a filter in Power Query before loading.. Applying filters in Power Query before loading data into the data model is the best practice for reducing data volume. Power Query pushes filters down to the OData source using OData query parameters (e.g., $filter), ensuring only the required rows are retrieved from the source. This minimizes network transfer and memory usage in the data model, aligning with the principle of early filtering in the ETL process.

Answer

Apply a filter in the data model using DAX.

Answer

Use 'Enable load' option to turn off loading for the query.

Answer

Load all data and then hide columns you don't need.

Question 8

You are designing a Power BI semantic model for an e-commerce company. You have a fact table with OrderID, CustomerID, OrderDate, and SalesAmount. You also have a Customers table with CustomerID, CustomerName, and CustomerSegment. You need to ensure that filters on CustomerSegment propagate to the SalesAmount measure. What should you do?

Accepted Answer

Create a one-to-many relationship from Customers[CustomerID] to Sales[CustomerID].. Creating a one-to-many relationship from Customers[CustomerID] to Sales[CustomerID] establishes the Customers table as the lookup (dimension) table and Sales as the related (fact) table, so filter context applied to Customers[CustomerSegment] flows down to the Sales table and affects the SalesAmount measure. This is the standard star-schema filter propagation behavior in Power BI. Option A does not propagate filters; LOOKUPVALUE merely retrieves a value row-by-row in a calculated column and cannot drive cross-table filtering. Option C would work functionally but destroys the dimensional model and is not the recommended design. Option D adds a Segment table but does not by itself create the needed relationship from Customers to Sales, so segment filters would not reach SalesAmount.

Answer

Use the LOOKUPVALUE function in a calculated column.

Answer

Merge the Customers and Sales tables into a single table in Power Query.

Answer

Create a snowflake schema by adding a separate Segment table.

Question 9

You are building a Power BI semantic model that includes a Date table. Which of the following is a best practice for creating a Date table?

Accepted Answer

Mark the date table as a date table in Power BI Desktop.. Option A is correct because marking a table as a date table in Power BI Desktop (via Table tools > Mark as date table) designates it as the model's official date table, enabling built-in time intelligence functions to work correctly and ensuring the table meets the requirements of a contiguous, unique date column. This is a documented best practice for any semantic model that uses time intelligence. Option B is wrong because relationships should be created between the date table and the date columns of fact tables, not to every date-bearing column, and a single active relationship per fact table is typical. Option C is not a best practice because CALENDARAUTO generates dates based on the model's data range, which can produce an unpredictable or incomplete range; a manually or Power Query–built date table with a fixed, contiguous range is preferred. Option D is wrong because a date table must contain a continuous, unbroken sequence of dates, not just the dates present in fact tables, so that time intelligence calculations over gaps work properly.

Answer

Create relationships from the date table to every fact table column that contains dates.

Answer

Use the CALENDARAUTO function to automatically generate dates.

Answer

Use a date table that includes only dates that exist in the fact tables.

Question 10

A Power BI report contains a table with a column 'Date' of type date. The report users need to filter data by fiscal year, which starts on April 1. What is the best practice to support this requirement during data preparation?

Accepted Answer

Create a separate date table in Power Query with a fiscal year column.. Creating a separate date table in Power Query with a fiscal year column is the best practice for handling fiscal year filtering. This approach ensures the date dimension is independent of fact tables, supports star schema design, and allows you to define fiscal year logic (starting April 1) directly in M code during data preparation, which is more efficient and maintainable than using DAX calculated columns or tables.

Answer

Split the date column into year, month, and day columns.

Answer

Use a DAX calculated table to generate fiscal year dates.

Answer

Add a calculated column in the existing table using DAX.

Question 11

You are creating a Power BI report for a sales team. The report contains a clustered bar chart showing total sales by product category. The sales manager wants to see the exact sales amount for each category without hovering over the bars. You need to configure the visual to display the values directly on the chart. What should you do?

Accepted Answer

Enable the Data labels option in the visual's formatting pane.. Enabling data labels is the direct way to show exact values on a bar chart. It displays the sales amount for each category without any user interaction. This is a built-in formatting feature that requires no additional visuals or measures.

Answer

Add a tooltip page that shows the sales amount when hovering over a bar.

Answer

Change the visual type to a table showing product category and total sales.

Answer

Add a card visual next to the chart that displays the total sales for the selected category.

Question 12

Your organization uses Power BI Premium capacity. You notice that reports are slow during peak hours. You need to identify which workspaces are consuming the most CPU resources on the capacity. What should you use?

Accepted Answer

Install and review the Power BI Premium Capacity Metrics app.. The Power BI Premium Capacity Metrics app is the correct tool because it is purpose-built to monitor Premium capacity health and provides per-workspace breakdowns of resource consumption, including CPU usage over time, so you can pinpoint which workspaces are driving load during peak hours. It surfaces metrics like CPU utilization, memory usage, query durations, and dataset refreshes at both the capacity and workspace level. In contrast, Power BI Desktop's Performance Analyzer (option A) only profiles a single report's visuals and queries on your local machine, not capacity-wide CPU consumption. Azure Monitor diagnostic logging (option C) can capture activity logs and metrics, but it requires manual setup and analysis and does not directly present workspace-level CPU rankings. The Capacity settings page in the admin portal (option D) only lets you configure capacity size, admins, and workload settings; it does not report which workspaces consume the most CPU.

Answer

Open the dataset in Power BI Desktop and view the performance analyzer.

Answer

Enable diagnostic logging in Azure Monitor for the capacity.

Answer

Check the 'Capacity settings' in the Power BI admin portal.

Question 13

Which TWO are valid Power BI workspace roles?

Accepted Answer

Viewer. In Power BI, the four valid workspace roles are Admin, Member, Contributor, and Viewer, so option A (Viewer) is correct because it grants read-only access to view reports and dashboards without editing content, and option B (Admin) is correct because it provides full control of the workspace including adding/removing users, managing permissions, and deleting the workspace. Option C (Owner) is not a Power BI workspace role—ownership concepts apply to items or capacities, not workspace membership roles. Option D (Reader) is not a workspace role; read-only access is provided by the Viewer role. Option E (Editor) is not a workspace role; edit permissions are granted via Contributor, Member, or Admin roles.

Answer

Owner

Answer

Reader

Answer

Editor

Question 14

You are a data analyst for a retail company. You have a Power BI semantic model that includes a fact table named Sales with columns: Date, ProductID, StoreID, Quantity, and Amount. You also have dimension tables: Product, Store, and Date. The Date table is marked as a date table. You need to create a measure that calculates the running total of sales amount over the last 12 months, including the current month. The measure should be dynamic based on the filter context. Which DAX expression should you use?

Accepted Answer

CALCULATE(SUM(Sales[Amount]), DATESINPERIOD(Date[Date], MAX(Date[Date]), -12, MONTH)). DATESINPERIOD(Date[Date], MAX(Date[Date]), -12, MONTH) returns a rolling 12-month window ending at the last date in the current filter context, and wrapping it in CALCULATE makes the running total dynamic as slicers or visuals change. This matches the requirement to include the current month and the preceding 11 months. Option A uses hard-coded dates from 2024-01-01, so it is not dynamic and may not cover the last 12 months. Option B uses PARALLELPERIOD, which shifts the entire period back 12 months rather than accumulating a rolling 12-month total. Option D uses TOTALYTD, which calculates a year-to-date total from the start of the fiscal or calendar year, not a rolling 12-month total.

Answer

CALCULATE(SUM(Sales[Amount]), DATESBETWEEN(Date[Date], DATE(2024,1,1), MAX(Date[Date])))

Answer

CALCULATE(SUM(Sales[Amount]), PARALLELPERIOD(Date[Date], -12, MONTH))

Answer

TOTALYTD(SUM(Sales[Amount]), Date[Date])

Question 15

You are a data analyst at a global retail company. You are building a Power BI semantic model to analyze sales performance across 50 countries. The data source is an Azure SQL Database with tables: Sales (SalesID, ProductID, StoreID, DateKey, Quantity, Amount), Products (ProductID, ProductName, CategoryID), Stores (StoreID, StoreName, CountryID), Countries (CountryID, CountryName), and Dates (DateKey, Date, Year, Month, Quarter). The model must support: 1) Hierarchical drill-down from Year to Quarter to Month. 2) Slicers for Country and Product Category. 3) Measures for Total Sales, Year-over-Year growth, and Moving Average (last 12 months). 4) The ability to filter by date range (e.g., last 3 months) while preserving the ability to show YoY growth for the selected period. The database contains 500 million rows in the Sales table. The company has strict performance requirements: report pages must load within 5 seconds. You need to design the model in Power BI Desktop. Which approach should you take?

Accepted Answer

Use Import storage mode for all tables with incremental refresh policy on the Sales table to load only the last 5 years of data.. Option C is correct because Import mode with incremental refresh on the 500-million-row Sales table delivers the sub-5-second report performance required, while the incremental refresh policy limits data loaded to the last 5 years and only refreshes changed partitions. Import mode also fully supports the required Year→Quarter→Month hierarchy, Country and Category slicers, and DAX time-intelligence measures like YoY growth and a 12-month moving average, provided a proper Dates table is related to Sales. Option A is wrong because DirectQuery for all tables pushes every visual query to Azure SQL, which will not reliably meet the 5-second page load requirement at this data volume. Option B is wrong because DirectQuery on the large Sales table still incurs slow remote queries for aggregations and time intelligence. Option D is wrong because using DateKey from Sales without a dedicated date table prevents correct time-intelligence functions such as SAMEPERIODLASTYEAR and DATESINPERIOD.

Answer

Use DirectQuery storage mode for all tables to ensure real-time data and aggregate queries at the source.

Answer

Use a composite model: Import for dimension tables and DirectQuery for Sales table to balance freshness and performance.

Answer

Use Import mode but do not create a date table; instead use the DateKey column from Sales for time intelligence.