PL-300 Practice Exam 61 — 20 Questions

Question 1

You are a data modeler for a logistics company. The company uses Power BI to analyze shipment data. The source system is a SQL Server database with tables: Shipments (ShipmentID, ShipDate, CustomerID, Weight, Cost, CarrierID), Customers (CustomerID, CustomerName, Region), Carriers (CarrierID, CarrierName, Mode). The database is updated in real-time. You need to build a semantic model that supports near real-time reporting with maximum data freshness while maintaining good query performance. The model should allow users to filter by customer region and carrier mode, and calculate total cost and weight per month. What is the best approach?

Accepted Answer

Use DirectQuery on the three tables and create a date table in the model.. Option C is correct because using DirectQuery (or dual storage) with a date table allows real-time data access and supports time intelligence. Option A is wrong because DirectQuery on a single flat table loses relationships. Option B is wrong because importing into a star schema does not support real-time. Option D is wrong because composite model is not needed for this scenario.

Answer

Import the three tables into Power BI and create a star schema with relationships.

Answer

Create a single view in SQL Server that joins all tables and use DirectQuery on that view.

Answer

Use a composite model: import Customers and Carriers, and use DirectQuery on Shipments.

Question 2

You are a Power BI developer for an e-commerce company. You have a semantic model that contains a table named Orders with columns: OrderID, CustomerID, OrderDate, ProductID, Quantity, and SalesAmount. You also have a table named Products with columns: ProductID, ProductName, Category, and Price. You need to create a measure that calculates the percentage of total sales for each product category. The measure should be used in a matrix visual with Category on rows. Which DAX expression should you use?

Accepted Answer

% Total Sales = DIVIDE(SUM(Orders[SalesAmount]), CALCULATE(SUM(Orders[SalesAmount]), ALLSELECTED(Products[Category]))). Option A is correct: ALLSELECTED respects the current filter context for the denominator, giving the correct percentage. Option B uses ALL, which removes all filters, not just the category. Option C uses ALLEXCEPT, which removes filters from all columns except Category, which is not what is needed. Option D is incorrect syntax.

Answer

% Total Sales = SUM(Orders[SalesAmount]) / CALCULATE(SUM(Orders[SalesAmount]), ALLSELECTED(Orders))

Answer

% Total Sales = DIVIDE(SUM(Orders[SalesAmount]), CALCULATE(SUM(Orders[SalesAmount]), ALLEXCEPT(Products, Products[Category])))

Answer

% Total Sales = DIVIDE(SUM(Orders[SalesAmount]), CALCULATE(SUM(Orders[SalesAmount]), ALL(Products)))

Question 3

You are a Power BI report creator for a university. You have a semantic model with a table named Enrollments with columns: EnrollmentID, StudentID, CourseID, EnrollmentDate, and Grade. You also have tables: Students (StudentID, StudentName, Major) and Courses (CourseID, CourseName, Department). You need to create a measure that counts the number of students who have enrolled in at least one course in the 'Science' department. The measure should be efficient and respect any filters on the report. Which DAX expression should you use?

Accepted Answer

Students in Science = CALCULATE(DISTINCTCOUNT(Enrollments[StudentID]), FILTER(Courses, Courses[Department] = "Science")). Option A is correct: CALCULATE with DISTINCTCOUNT and a FILTER on the related Courses table ensures only students with Science courses are counted. Option B counts enrollments, not students. Option C is incorrect because FILTER does not return a table of courses. Option D is incorrect syntax.

Answer

Students in Science = CALCULATE(DISTINCTCOUNT(Enrollments[StudentID]), FILTER(Enrollments, RELATED(Courses[Department]) = "Science"))

Answer

Students in Science = DISTINCTCOUNT(Enrollments[StudentID])

Answer

Students in Science = COUNTROWS(Enrollments)

Question 4

You are a Power BI data analyst at a financial services company. You have created a Power BI dataset that uses a live connection to an Azure Analysis Services (AAS) tabular model. The AAS model contains sensitive financial data. You need to implement row-level security (RLS) such that each user can only see data for their assigned region. The user's region is stored in a SQL Server database table called 'UserRegion' that maps user principal names (UPNs) to region IDs. The AAS model already has a 'Region' dimension table with a 'RegionID' column. You are not allowed to modify the AAS model. What should you do?

Accepted Answer

In Power BI Desktop, import the 'UserRegion' table from SQL Server and define an RLS role that filters the 'Region' table using the USERPRINCIPALNAME() function.. Option C is correct. You can implement RLS in Power BI Desktop by defining a role that uses the USERPRINCIPALNAME() function to filter the 'Region' table based on the 'UserRegion' table imported into the Power BI dataset. Option A is wrong because you cannot modify the AAS model. Option B is wrong because RLS in AAS requires modifying the model. Option D is wrong because the 'Region' table is in AAS, not in the SQL database.

Answer

Use Power Query to filter the 'Region' table based on the current user's region stored in the SQL database.

Answer

Define RLS roles directly in the AAS model using the USERNAME() function.

Answer

Modify the AAS model to include the 'UserRegion' table and define RLS roles in AAS.

Question 5

You are a Power BI administrator. The company has a premium capacity that many users publish reports to. Recently, users have reported that some reports are slow to load. You suspect that the capacity is being overused by certain large datasets. You need to identify which workspaces and datasets are consuming the most memory and CPU resources on the capacity. You want to use a tool that provides historical metrics and can be queried. What should you do?

Accepted Answer

Install the Power BI Premium Capacity Metrics app from AppSource.. Option D is correct because the Power BI Premium Capacity Metrics app provides detailed historical metrics on memory, CPU, and query activity. Option A is wrong because the admin portal only shows current status. Option B is wrong because Log Analytics requires configuration and may not have historical data. Option C is wrong because Performance Analyzer is for individual reports, not capacity-wide.

Answer

Use Performance Analyzer in Power BI Desktop for each report.

Answer

Use the Power BI admin portal to view capacity metrics in real-time.

Answer

Enable Power BI activity logs and query Log Analytics for capacity metrics.

Question 6

A Power BI administrator wants to allow users to create dashboards and reports, but prevent them from sharing content outside the organization. Which two settings should be configured in the Power BI admin portal? (Choose two.)

Accepted Answer

Disable 'Share content with external users' in the tenant settings.. Option A (control 'Share content with external users') and Option D (disable 'Publish to web') are correct. Option A directly blocks sharing with external users. Option D prevents public sharing via embed codes. Option B (create workspaces) is irrelevant to sharing. Option C (export data) is about exporting, not sharing. Option E (featured tables) is unrelated.

Answer

Disable 'Create workspaces' in the tenant settings.

Answer

Disable 'Export data' in the tenant settings.

Answer

Disable 'Featured tables' in the tenant settings.

Question 7

Your organization uses Power BI and has a requirement that all reports must be accessible to users with screen readers. You need to configure the Power BI service to enforce accessibility compliance. What should you do?

Accepted Answer

In the Power BI admin portal, enable the 'Enforce accessibility requirements' setting.. Option A is correct because accessibility settings in Power BI service can enforce requirements like alt text and tab order. Option B is wrong because Microsoft Intune manages devices, not Power BI accessibility. Option C is wrong because Microsoft Entra ID does not have accessibility settings for Power BI. Option D is wrong because report-level settings are manual, not enforced tenant-wide.

Answer

Use Microsoft Entra ID to enforce conditional access for accessibility.

Answer

Use Microsoft Intune to enforce accessibility policies on user devices.

Answer

Set accessibility properties on each report individually.

Question 8

You are a Power BI administrator. Your company has a Power BI tenant with a large number of workspaces. A new regulation requires that all shared dashboards must have a data retention policy that deletes data older than 6 months. You need to implement this for all existing and future dashboards. The solution must be automated and not require manual intervention. What should you do?

Accepted Answer

Develop a PowerShell script using the Power BI REST API to regularly delete dashboards older than 6 months.. Option C is correct because Power BI does not have built-in data retention for dashboards; you must use the REST API to programmatically manage content. Option A is wrong because there is no tenant-level retention setting. Option B is wrong because Microsoft Purview does not manage Power BI data retention. Option D is wrong because there is no dashboard-level setting.

Answer

Configure a tenant-level data retention policy in the Power BI admin portal.

Answer

Set a retention period on each dashboard manually in the dashboard settings.

Answer

Use Microsoft Purview data lifecycle management to apply retention labels.

Question 9

A Power BI administrator needs to prevent users from creating new workspaces, but allow existing workspaces to continue functioning. Which two settings should be configured? (Choose two.)

Accepted Answer

In the Power BI admin portal, under 'Workspace settings', disable 'Create workspaces' for all users.. Option A (disable 'Create workspaces' for all users) and Option D (enable 'Block users from creating workspaces' in the admin portal) are correct. Option A is a tenant setting to disable workspace creation. Option D is the actual admin portal setting. Option B is wrong because disabling 'Use workspaces' would break existing workspaces. Option C is wrong because setting a group policy is not the correct approach. Option E is wrong because removing workspace creation from the admin portal is not a granular setting.

Answer

Set the 'Maximum number of workspaces a user can create' to 0 for all security groups.

Answer

Remove the 'Create workspace' button from the Power BI navigation pane using custom branding.

Answer

In the Power BI admin portal, disable 'Use workspaces' for all users.

Question 10

You are a business analyst at a marketing agency. You receive a weekly export from a web analytics platform as a CSV file. The file contains columns: Date, Campaign, Impressions, Clicks, Conversions. You need to create a Power BI report that shows trends over time, with the ability to filter by campaign. The CSV file is updated weekly and you want to automate the refresh. You also want to ensure that any new campaigns are automatically included in the report. You have limited experience with Power BI. What should you do?

Accepted Answer

Create a Power BI dataflow that reads the CSV file from a SharePoint folder, transforms the data (e.g., change data types), and then load the dataflow as a dataset into Power BI. Schedule the dataflow to refresh weekly.. Option D is correct because it uses a Power BI dataflow to read the CSV from a SharePoint folder, which supports scheduled refresh without a personal gateway. The dataflow can automatically include new campaigns as they appear in the folder, and the scheduled weekly refresh ensures the report stays up to date. This approach is ideal for users with limited Power BI experience because dataflows handle transformation and refresh centrally in the Power BI Service.

Answer

Use Power Query to read the CSV from a local folder, but schedule the refresh using a personal gateway.

Answer

Import the CSV file directly into Power BI Desktop, create the report, and publish to Power BI Service. Manually re-import the CSV each week.

Answer

Enable incremental refresh on the dataset to only load new data each week.

Question 11

You are a Power BI developer for a financial services company. You are preparing data from multiple sources: a CSV file containing daily stock prices (ticker, date, close_price), a SQL Server database with company information (ticker, company_name, sector), and an Excel file with quarterly earnings data (ticker, quarter, earnings_per_share). The CSV file has 5 years of daily data (approx 1.3 million rows). The SQL Server table has 5000 rows. The Excel file has 20,000 rows. You need to create a data model that allows users to filter by sector, company, and date range, and to calculate moving averages of stock prices and compare earnings over time. Performance is critical. You must decide the best approach to combine and model this data. What should you do?

Accepted Answer

Import all tables, create a date table with CALENDAR, and establish relationships: StockPrices[Date] -> DateTable[Date], StockPrices[Ticker] -> Company[Ticker], Earnings[Ticker] -> Company[Ticker], and create a many-to-many relationship between Earnings and DateTable using a bridge table.. Option C is correct because importing all tables into the in-memory VertiPaq engine ensures optimal performance for large datasets (1.3M rows) and complex calculations like moving averages. Creating a separate date table with CALENDAR enables proper time intelligence, while the bridge table resolves the many-to-many relationship between quarterly earnings and daily dates, allowing accurate filtering by sector, company, and date range without performance degradation.

Answer

Use DirectQuery for StockPrices (CSV) and import the other tables.

Answer

Import only StockPrices and Company, and use the auto date/time feature; ignore Earnings data.

Answer

Import all tables, then in Power Query merge StockPrices with Company and Earnings into a single flat table using left outer joins.

Question 12

You are a data analyst for an e-commerce company. You have a Power BI dataset that imports data from a Salesforce source using the Salesforce connector. The dataset is refreshed daily. Recently, the refresh started failing with the error 'Unable to connect to the data source'. You have verified that the Salesforce credentials are correct and that the Salesforce service is healthy. What is the most likely cause?

Accepted Answer

The OAuth redirect URI for the Salesforce app is not configured correctly.. The Salesforce connector in Power BI uses OAuth 2.0 for authentication. Even if credentials are correct and the Salesforce service is healthy, a misconfigured OAuth redirect URI in the Salesforce connected app prevents Power BI from completing the authorization handshake, causing the 'Unable to connect to the data source' error during refresh. This is a common issue when the redirect URI does not match the Power BI service's callback endpoint.

Answer

The on-premises data gateway is not installed.

Answer

The privacy levels for the data source are set to 'Private'.

Answer

The Power BI service is not available in the same region as Salesforce.

Question 13

You are a data analyst at a retail company. You have a Power BI semantic model that imports sales data from an Azure SQL Database. The database uses a timestamp column to track transaction time. You need to reduce the data refresh time and ensure that only the last 30 days of data are refreshed during each scheduled refresh. You have already created the necessary parameters rangeStart and rangeEnd in Power Query. What should you do next to implement incremental refresh?

Accepted Answer

In Power BI Desktop, on the model view, select the table and set the incremental refresh policy.. Option D is correct because incremental refresh policies are defined in Power BI Desktop on the model view, not in the service or by simply filtering in Power Query. After creating the rangeStart and rangeEnd parameters, you must select the table in the Model view, open the incremental refresh policy dialog, and configure the policy to filter data based on those parameters, ensuring only the last 30 days are refreshed.

Answer

In the Power BI service, go to the dataset settings and configure the scheduled refresh.

Answer

In Power Query Editor, apply the rangeStart and rangeEnd filters to the data and then close and apply.

Answer

In the Power BI service, create a new refresh schedule and set the incremental refresh period.

Question 14

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.. Option B is correct because 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 15

You are a data analyst for a global retail company. The company uses Power BI Premium capacity. You are building a dataset that combines sales data from three sources:
1. An Azure SQL Database that stores transactional sales data (10 million rows per day, retained for 5 years).
2. A SharePoint Online folder containing monthly Excel reports from regional offices (each report has a different structure).
3. A Dataverse table that contains customer feedback scores.

Requirements:
- The dataset must support near real-time reporting for the current month's sales (maximum 15-minute latency).
- Historical sales data (older than current month) can be refreshed daily.
- Customer feedback scores should be updated every hour.
- The Excel reports from SharePoint must be combined into a single table with consistent columns.
- The final dataset should be optimized for fast query performance.

You need to design the data preparation strategy. What should you do?

Accepted Answer

Use a composite model: DirectQuery for the current month's sales data from Azure SQL, and Import mode for historical sales (with incremental refresh) and for customer feedback (with hourly refresh). Combine SharePoint files using Power Query and load them into the model using Import mode. Set up a DirectQuery connection for near real-time.. Option C is correct because it uses a composite model to meet all requirements: DirectQuery for near real-time current-month sales (≤15-minute latency), Import mode with incremental refresh for historical sales (daily refresh), Import mode for customer feedback (hourly refresh), and Power Query to combine SharePoint Excel files into a consistent table. This approach balances real-time needs with query performance and refresh flexibility, leveraging Power BI Premium's composite model capabilities.

Answer

Use DirectQuery for all data sources and create views in Azure SQL to transform the SharePoint and Dataverse data. Use Power Query to combine SharePoint files in a view.

Answer

Import all data into Power BI using Import mode. Schedule refreshes every 15 minutes for the current month and daily for historical data.

Answer

Use Azure Data Factory to copy all data to Azure SQL Database, then connect Power BI using DirectQuery.

Question 16

You are the Power BI administrator for a large enterprise. The company has a Power BI Premium capacity with a single dataset that is used by multiple reports and dashboards. The dataset is refreshed daily at 3:00 AM, and the refresh typically completes within 2 hours. Recently, users have reported that the dataset is not showing the most recent data until after 6:00 AM. You investigate and find that the scheduled refresh is taking 4 hours to complete, and there are no errors in the refresh history. The dataset uses import mode and connects to an on-premises SQL Server data warehouse. The data model contains several large fact tables and multiple calculated tables and measures. What should you do to reduce the refresh time and ensure data is available by 5:00 AM?

Accepted Answer

Implement incremental refresh on the fact tables to refresh only new and changed data. Implementing incremental refresh on the fact tables allows Power BI to refresh only new or changed data instead of the entire dataset each time. This significantly reduces the refresh window, especially for large fact tables, because only the latest partition (e.g., today's data) is processed. Since the scheduled refresh starts at 3:00 AM and must complete by 5:00 AM, incremental refresh can cut the refresh time from 4 hours to under 2 hours by avoiding reprocessing historical data.

Answer

Remove all calculated tables and measures and replace them with calculated columns in Power Query

Answer

Change the dataset storage mode to DirectQuery to avoid the import process

Answer

Install an additional on-premises data gateway and configure load balancing

Question 17

You are a data analyst for a retail company. You receive daily sales data in CSV files stored in an Azure Blob Storage container. The files have a consistent naming convention: Sales_YYYYMMDD.csv. You need to load only the latest file into Power BI each day, using Power Query. You want to automate this process so that each time you refresh the dataset, it picks up the most recent file. You have already created a connection to the container. What should you do in Power Query to ensure only the latest file is loaded?

Accepted Answer

Use 'From Folder' connector, filter by creation date to keep only the latest file.. Option C is correct because the 'From Folder' connector in Power Query allows you to connect to a folder (including Azure Blob Storage containers mapped as folders) and then filter files by creation date or last modified date. By filtering to keep only the latest file, you ensure that each refresh loads the most recent CSV without manual intervention. This approach fully automates the process, as Power Query will evaluate the filter each time the query refreshes.

Answer

Use 'From Azure Blob Storage' and filter by last modified date.

Answer

Use 'Combine Files' and then filter to the latest date.

Answer

Create a parameter for the file name and update it manually.

Question 18

You have a Power BI report that displays sales data by region. The report includes a map visual showing sales amount by state. You notice that some states are not displayed on the map because the state names in the data do not match the standard names used by Power BI's map visualization. For example, 'California' is correct but 'CA' is not recognized. You need to ensure all states appear correctly. Which action should you take? A. Create a calculated column that maps state abbreviations to full names using a SWITCH statement. B. Change the map visual to a filled map. C. Use the 'Data category' property to set the field to 'State or Province'. D. Add a custom map image. Which option is the best?

Accepted Answer

Create a calculated column that maps state abbreviations to full names using a SWITCH statement. Option A is correct because you can create a calculated column that converts abbreviations to full names using a SWITCH or a mapping table. Option B is wrong because a filled map has the same recognition issue. Option C is wrong because setting data category does not change the values. Option D is wrong because custom map images require additional setup and may not solve the recognition issue.

Answer

Use the 'Data category' property to set the field to 'State or Province'

Answer

Add a custom map image

Answer

Change the map visual to a filled map

Question 19

You are a Power BI report creator. You have a dataset that includes a date table and a sales table. The date table has a column 'FiscalYear' (e.g., 'FY2025'). The sales table has a column 'OrderDate'. You need to create a relationship between the date table and the sales table based on the fiscal year. However, the date table has one row per fiscal year, and the sales table has multiple rows per fiscal year. You want to filter sales by fiscal year. What is the correct approach to model this?

Accepted Answer

Create a one-to-many relationship from Date[FiscalYear] to Sales[FiscalYear] (assuming Sales table has FiscalYear).. Option C is correct because it establishes a standard one-to-many relationship between the Date table (with one row per fiscal year) and the Sales table (with multiple rows per fiscal year) using a common FiscalYear column. This allows Power BI to filter sales by fiscal year through the relationship, leveraging the date table as a filter dimension. The relationship must be based on a column in the Sales table that contains the fiscal year for each order, which can be added as a calculated column if not already present.

Answer

Add a calculated column in Sales to extract fiscal year from OrderDate and use that for filtering without a relationship.

Answer

Merge the Date table into the Sales table in Power Query.

Answer

Create a many-to-many relationship between Date and Sales using a bridge table.

Question 20

You are a data analyst at a retail company. You are building a Power BI report to analyze sales performance across multiple stores. The source data comes from an Azure SQL Database that contains a table 'Sales' with columns: StoreID, ProductID, SaleDate, Quantity, and Amount. The database also has a 'Stores' table with StoreID and StoreName, and a 'Products' table with ProductID, ProductName, and Category. You need to create a data model that supports filtering by store, product category, and date, and also allows calculation of year-over-year sales growth. You want to minimize the model size and ensure optimal performance. The data volume is large (millions of rows). You must design the data model. What should you do?

Accepted Answer

Import Sales, Stores, and Products tables, create a separate date table using CALENDAR, and establish relationships between Sales and dimension tables.. Option B is correct because it follows the star schema best practice: importing dimension tables (Stores, Products, a dedicated Date table) and the fact table (Sales) separately, then creating relationships. This minimizes model size by avoiding data duplication and enables efficient filtering by store, product category, and date. The separate date table is essential for accurate year-over-year calculations using DAX time intelligence functions like SAMEPERIODLASTYEAR, which require a continuous date range.

Answer

Import all tables as they are and create a single flat table by merging Sales, Stores, and Products in Power Query.

Answer

Import Sales table only and create calculated columns for StoreName and ProductName using RELATED.

Answer

Import Sales table and use the auto date/time feature for time intelligence.