PL-300 Practice Exam 62 — 20 Questions

Question 1

You are a data analyst for a healthcare organization. You have a Power BI dataset that imports patient data from an on-premises SQL Server database. The database contains personally identifiable information (PII). You need to ensure that all PII columns are obfuscated when the data is loaded into Power BI. You have already masked the columns in the source database using dynamic data masking. However, when you refresh the dataset in Power BI, the masked values are not appearing. What should you do?

Accepted Answer

In the data source settings, use a SQL Server account that has been granted the UNMASK permission to see masked data, but actually you need an account WITHOUT UNMASK to see masked data. Actually, correct: Configure the data source connection to use a user without the UNMASK permission so that dynamic data masking applies.. Option D is correct because dynamic data masking (DDM) in SQL Server works by returning masked values to users who lack the UNMASK permission. When Power BI connects to the database using a SQL Server account without UNMASK, DDM automatically obfuscates the PII columns in the query results, so the masked data is loaded into Power BI. The current issue is that the connection account likely has UNMASK permission, which reveals the original unmasked data instead of the masked values.

Answer

In Power BI Desktop, define row-level security roles to restrict PII columns.

Answer

Apply data masking in Power Query using Text.Start or Text.End functions.

Answer

Install the on-premises data gateway and configure it to use the service account.

Question 2

You are a data analyst for a multinational corporation. You are building a Power BI report that uses a large fact table (100 million rows) and several dimension tables. The data source is a SQL Server data warehouse. Users need to see near real-time data with a maximum latency of 15 minutes. The current import mode takes too long to refresh. You decide to use DirectQuery mode. However, queries are slow. You need to improve query performance. You consider creating aggregations in the data source. Which approach should you take in Power BI to leverage these aggregations?

Accepted Answer

Define aggregations in Power BI on the DirectQuery tables.. Option B is correct because Power BI allows you to define aggregations on DirectQuery tables, which enables the engine to route queries to pre-aggregated data at the source (e.g., SQL Server indexed views or materialized views) when possible, significantly reducing query latency. This approach leverages the existing aggregations in the data source without changing the storage mode or importing data, maintaining near real-time freshness with a 15-minute latency requirement.

Answer

Create a composite model with an imported aggregated table.

Answer

Change the storage mode to Import for the fact table.

Answer

Create a SQL Server view that aggregates data and use it as the source.

Question 3

You are preparing a Power BI dataset that uses a SQL Server database as the source. Your manager asks you to ensure that the dataset refreshes automatically every morning at 6:00 AM. You have configured a scheduled refresh in the Power BI service. However, after the first scheduled refresh, you notice that it failed with an error: 'The data source credentials are invalid.' What is the most likely cause and solution?

Accepted Answer

The data source credentials are not configured in the gateway.. The error 'The data source credentials are invalid' indicates that the credentials stored in the Power BI service for the data source are incorrect or missing. Since the dataset uses a SQL Server database, which is an on-premises data source, the credentials must be configured within the on-premises data gateway settings in the Power BI service. Option B correctly identifies that the gateway credentials were not set up, causing the refresh to fail despite the scheduled refresh being configured.

Answer

The scheduled refresh time is not set correctly.

Answer

An on-premises data gateway is not installed.

Answer

The dataset is too large for scheduled refresh.

Question 4

You are a data analyst for a financial services company. You have a Power BI dataset that combines data from two sources: a CSV file in SharePoint Online and an on-premises SQL Server database. The CSV file contains exchange rates that are updated daily. The SQL Server database contains transaction data. You need to ensure that the dataset can be refreshed automatically in the Power BI service. The CSV file is updated at 6:00 AM daily, and the SQL Server database is updated continuously. You have already published the report. What should you do to enable automated refresh?

Accepted Answer

Install and configure an on-premises data gateway, then set up a scheduled refresh.. Option C is correct because the on-premises SQL Server database requires an on-premises data gateway to bridge the Power BI service with the local network. Even though the CSV file is in SharePoint Online, the dataset combines both sources; the gateway is mandatory for the SQL Server component. Without it, scheduled refresh cannot access the on-premises data, and the dataset will fail to refresh automatically.

Answer

Use Power Automate to refresh the dataset after the CSV is updated.

Answer

Enable incremental refresh for the SQL Server table to reduce refresh time.

Answer

Configure a scheduled refresh in the dataset settings. The gateway is not required because the CSV file is in SharePoint Online.

Question 5

You are a Power BI analyst at a marketing agency. You have a data model with a 'Campaigns' table (columns: CampaignID, CampaignName, StartDate, EndDate, Budget, Spent) and a 'Results' table (columns: ResultID, CampaignID, Date, Impressions, Clicks, Conversions). You need to model the data to analyze campaign performance over time. You create a relationship between Campaigns and Results on CampaignID (one-to-many). You also create a date dimension 'Calendar' and relate it to Results[Date]. However, when you create a measure to calculate the total budget by month, the budget for a campaign that spans multiple months is counted in each month, leading to double-counting. What should you do to accurately allocate the budget across months?

Accepted Answer

Create a calculated table using CROSSJOIN between Campaigns and Calendar, then use it as a bridge. Option D is correct because a many-to-many relationship between Campaigns and Calendar via a bridge table allows proper allocation. Option A is wrong because changing the relationship direction won't solve double-counting. Option B is wrong because a simple calculated column cannot allocate budget correctly. Option C is wrong because a calculated table with CROSSJOIN is needed to create the bridge.

Answer

Change the relationship between Campaigns and Results to bidirectional

Answer

Use the DAX function USERELATIONSHIP to activate an inactive relationship

Answer

Add a calculated column to Campaigns that divides the budget by the number of months

Question 6

You are creating a Power BI report for a small business. The data source is a Microsoft Access database with tables: Customers (CustomerID, CompanyName, City), Orders (OrderID, CustomerID, OrderDate, Amount). You need to model the data to analyze total orders by customer and by month. What is the most efficient approach?

Accepted Answer

Import both tables into Power BI and create a relationship between CustomerID columns.. Option A is correct because importing both tables and creating a relationship allows for easy analysis. Option B is wrong because DirectQuery on Access is not recommended due to performance. Option C is wrong because a single table duplicates customer information. Option D is wrong because an external database is unnecessary.

Answer

Use DirectQuery on the Access database to avoid importing.

Answer

Create a single query in Power Query that joins the tables and import the result.

Answer

Migrate the Access database to SQL Server and then import.

Question 7

You are building a Power BI report for a logistics company. The data source is a SQL Server database with tables: 'Shipments' (columns: ShipmentID, OrderDate, ShipDate, CustomerID, Origin, Destination, Weight, Cost, Revenue), 'Customers' (CustomerID, CustomerName, Segment, Country), and 'Calendar' (Date, Year, Month, Quarter). You need to create a star schema in Power BI. You have already created the relationships. When you try to create a measure to calculate the average delivery time (ShipDate - OrderDate), you get an error because the data types are datetime. You want to create a calculated column in the Shipments table that shows the delivery time in days. Which DAX expression should you use?

Accepted Answer

DeliveryDays = DATEDIFF(Shipments[OrderDate], Shipments[ShipDate], DAY). Option A is correct because DATEDIFF calculates the difference in days. Option B is wrong because subtracting datetimes directly may not work in all cases. Option C is wrong because DATEDIFF returns an integer, not a string. Option D is wrong because CALCULATE is not needed.

Answer

DeliveryDays = Shipments[ShipDate] - Shipments[OrderDate]

Answer

DeliveryDays = CALCULATE(DATEDIFF(Shipments[OrderDate], Shipments[ShipDate], DAY))

Answer

DeliveryDays = FORMAT(Shipments[ShipDate] - Shipments[OrderDate], "d")

Question 8

You are a data analyst for a logistics company. You have a Power BI semantic model that contains a table named Shipments with columns: ShipmentID, OriginCity, DestinationCity, ShipDate, DeliveryDate, Weight, and Cost. The model also contains a table named Cities with columns: City, State, Region, and Population. You need to create a measure that calculates the average delivery time in days for shipments that originated from cities with a population greater than 1 million. The measure should be efficient. Which DAX expression should you use?

Accepted Answer

AVG Delivery Time = CALCULATE(AVERAGE(Shipments[DeliveryDate] - Shipments[ShipDate]), FILTER(Cities, Cities[Population] > 1000000)). Option A uses CALCULATE with a FILTER on the related table, which is correct and efficient. Option B uses SUMX over the entire table, which can be slow. Option C uses an iterator incorrectly. Option D uses DISTINCT unnecessarily.

Answer

AVG Delivery Time = SUMX(FILTER(Shipments, RELATED(Cities[Population]) > 1000000), Shipments[DeliveryDate] - Shipments[ShipDate]) / COUNTROWS(FILTER(Shipments, RELATED(Cities[Population]) > 1000000))

Answer

AVG Delivery Time = CALCULATE(AVERAGE(Shipments[DeliveryDate] - Shipments[ShipDate]), DISTINCT(Cities[Population] > 1000000))

Answer

AVG Delivery Time = AVERAGEX(Shipments, Shipments[DeliveryDate] - Shipments[ShipDate])

Question 9

You are a data analyst at a multinational retail company. The company uses Microsoft Power BI to analyze sales data from multiple regions. The source data is stored in Azure SQL Database and includes tables: Sales (OrderID, ProductID, Quantity, Amount, OrderDate, StoreID), Stores (StoreID, StoreName, Region, Country), Products (ProductID, ProductName, Category, Price). The model is imported daily. You need to design a semantic model that supports the following requirements: 1) Allow users to filter by year and month using a single slicer. 2) Ensure that time intelligence functions (e.g., TOTALYTD, SAMEPERIODLASTYEAR) work correctly. 3) Minimize model size. 4) Provide a consistent date dimension for all fact tables. The Sales table has orders from 2018-01-01 to 2025-12-31. You decide to create a date table. Which of the following approaches should you take?

Accepted Answer

Create a date table using DAX with CALENDAR and mark it as a date table.. Option B is correct because creating a date table in DAX using CALENDAR and marking it as a date table satisfies all requirements: it minimizes model size (no extra columns), enables time intelligence, and provides a consistent dimension. Option A is wrong because Power BI's auto-generated date hierarchy cannot be marked as a date table for time intelligence. Option C is wrong because importing a full date table from SQL adds overhead without benefit. Option D is wrong because using the Sales[OrderDate] column directly does not provide a separate date dimension and may cause issues with time intelligence.

Answer

Use the Sales[OrderDate] column directly and create a calculated column for year and month.

Answer

Use Power BI's auto-generated date hierarchy for each date column.

Answer

Import a date table from the source database that includes all dates and additional attributes.

Question 10

You are a Power BI developer at a retail company. You have a data model with a 'Sales' fact table (10 million rows) and dimension tables: 'Date', 'Customer', 'Product', 'Store'. The 'Sales' table includes columns: SalesID, DateKey, CustomerID, ProductID, StoreID, Quantity, UnitPrice, Discount, TotalAmount. The 'Product' dimension has 5,000 rows and includes columns: ProductID, ProductName, Category, SubCategory, Brand, Price. The 'Store' dimension has 200 rows and includes columns: StoreID, StoreName, Region, City, Manager. The 'Customer' dimension has 100,000 rows. The report currently has a measure 'Total Sales' = SUM(Sales[TotalAmount]) and a measure 'Total Quantity' = SUM(Sales[Quantity]). Users complain that the report is slow when filtering by multiple categories and regions simultaneously. You need to improve performance without changing the data source. Which action should you take first?

Accepted Answer

Reduce the number of columns in the Customer dimension by removing unused columns. Option C is correct because reducing the cardinality of the Customer dimension by removing unnecessary columns (e.g., address details) can significantly reduce model size and improve performance. Option A is wrong because disabling auto date/time might help but is a minor improvement. Option B is wrong because aggregations can help but require additional setup and are not the first step. Option D is wrong because changing to dual storage mode may not improve performance and could increase complexity.

Answer

Create an aggregation table for the Sales table

Answer

Disable the auto date/time feature in Power BI

Answer

Change the storage mode of the Sales table to Dual

Question 11

You are a Power BI administrator for a large enterprise. You have a Power BI semantic model that uses a single large fact table named Sales (100 million rows) and several dimension tables. The model is used by multiple departments, each with different row-level security (RLS) rules based on the SalesRegion column. You have implemented RLS using static roles. However, you notice that when users from different departments view the same report page, the query performance varies significantly. You suspect that the RLS filters are causing the performance difference. You need to investigate and optimize the RLS performance. What should you do first?

Accepted Answer

Use Power BI Performance Analyzer to capture query performance for each user role and analyze the generated DAX queries in DAX Studio.. Option A is correct: using Performance Analyzer and DAX Studio can identify which queries are slow and whether RLS is the cause. Option B would not help directly. Option C is a possible optimization but not the first step. Option D is wrong because dynamic RLS is not necessarily faster.

Answer

Increase the data model's memory limit in Premium capacity.

Answer

Convert all RLS roles to use dynamic RLS with USERPRINCIPALNAME.

Answer

Remove all RLS roles and implement security at the report level using bookmarks.

Question 12

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 B is correct: Using Import mode with incremental refresh reduces data volume and leverages Power BI's compression and in-memory engine for fast performance. Option A is wrong because DirectQuery over such a large table would be slow. Option C is wrong because Composite model adds complexity and may not meet performance targets. Option D is wrong because it lacks the necessary date hierarchy for drill-down.

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.

Question 13

You are a data analyst for a university. You need to build a Power BI report to analyze student enrollment by program and year. Data sources: a CSV file with student enrollment records (StudentID, Program, EnrollmentYear, Status) and an Excel file with program details (ProgramCode, ProgramName, Faculty). The CSV file contains 1 million rows. The report must allow users to filter by faculty and program, and see a bar chart of enrollment counts by year. You need to design the data model. What should you do?

Accepted Answer

Import both tables into Power BI, create a relationship between Enrollment[Program] and Programs[ProgramCode], use Star schema design.. Option A is correct: Import both tables, create relationships, and use a star schema. Option B is wrong because DirectQuery on a CSV is not supported. Option C is wrong because having no relationships prevents filtering. Option D is wrong because merging loses granularity and may cause duplication.

Answer

Import the CSV file only and use DirectQuery for the Excel file to save memory.

Answer

Import both tables but do not create relationships; use visual-level filters to synchronize selections.

Answer

Merge the two tables in Power Query into a single table to simplify the model.

Question 14

You are a Power BI analyst at a retail company. The company has a SQL Server data warehouse with tables: 'Sales' (SalesID, ProductID, CustomerID, SalesDate, Amount, Quantity), 'Products' (ProductID, ProductName, Category, UnitPrice), 'Customers' (CustomerID, CustomerName, Region, Segment). The data warehouse is refreshed nightly. You need to build a Power BI solution that meets the following requirements:
- The report must be published to the Power BI service and support scheduled refresh.
- Users must be able to analyze sales by product category and customer region.
- The data model should follow a star schema design.
- A measure must calculate the running total of sales by date.
- The report must include a visual that shows the top 5 products by sales amount for the current year.
- Performance is critical; the report should load quickly.

You have imported the data into Power BI Desktop. Which of the following actions should you take to meet the requirements?

Accepted Answer

Create a date dimension table. Build a star schema with Sales as fact and Products, Customers, and Date as dimensions. Use TOTALYTD for running total and TOPN for top products.. Option B is correct because creating a date dimension, using a star schema, and using DAX measures (including TOTALYTD for running total and TOPN for top products) is the best practice. Option A is wrong because using a single flat table would create a poor model and slow performance. Option C is wrong because DirectQuery would be slower for aggregated measures like running total. Option D is wrong because using calculated columns for running total is less efficient than measures.

Answer

Import all tables. Create a calculated column for running total using FILTER and EARLIER. Use a slicer to filter by current year and sort a bar chart by sales.

Answer

Use DirectQuery mode to connect to the data warehouse. Write a SQL query for each visual to ensure real-time data. Use a SQL window function for running total.

Answer

Create a single flat table by merging all tables in Power Query. Use a measure for running total with EARLIER function. Create a Top N filter on a table visual.

Question 15

You are a Power BI data analyst at a global retail company. Your organization uses a SQL Server data warehouse to store sales transactions, product inventory, and customer demographics. The data warehouse is updated nightly. You have been tasked with building a Power BI report to analyze sales performance across different regions and product categories. The report must meet the following requirements:

- Allow users to filter data by date, region, and product category.
- Provide drill-down from region to store level.
- Display key metrics such as total sales, sales growth compared to the previous year, and profit margin.
- Ensure that the report loads quickly even when users apply multiple filters.
- Users must be able to export the underlying data to Excel for further analysis.
- The report should be accessible on mobile devices with a responsive layout.
- You need to implement row-level security so that regional managers can only see data for their own region.
- The dataset must support scheduled refresh.

You have imported the necessary tables from the data warehouse using Import mode. You created a date dimension table using DAX and marked it as a date table. You also created a separate table for regions with a column 'RegionManagerEmail' that maps each region to the manager's email address. You plan to use RLS by creating a role that filters the region table based on the user's email address.

After publishing the report to the Power BI service, you notice that the report takes a long time to load when users select a large date range. Additionally, the profit margin measure, which is calculated as DIVIDE(SUM(Sales[Profit]), SUM(Sales[Revenue])), returns blank for some rows even though both Profit and Revenue have values. You also receive feedback that the mobile layout is not optimized; the visuals are too small and the slicers are not accessible.

You need to address these issues. What should you do?

Accepted Answer

Create aggregations on the fact table in the data source to reduce data volume, verify relationships for the profit margin measure, and use the Power BI mobile layout view to optimize visuals and slicers.. Option C is correct because creating aggregations on the fact table in the data source (e.g., pre-aggregated daily sales) reduces the amount of data loaded into the model, improving load times. The profit margin issue is likely due to a missing relationship or filter context; verifying relationships and using DIVIDE with an alternate result will fix blanks. For mobile layout, using the mobile layout view to resize visuals and place slicers at the top improves accessibility. Option A is wrong because increasing RAM is a temporary fix and does not address the root cause. Option B is wrong because incremental refresh can help with load times but does not fix the profit margin issue or mobile layout. Option D is wrong because changing to DirectQuery might introduce performance issues and does not address the profit margin or mobile layout.

Answer

Implement incremental refresh on the fact table and create a separate measure for profit margin using COALESCE.

Answer

Change the storage mode to DirectQuery for the fact table and use the Power BI Desktop's 'Optimize for mobile' option.

Answer

Increase the capacity of the Power BI service to Premium Per User and enable large dataset storage.

Question 16

You are a Power BI analyst for a multinational retail company. The company's sales data is stored in an Azure SQL Database with tables: Sales (SalesID, Date, ProductID, Quantity, Amount), Products (ProductID, ProductName, Category), and Calendar (Date, Year, Month, Day). The Sales table contains 500 million rows. You are creating a Power BI report to analyze daily sales trends over the past 5 years. The report must allow users to drill from year to month to day using a hierarchy. The performance of the report is critical; users expect visuals to load within 5 seconds. The current model imports all tables without any optimization, and the date hierarchy visual takes over 30 seconds to render. You need to redesign the data model to meet the performance requirement. What should you do?

Accepted Answer

Create an aggregated table in Power Query that groups sales by date, and create a date hierarchy in the Calendar table.. Option B is correct because reducing the granularity of the Sales table by pre-aggregating daily sales reduces the number of rows drastically, improving query performance. Creating a date hierarchy in the Calendar table allows drill-down. Option A is wrong because DirectQuery may still be slow over 500 million rows without aggregation. Option C is wrong because incremental refresh only affects data load time, not query performance. Option D is wrong because calculated columns add to model size and do not help with aggregation.

Answer

Add calculated columns for Year, Month, and Day in the Sales table to avoid using the Calendar table.

Answer

Switch the import mode to DirectQuery to avoid loading all data into memory.

Answer

Implement incremental refresh policy on the Sales table to reduce the amount of data loaded.

Question 17

You are building a Power BI report for the HR department to analyze employee turnover. You have an Employee table with columns: EmployeeID, Name, Department, HireDate, TerminationDate (nullable). The report must include a measure that calculates the number of active employees as of the last day of each month. The data model also includes a Calendar table with a continuous date range. The measure must be accurate regardless of any slicer selections on the Calendar table. You need to write the DAX measure. Which measure should you use?

Accepted Answer

CALCULATE(COUNTROWS(Employee), Employee[HireDate] <= MAX('Calendar'[Date]), (Employee[TerminationDate] > MAX('Calendar'[Date]) || ISBLANK(Employee[TerminationDate]))). Option B is correct because it uses a pattern that calculates employees hired on or before the last date of the month and terminated after that date or still active (TerminationDate is blank). The use of MAX('Calendar'[Date]) ensures it respects the current filter context for the month, and the logic correctly counts active employees. Option A is wrong because it uses MIN instead of MAX, and the logic is reversed. Option C is wrong because it uses a fixed date. Option D is wrong because it sums an expression incorrectly.

Answer

CALCULATE(COUNTROWS(Employee), Employee[HireDate] <= MAX('Calendar'[Date]), Employee[TerminationDate] > MIN('Calendar'[Date]) || ISBLANK(Employee[TerminationDate]))

Answer

SUMX(Employee, IF(Employee[HireDate] <= MAX('Calendar'[Date]) && (Employee[TerminationDate] > MAX('Calendar'[Date]) || ISBLANK(Employee[TerminationDate])), 1, 0))

Answer

CALCULATE(COUNTROWS(Employee), Employee[HireDate] <= TODAY(), ISBLANK(Employee[TerminationDate]))

Question 18

You are a Power BI consultant for a logistics company. The company has a semantic model with a 'Shipments' fact table (containing 'ShipmentID', 'Date', 'OriginCity', 'DestinationCity', 'Weight', 'Cost') and 'City' dimension table (with 'City', 'Region', 'Country'). The company wants a report that displays a map visual with bubbles sized by total shipment weight, and a slicer to select a region. Additionally, they want to be able to click on a bubble (city) and see a table of shipments from that city. You have implemented a map visual using the 'City' field and a table visual. When you click on a bubble, the table does not filter to show only shipments from that city. You have confirmed that cross-filtering is enabled. What is the most likely cause?

Accepted Answer

The relationship between Shipments and City is based on OriginCity, but the table shows DestinationCity, so the filter does not apply.. The map visual uses the 'City' field from the City dimension table. When you click a bubble, it sends a filter on the City table. However, the table visual likely uses fields from the Shipments table (e.g., ShipmentID, DestinationCity). If the relationship between Shipments and City is based on OriginCity, and the table is showing DestinationCity, the filter from the map (on City) may not propagate correctly because the relationship is on OriginCity, not DestinationCity. Option D is correct. Option A is incorrect because the slicer is not the issue. Option B is incorrect because the map should have a legend if needed. Option C is incorrect because the table might have many rows but filtering should work.

Answer

The map visual does not have a legend field, so clicking does not generate a filter.

Answer

The table visual has a filter that prevents cross-filtering.

Answer

The region slicer is interfering with the cross-filtering.

Question 19

You have a Power BI report that uses a date table with a continuous date range. You want to add a slicer that allows users to select a single month and then display data for that month only. You add a slicer visual with the Date field. However, the slicer shows individual dates instead of months. How should you configure the slicer to show months? A. Change the slicer type to 'Between' and then select a month range. B. Create a calculated column for Month and use that in the slicer. C. Use the 'Hierarchy' slicer with Date hierarchy. D. Set the slicer to 'List' and enable 'Show select all'. Which option is best?

Accepted Answer

Create a calculated column for Month and use that in the slicer. Option B is correct because creating a Month column (e.g., 'Month Year') allows users to select a month. Option A is wrong because 'Between' slicer still shows dates. Option C is wrong because hierarchy slicer shows drill-down, not single selection. Option D is wrong because 'Select all' does not group by month.

Answer

Set the slicer to 'List' and enable 'Show select all'

Answer

Use the 'Hierarchy' slicer with Date hierarchy

Answer

Change the slicer type to 'Between' and then select a month range

Question 20

You are working on a Power BI report for a marketing team. The report includes a page with a line chart showing website visits over time, and a table showing the top 5 marketing campaigns by conversion rate. The marketing manager wants to be able to click on a point in the line chart (representing a specific date) and have the table update to show only campaigns that were active on that date. The campaigns data includes a 'StartDate' and 'EndDate'. You have implemented a measure to filter campaigns based on the selected date. However, when you click on a date point in the line chart, the table does not update. What should you do to enable this interaction?

Accepted Answer

In the 'Edit interactions' settings, ensure that the line chart cross-filters the table.. The issue is likely that cross-filtering between the line chart and table is disabled. By default, visuals cross-filter each other, but if it's turned off, clicking won't affect others. Option A is correct: enable the cross-filter interaction. Option B is incorrect because drillthrough is for page navigation. Option C is incorrect because bookmarks are not needed. Option D is incorrect because editing interactions is the correct approach but the specific action is to enable cross-filtering, not disable.

Answer

In the 'Edit interactions' settings, disable cross-filtering between the line chart and table.

Answer

Create a drillthrough page and configure the line chart to drill through to the table page.

Answer

Add a bookmark for each date and set the line chart to trigger the bookmark on click.