PL-300 Visualize and analyze the data Practice Test 7 — 15 Questions

Question 1

Which TWO DAX functions can be used to create a dynamic title that shows the currently selected year in a report, assuming a date table with a Year column?

Accepted Answer

CONCATENATEX(VALUES('Date'[Year]), 'Date'[Year], ", "). Option D, SELECTEDVALUE('Date'[Year]), is correct because SELECTEDVALUE returns the single value of the Year column when exactly one year is in filter context, and returns blank otherwise, which is exactly the behavior needed to display the currently selected year in a dynamic title. Option C, CONCATENATEX(VALUES('Date'[Year]), 'Date'[Year], ", "), is also correct because it iterates over the distinct Year values in the current filter context and concatenates them into a text string, allowing the title to show the selected year (or multiple years if several are selected). Option A, SUM('Date'[Year]), is not appropriate because summing year numbers produces a meaningless numeric total rather than the selected year label. Option B, DISTINCT('Date'[Year]), is a table function that returns a table, not a scalar value, so it cannot directly produce a title string. Option E, CALCULATE(VALUES('Date'[Year])), is invalid as written because CALCULATE requires a scalar expression as its first argument, and VALUES returns a table.

Answer

SUM('Date'[Year])

Answer

DISTINCT('Date'[Year])

Answer

CALCULATE(VALUES('Date'[Year]))

Question 2

Which THREE steps are required to set up row-level security (RLS) in Power BI Desktop?

Accepted Answer

Use the View as Roles feature to test the security filters.. Option D is correct because setting up RLS always begins in Power BI Desktop by creating one or more roles and defining the DAX filter expressions (for example, [Region] = "West" or USERPRINCIPALNAME()) in the Manage Roles dialog, which is the core definition of row-level security. Option A is correct because the View as Roles feature lets you validate that each role's DAX filters return the intended rows before publishing, catching misconfigured filters early. Option B is correct because RLS only takes effect for real users after the dataset is published and members or security groups are assigned to the roles in the dataset's Security settings in the Power BI service. Option C is not required because an on-premises data gateway is only needed for scheduled refresh or DirectQuery to on-premises sources, not for defining or applying RLS. Option E is not required because roles and DAX filters must be created in Power BI Desktop before publishing; publishing first is neither necessary nor a valid setup step.

Answer

Install and configure an on-premises data gateway.

Answer

Publish the report to the Power BI service before defining roles.

Question 3

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.. The performance issue is due to the large Sales table (500 million rows) being aggregated on the fly for the date hierarchy. Creating an aggregated table in Power Query that pre-groups sales by date reduces the data volume and allows the date hierarchy to be built on a smaller, optimized table. This improves query performance and meets the 5-second requirement.

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 4

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 CALCULATE with COUNTROWS and filters Employee to rows where HireDate is on or before the last date in the current Calendar context (MAX('Calendar'[Date])) and either TerminationDate is after that same last date or is blank, which precisely counts employees active as of the end of each month and remains accurate under Calendar slicer selections. Option A is wrong because it compares TerminationDate to MIN('Calendar'[Date]) instead of MAX, so it can count employees who terminated mid-period and does not evaluate active status as of the month-end date. Option C is wrong because SUMX iterates the Employee table without CALCULATE context transition, so MAX('Calendar'[Date]) is evaluated in the row context rather than the filter context and the measure will not respond correctly to Calendar slicers. Option D is wrong because it uses TODAY() and only checks ISBLANK(TerminationDate), so it ignores the Calendar table entirely and cannot produce active counts as of each month-end.

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 5

You have a Power BI report that uses a live connection to an Azure Analysis Services tabular model. Users report that the report is slow to load. You need to improve performance without changing the data model. What should you do?

Accepted Answer

Implement report-level measures to pre-calculate complex aggregations.. Option A is correct because with a live connection to Azure Analysis Services, the tabular model cannot be modified, so report-level measures (created in the report using DAX) can pre-calculate complex aggregations and reduce the work sent to the server, improving load time. Option B is wrong because 'Load to report' is not a valid setting for a live-connected dataset, and importing data would change the connection mode and require model changes. Option C is wrong because the data model resides in Azure Analysis Services and cannot be altered in this scenario. Option D is wrong because adding more visuals per page increases rendering and query load, making the report slower, not faster.

Answer

Enable 'Load to report' on the dataset to import data into Power BI.

Answer

Reduce the number of relationships in the data model.

Answer

Increase the number of visuals on each report page to reduce the number of pages.

Question 6

You are designing a Power BI report for executive leadership. The report must display key performance indicators (KPIs) with current values and trend arrows. Which visual type should you use?

Accepted Answer

KPI visual. The KPI visual is specifically designed to show a current value, a status indicator (like trend arrow), and a goal. Option A is wrong because a table visual is for detailed data, not KPIs. Option B is wrong because a card visual only shows a single value without trend. Option D is wrong because a gauge visual shows progress toward a goal but not a trend arrow.

Answer

Table visual

Answer

Card visual

Answer

Gauge visual

Question 7

You have a Power BI report that uses a DirectQuery dataset from Azure Synapse Analytics. Users complain that applying a filter on a date slicer takes over 30 seconds. The underlying table contains 500 million rows. You need to improve slicer responsiveness. What should you do?

Accepted Answer

Create a date dimension table in the source and use it for the slicer.. The correct option is C: create a date dimension table in the source and use it for the slicer. In a DirectQuery dataset over a 500-million-row Synapse table, filtering a slicer on a raw date column forces a costly DISTINCT/scan against the huge fact table, whereas a small, indexed date dimension lets the slicer query a tiny table and then filter the fact table via a relationship, dramatically improving responsiveness. Option A does not reduce the slicer's own query cost, and option B is irrelevant because adding visuals does not make the slicer's DirectQuery faster. Option D is not viable because importing a column into a DirectQuery model would break the single-source DirectQuery design and cannot be used as a slicer against the DirectQuery fact table without a supported relationship.

Answer

Disable cross-filtering between the slicer and other visuals.

Answer

Add more visuals to the page so the slicer filter is reused across visuals.

Answer

Import the date column into Power BI and use it for the slicer.

Question 8

You have a Power BI report with a page that contains a matrix visual showing sales by year and quarter. Users want to be able to drill down from year to quarter and then drill up again. The matrix currently shows only year. What should you configure?

Accepted Answer

Add both Year and Quarter fields to the matrix rows and enable drill-down mode.. Enabling the drill-down mode and adding both Year and Quarter to the matrix rows allows users to drill down and up. Option A is wrong because enabling drill-down without adding Quarter to the rows won't allow drill-down. Option B is wrong because adding a drillthrough page is for navigating to a detail page, not for drilling down in the same visual. Option D is wrong because adding a hierarchy to the columns would affect column drill-down, not row drill-down.

Answer

Add a hierarchy to the matrix columns.

Answer

Enable the drill-down mode on the matrix visual.

Answer

Add a drillthrough page that filters by year.

Question 9

You have a Power BI report that uses a composite model with a DirectQuery source from Azure SQL Database and an imported table from Excel. You need to ensure that when a user filters the imported table, the DirectQuery table is also filtered accordingly. What should you configure?

Accepted Answer

Set the relationship direction to filter the DirectQuery table from the imported table.. Option A is correct because in a composite model, relationships between an imported table and a DirectQuery table can be configured so that filters flow from the imported table to the DirectQuery table, which is exactly what is needed for the imported table's filter to propagate to the DirectQuery table. This is achieved by setting the relationship's cross-filter direction to single, with the imported table on the "one" side and the DirectQuery table on the "many" side, allowing the filter to flow from imported to DirectQuery. Option B is not appropriate because merging the imported table into the DirectQuery source would change the model architecture and is not a configuration for filter propagation. Option C is incorrect because bidirectional cross-filtering is not required and can introduce ambiguity or performance issues; single-direction filtering from imported to DirectQuery is sufficient. Option D is not valid because calculated columns in a DirectQuery table cannot reference columns from an imported table in a composite model, as they are evaluated at the source and cannot access imported data.

Answer

Merge the imported table into the DirectQuery source.

Answer

Enable bidirectional cross-filtering on the relationship between the tables.

Answer

Create a calculated column in the DirectQuery table that references the imported table.

Question 10

You are designing a Power BI report to analyze sales performance across multiple regions. The data includes a Date table with a Many-to-One relationship to Sales. You need to ensure that users can drill down from Year to Quarter to Month in a matrix visual. What must you configure on the Date table?

Accepted Answer

Mark the Date table as a date table. The correct option is D: Mark the Date table as a date table. In Power BI, marking a table as a date table ensures that the engine recognizes the date column as a proper time dimension, which enables accurate time intelligence and reliable drill-down behavior (Year → Quarter → Month) in visuals like the matrix. Without this designation, Power BI may auto-generate an implicit date hierarchy that can behave inconsistently, especially with multiple date columns or non-contiguous dates. Option A is insufficient because the built-in hierarchy is not guaranteed to be recognized as a formal date table, and it can cause incorrect aggregation. Option B is not required for drill-down; the relationship already exists as Many-to-One, and the relationship column does not need to be the date column. Option C is unnecessary because Power BI can derive Year, Quarter, and Month from a marked date table without manually adding calculated columns.

Answer

Use the built-in date hierarchy without marking the table

Answer

Create a relationship between Date and Sales on the Date column

Answer

Add calculated columns for Year, Quarter, and Month

Question 11

You have a Power BI report that uses DirectQuery to a SQL Server database. Users report that the report is slow when filtering by a slicer on a large dimension table. You need to improve performance without changing the data source. Which approach should you take?

Accepted Answer

Set 'Assume Referential Integrity' on the relationship between tables. The correct option is C: Set 'Assume Referential Integrity' on the relationship between tables. In a DirectQuery model, enabling this setting tells Power BI that the dimension's key values always exist in the fact table, so it can generate INNER JOINs instead of the more expensive OUTER JOINs with additional null-checking logic, which speeds up slicer filtering on a large dimension. Options A and B are not valid fixes here: an aggregation table would change the model and typically requires Import/Dual storage, and switching the dimension to Dual storage mode would cache it but does not by itself optimize the DirectQuery join behavior for this scenario. Option D changes the data granularity and semantics of the slicer rather than improving query performance, so it does not meet the requirement without altering the data source.

Answer

Create an aggregation table on the dimension

Answer

Change the storage mode of the dimension table to Dual

Answer

Reduce the cardinality of the slicer field by grouping values

Question 12

You are building a Power BI report from a large dataset (10 GB) stored in Azure Synapse Analytics. The dataset includes a fact table with 500 million rows. You need to minimize the amount of data imported into Power BI while still allowing users to filter by year and product category. Which approach should you use?

Accepted Answer

Configure Incremental Refresh with a filter on year. Incremental Refresh with a filter on year lets Power BI import only the recent or relevant year partitions into the dataset while keeping the large 10 GB Synapse fact table from being fully loaded, and it still supports user filtering by year and product category. This directly minimizes imported data volume for a 500-million-row fact table while preserving the required filter dimensions. Option A does not reduce imported data because DirectQuery leaves the data in the source and queries it live, which can hurt performance and does not satisfy the import-minimization goal. Option B reduces columns but still imports all 500 million rows, and Option C creates an aggregation table that changes granularity rather than simply limiting the imported fact data.

Answer

Use DirectQuery to avoid importing data

Answer

Import only the necessary columns from the fact table

Answer

Create an aggregation table on year and product category

Question 13

You are designing a Power BI dashboard for executive review. The dashboard must include a card visual showing the total sales for the current year. You need to ensure that the card visual updates automatically when data refreshes. Which TWO actions should you take?

Accepted Answer

Configure a scheduled refresh for the dataset. Option A is correct because configuring a scheduled refresh on the dataset ensures the underlying data in the Power BI service is periodically updated from the source, so the card visual reflects current-year sales after each refresh cycle. Option D is correct because enabling page auto-refresh in the report settings causes the report page (and its visuals, including the card) to automatically re-query the dataset at a defined interval, keeping the displayed value current without manual interaction. Option B is incorrect because there is no per-visual 'refresh every 5 minutes' setting in Power BI; refresh cadence is controlled at the dataset or page level. Option C is incorrect because a measure recalculating on every interaction only recomputes within the already-loaded data model and does not pull fresh data from the source. Option E is incorrect because there is no 'refresh on page load' report setting; page refresh behavior is governed by the page auto-refresh feature, not a load-time trigger.

Answer

Set the visual to refresh every 5 minutes

Answer

Use a measure that recalculates on every interaction

Answer

Set the report to refresh on page load

Question 14

You have a Power BI report that uses a composite model with a DirectQuery source and an imported table. Users report that some visuals show incorrect totals when using measures that reference both sources. Which THREE conditions must be met to ensure correct results?

Accepted Answer

Ensure relationships are defined with appropriate cardinality and cross-filter direction. Option A is correct because in a composite model, relationships between DirectQuery and imported tables must have the right cardinality (one-to-many, many-to-one, etc.) and cross-filter direction (single or both) so that filter context propagates correctly across storage engines; incorrect cardinality or bidirectional settings can produce wrong totals. Option C is correct because measures must aggregate at the granularity that matches the model's relationships and source data; aggregating at a mismatched grain causes double-counting or missing rows when combining DirectQuery and imported data. Option E is correct because setting 'Assume Referential Integrity' on relationships between sources lets Power BI use INNER JOIN semantics instead of LEFT OUTER JOIN, which improves correctness and performance when the underlying data truly has matching keys. Option B is not required because Dual storage mode is only needed when a table must be queried both in import and DirectQuery modes, not as a blanket setting for both sources. Option D is not a real correctness requirement; 'Reduce queries' is a performance-related report setting, not a condition for accurate totals.

Answer

Set both sources to Dual storage mode

Answer

Enable 'Reduce queries' in report settings

Question 15

You are reviewing a DAX query that returns total sales by year and quarter. However, the result includes rows for quarters with no sales. Which modification should you make to exclude empty rows?

Accepted Answer

Add a filter: 'FILTER(ALL('Date'), NOT ISBLANK([Total Sales]))' inside SUMMARIZECOLUMNS. SUMMARIZECOLUMNS returns rows for all combinations of grouping columns, even if the measure returns BLANK. To exclude rows where the measure is blank, you can apply a filter like FILTER(ALL('Date'), NOT ISBLANK([Total Sales])) to remove those rows. Option B is incorrect because SUMMARIZECOLUMNS does not automatically exclude blank rows; it returns blank rows by default. Option C is incorrect because using ADDCOLUMNS with SUMMARIZE would not inherently exclude blank rows. Option D is incorrect because NATURALINNERJOIN would only keep matching rows, but it would not filter based on the measure value.

Answer

No modification is needed; SUMMARIZECOLUMNS already excludes blank rows

Answer

Replace SUMMARIZECOLUMNS with ADDCOLUMNS(SUMMARIZE(...))

Answer

Wrap the SUMMARIZECOLUMNS in a NATURALINNERJOIN with Sales table