PL-300 Practice Test 23 — 15 Questions

Question 1

Your organization uses Power BI and has implemented Microsoft Defender for Cloud Apps (formerly Microsoft Cloud App Security). A user reports that they are unable to export data from a Power BI report. The Power BI tenant settings allow export. What could be the cause?

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

Accepted Answer

A Microsoft Defender for Cloud Apps session policy is blocking the export based on the user's risk level or the report's sensitivity.. The correct answer is D: a Microsoft Defender for Cloud Apps session policy is blocking the export based on the user's risk level or the report's sensitivity. Because Defender for Cloud Apps can proxy Power BI sessions via Conditional Access App Control, its session policies can inspect and block actions like export even when the Power BI tenant setting allows export. Option A is wrong because the scenario states the tenant settings already allow export. Option B is wrong because sensitivity labels alone do not automatically block export; protection is enforced through encryption/permissions, not a blanket export block. Option C is wrong because being an external guest user does not by itself prevent exporting data.

Answer

The 'Export to Excel' setting is disabled in the Power BI admin portal.

Answer

A sensitivity label with high classification is applied to the report, automatically blocking export.

Answer

The user is an external guest user.

Question 2

Which TWO of the following are valid DAX functions for time intelligence? (Select two.)

Accepted Answer

DATEADD. DATEADD is a valid DAX time intelligence function that shifts dates forward or backward by a specified number of intervals (days, months, quarters, years). TOTALYTD is also a valid time intelligence function that calculates the year-to-date value of an expression. Both are part of the dedicated time intelligence function set in DAX, which requires a properly marked date table with continuous dates.

Answer

RANKX

Answer

CONCATENATEX

Answer

MINX

Question 3

A Power BI administrator needs to ensure that reports containing sensitive financial data are only accessible to users who have completed mandatory training and are using compliant devices. The organization uses Microsoft Entra ID and Microsoft Intune. Which feature should the administrator configure?

Accepted Answer

Create a Conditional Access policy that requires device compliance and a specific group membership for training completion. The correct option is C: a Conditional Access policy that requires device compliance and a specific group membership for training completion. Conditional Access in Microsoft Entra ID can enforce both device compliance (via Intune) and group membership as access controls, so only users in the trained group on compliant devices can reach the Power BI reports. Option A is wrong because Microsoft Purview scans and classifies data but does not enforce training-based access at sign-in. Option B is wrong because row-level security filters rows within a dataset, not user training status or device compliance. Option D is wrong because sensitivity labels and MFA do not verify training completion or device compliance.

Answer

Deploy Microsoft Purview to scan the reports and enforce access policies

Answer

Use row-level security (RLS) to filter data based on user training status

Answer

Apply sensitivity labels to the reports and require MFA

Question 4

You are a Power BI report creator for a non-profit organization. You have a semantic model with a 'Donations' fact table (columns: 'DonationID', 'DonorID', 'Date', 'Amount', 'CampaignID') and a 'Donors' dimension table (columns: 'DonorID', 'DonorName', 'City', 'DonorType' (Individual/Corporate)). You need to create a report page that shows a scatter chart with 'Total Donation Amount' on the X-axis and 'Number of Donations' on the Y-axis, with each point representing a donor type. You also want to add a trend line to show the correlation. When you create the scatter chart, only one point appears (for all donors combined), instead of separate points for Individual and Corporate. What is the most likely cause?

Accepted Answer

The DonorType field is placed in the 'Values' well instead of the 'Legend' well.. The correct option is B: the DonorType field must be placed in the Legend well so the scatter chart splits into one point per donor type (Individual and Corporate). In Power BI, the Legend well is what creates separate series/points by category, while the Values well holds the numeric measures (Total Donation Amount and Number of Donations). With DonorType in Values, the chart aggregates everything into a single point, which matches the symptom described. Option A is wrong because an inactive relationship would affect filtering/aggregation, not the number of scatter points, and the model already relates Donations to Donors via DonorID. Option C is wrong because SUM and COUNT are appropriate for total amount and donation count, and incorrect aggregation would not by itself collapse categories. Option D is wrong because scatter charts do support multiple categories via the Legend well; small multiples are not required.

Answer

The relationship between Donations and Donors is inactive.

Answer

The measures are incorrectly defined; they should use SUM and COUNT respectively.

Answer

The scatter chart does not support multiple categories; you need to use a small multiples chart.

Question 5

Which TWO DAX functions can be used to create a calculated table in Power BI?

Accepted Answer

FILTER. FILTER (B) is correct because it is a table-returning DAX function that produces a filtered table, which can be used as the expression of a calculated table (e.g., a table defined as FILTER(Sales, Sales[Amount] > 1000)). SUMMARIZECOLUMNS (D) is also correct because it returns a table grouped by specified columns with aggregated values, making it a valid expression for creating a calculated table. Both functions return table values, which is the requirement for a calculated table's definition. SELECTEDVALUE (A) returns a scalar value from a single-column context, not a table, so it cannot define a calculated table. CALCULATE (C) modifies filter context and returns a scalar value, not a table, so it is invalid here. SUMX (E) is an iterator that returns a scalar aggregate, not a table, so it also cannot create a calculated table.

Answer

SELECTEDVALUE

Answer

CALCULATE

Answer

SUMX

Question 6

You are designing a Power BI report for executives. The dataset contains sales data with a many-to-many relationship between 'Sales' and 'Product' tables via a 'ProductSales' bridge table. Users complain that some measures return incorrect totals when using multiple related fields. What is the most likely cause?

Accepted Answer

The many-to-many relationship is causing ambiguity in measure evaluation. The many-to-many relationship is causing ambiguity in measure evaluation. In a bridge-table (ProductSales) many-to-many model, a single fact row can be reached through multiple product paths, so measures like SUM over Sales can be double-counted or misallocated when users slice by multiple related fields, producing incorrect totals. This is a classic DAX/modeling ambiguity that requires resolving the relationship (e.g., proper bridge filtering or distinct-count logic) rather than a simple setting change. Option B is unlikely because key data type mismatches would typically block relationship creation or cause blanks, not selective wrong totals. Option C is not the root cause, since single vs. both cross-filter direction affects filter propagation but does not by itself create the many-to-many double-counting ambiguity. Option D is unrelated, as RLS would consistently hide rows for restricted users, not produce incorrect aggregate totals for executives with full access.

Answer

Data type mismatches between key columns

Answer

The cross-filter direction is set to single instead of both

Answer

Row-level security (RLS) is filtering out some rows

Question 7

A company has a Power BI semantic model with a table named 'Sales' that contains columns: OrderDate, ShipDate, Quantity, and Revenue. The company wants to create a measure that calculates the total revenue for orders shipped within 7 days of the order date. Which DAX expression should be used?

Accepted Answer

CALCULATE(SUM(Sales[Revenue]), DATEDIFF(Sales[OrderDate], Sales[ShipDate], DAY) <= 7). Options A and C contain the same valid DAX expression: CALCULATE(SUM(Sales[Revenue]), DATEDIFF(Sales[OrderDate], Sales[ShipDate], DAY) <= 7). This expression correctly uses CALCULATE to modify the filter context, applying DATEDIFF to compute the day difference between OrderDate and ShipDate, and filtering for orders shipped within 7 days. Both are correct because they are identical. Option B uses SUMX with FILTER, but direct date subtraction (Sales[ShipDate] - Sales[OrderDate]) in DAX treats dates as serial numbers with time components, which can lead to inaccurate day counts and is not recommended. Option D also uses direct date subtraction in a filter argument, which is similarly incorrect. Therefore, A and C are correct.

Answer

SUMX(FILTER(Sales, Sales[ShipDate] - Sales[OrderDate] <= 7), Sales[Revenue])

Answer

CALCULATE(SUM(Sales[Revenue]), Sales[ShipDate] - Sales[OrderDate] <= 7)

Question 8

Which TWO actions should you take to improve the performance of a DirectQuery model in Power BI? (Select two.)

Accepted Answer

Limit the columns selected to only those needed in the report.. Option A is correct because a DirectQuery model translates each visual into a query against the source, so limiting the columns selected to only those needed in the report reduces the width of the generated SQL and the volume of data transferred, lowering query cost and latency. Option C is correct because pushing filters to the source database as much as possible lets the backend engine apply its own indexing and query optimization, so less data is returned to Power BI and processing is done where it is most efficient. Option B is wrong because calculated columns in a DirectQuery table are computed at query time and can force row-by-row evaluation or even prevent query folding, which typically hurts rather than helps performance. Option D is wrong because aggregations on imported tables apply to Import mode tables, not to a DirectQuery model's source tables. Option E is wrong because row-level security filters add predicates to every DirectQuery query and increase complexity and overhead rather than improving performance.

Answer

Use calculated columns instead of measures to precompute values.

Answer

Create aggregations on the imported tables.

Answer

Implement row-level security filters on the fact table.

Question 9

You have a Power BI report that shows sales by region. The map visual displays regions with incorrect boundaries. What is the most likely cause?

Accepted Answer

Bing Maps geocoding inaccuracies.. The correct answer is D: Bing Maps geocoding inaccuracies. Power BI's map and filled map visuals rely on Bing Maps to geocode location data (such as region names) into geographic shapes, and ambiguous or non-standard region names can be matched to the wrong boundaries, producing incorrect shapes. This is the most likely cause of misdrawn region boundaries, since the visual itself is functioning but the geocoding lookup resolves to the wrong place. Option A is incorrect because visual choice affects suitability, not boundary accuracy. Option B is incorrect because a stale data source would show outdated values, not wrong geographic boundaries. Option C is incorrect because overlapping labels are a formatting/rendering issue, not a cause of incorrect region shapes.

Answer

The map visual is not the best choice for the data.

Answer

The data source is not refreshed.

Answer

The map labels are overlapping.

Question 10

You need to create a relationship between two tables in Power BI. Table A has a column 'ProductID' with unique values. Table B has a column 'ProductID' with duplicate values. Which relationship cardinality should you choose?

Accepted Answer

One-to-many (Table A to Table B). The correct choice is B, One-to-many (Table A to Table B), because Table A's ProductID column contains unique values, making it the 'one' side, while Table B's ProductID column has duplicates, making it the 'many' side; in Power BI this is the standard star-schema relationship where the unique-key table filters the fact table. A one-to-many relationship from Table A to Table B correctly reflects that each ProductID in A can match multiple rows in B. Option A (many-to-one) reverses the direction and would require Table B to be the unique side. Option C (one-to-one) is invalid because Table B has duplicate ProductID values. Option D (many-to-many) is unnecessary and would not be the appropriate model when one side is already unique.

Answer

Many-to-one (Table A to Table B)

Answer

One-to-one

Answer

Many-to-many

Question 11

You have a Power BI report with the DAX measure shown in the exhibit. Users report that the measure returns blank for some months even though sales data exists for both current and previous years. What is the most likely cause?

Accepted Answer

The 'Date' table is not marked as a date table or is missing dates.. The correct answer is B: the 'Date' table is not marked as a date table or is missing dates. Time-intelligence functions such as SAMEPERIODLASTYEAR, DATEADD, and TOTALYTD rely on a contiguous, complete date table marked as a date table; if dates are missing or the table is not marked, the measure returns blank for those months even when underlying sales exist. Option A is wrong because SUM vs. SUMX affects row-context aggregation, not time-intelligence blank results. Option C is wrong because DIVIDE handles division by zero by returning BLANK or an alternate result, which is not the cause of missing months. Option D is wrong because single cross-filter direction is the default and correct setting for a one-to-many Date-to-Sales relationship and does not cause blanks in time intelligence.

Answer

The measure uses SUM instead of SUMX.

Answer

The DIVIDE function is incorrectly handling division by zero.

Answer

The relationship between 'Sales' and 'Date' is set to cross-filter direction single.

Question 12

You are a Power BI developer for a retail company. You have a semantic model that includes a 'Sales' fact table with columns: 'Date', 'ProductID', 'StoreID', 'Quantity', 'UnitPrice'. The 'Product' dimension table includes 'ProductID', 'ProductName', 'Category', 'SubCategory'. The 'Store' dimension table includes 'StoreID', 'StoreName', 'Region', 'District'. You need to create a report page that allows users to analyze sales performance by product category and store region. The report must include a matrix visual with: - Rows: Product Category - Columns: Store Region - Values: Total Sales Amount (Quantity * UnitPrice) Additionally, users must be able to drill down from category to subcategory in the rows, and from region to district in the columns. You also need to ensure that when a user selects a specific store region, the matrix only shows data for that region and its districts. You have created the measures and the matrix visual. However, when you test the drill down, the hierarchy does not work as expected: clicking the expand icon on a category does not show subcategories. What is the most likely cause?

Accepted Answer

The matrix rows do not have a hierarchy defined; Category and SubCategory are separate fields.. The correct answer is C: the matrix rows do not have a hierarchy defined, with Category and SubCategory as separate fields. In Power BI, drill-down in a matrix requires a single field well containing a hierarchy (or nested fields in the Rows bucket) so the expand/collapse icon can navigate from Category to SubCategory; placing Category and SubCategory as independent fields prevents the expected drill behavior. Option A is wrong because a blank Total Sales measure would show empty values, not block the hierarchy expansion. Option B is wrong because a Store Region slicer filters data but does not disable matrix drill-down. Option D is wrong because single-direction relationships affect filter propagation and drillthrough pages, not in-matrix hierarchy expansion.

Answer

The Total Sales measure is incorrectly defined, causing blank values for subcategories.

Answer

A slicer for Store Region is interfering with the matrix drill down behavior.

Answer

The relationship between Sales and Product is set to single direction, preventing drill through.

Question 13

You are building a Power BI report that uses a DirectQuery source. Which TWO of the following actions can improve report performance?

Accepted Answer

Reduce the number of visuals on each report page.. Option C is correct because in DirectQuery mode every visual issues its own queries against the source, so reducing the number of visuals per page directly cuts the number of round trips and the volume of data the source must process. Option E is correct because pre-aggregating data into summary tables in the source means DirectQuery retrieves smaller, already-computed result sets instead of scanning large detail tables, which lowers query cost and latency. Option A is wrong because calculated columns in a DirectQuery model are computed at query time (or force the column to be materialized), adding processing overhead rather than reducing source load. Option B is wrong because disabling 'Reduce queries sent to the source' removes the optimization that consolidates and limits queries, increasing the number of queries hitting the source. Option D is wrong because complex, deeply nested DAX measures translate into more elaborate SQL and heavier source-side computation, degrading rather than improving performance.

Answer

Add calculated columns to the model.

Answer

Disable the 'Reduce queries sent to the source' option.

Answer

Use complex DAX measures with many nested functions.

Question 14

You have a Power BI dataset that uses DirectQuery to a SQL Server data warehouse. You need to ensure that when users view reports, they see only data relevant to their department. The data warehouse contains a 'Department' column. What should you implement?

Accepted Answer

Define row-level security (RLS) roles in Power BI Desktop and assign users.. The correct option is D: defining row-level security (RLS) roles in Power BI Desktop and assigning users, because RLS filters rows at query time based on the user's identity, so each user sees only the rows matching their department in the 'Department' column, and it works with DirectQuery by translating the filter into the source query. This is the standard, scalable way to enforce per-user data visibility in a single dataset. Option A only controls how credentials are passed to the source and does not by itself filter rows by department. Option B is inefficient and does not use a single governed dataset, and Option C, object-level security, restricts access to tables or columns rather than filtering rows by department value.

Answer

Configure the data source credentials to pass the user's identity.

Answer

Create separate datasets for each department and grant access accordingly.

Answer

Implement object-level security (OLS) on the 'Department' column.

Question 15

You have a Power BI model with a table 'Sales' that contains a column 'OrderDate'. You need to create a calculated column that extracts the year from OrderDate. Which DAX expression should you use?

Accepted Answer

YEAR(Sales[OrderDate]). The correct option is B, YEAR(Sales[OrderDate]), because DAX provides the YEAR() function specifically to extract the integer year from a date column, which is exactly what is needed for a calculated column on Sales[OrderDate]. Option A is invalid because DATEPART is a T-SQL function, not a DAX function, so it would not work in a Power BI calculated column. Option C, CALENDAR(YEAR(...), YEAR(...)), returns a single-column table of dates rather than a scalar year value, so it cannot be used as a calculated column expression. Option D, FORMAT(Sales[OrderDate], "yyyy"), returns the year as a text string rather than a numeric value, which is less appropriate for extracting the year for typical numeric or sorting use.

Answer

DATEPART("year", Sales[OrderDate])

Answer

CALENDAR(YEAR(Sales[OrderDate]), YEAR(Sales[OrderDate]))

Answer

FORMAT(Sales[OrderDate], "yyyy")