PL-300 Practice Test 20 — 15 Questions

Question 1

You have two tables: 'Orders' and 'Customers'. You want to create a relationship where each order is linked to one customer, but a customer can have many orders. Which cardinality should you choose?

Accepted Answer

Many-to-one (*:1) from Orders to Customers. The correct choice is B, many-to-one (*:1) from Orders to Customers, because each individual order row references exactly one customer row, while the same customer key can appear in many order rows — which is precisely the *:1 direction when viewed from Orders toward Customers. In relational terms this is implemented by placing a foreign key in Orders pointing to the Customers primary key, enforcing referential integrity without duplicating customer data. Option A (*:*) is wrong because many-to-many requires a junction table and would let one order map to multiple customers, contradicting the scenario. Option C (1:* from Orders to Customers) reverses the direction, implying one order relates to many customers. Option D (1:1) is wrong because it would restrict each customer to at most one order.

Answer

Many-to-many (*:*)

Answer

One-to-many (1:*) from Orders to Customers

Answer

One-to-one (1:1)

Question 2

You are importing a large CSV file (200 MB) into Power BI Desktop. The import is very slow and sometimes fails. What should you do to improve performance?

Accepted Answer

Filter rows and columns during import using Power Query to reduce data size.. Filtering rows and columns via Power Query is the correct approach to reduce the data loaded into the Power BI Desktop model. However, query folding does not apply to CSV files — it is a technique for database sources. For CSV, Power Query reads the entire file but still reduces memory and processing overhead by discarding unnecessary columns and rows before loading into the model. This is a standard best practice for large flat files.

Answer

Use Power BI Service to import the file instead.

Answer

Remove all relationships before import.

Answer

Upgrade to Power BI Premium.

Question 3

You are building a star schema in Power BI. The fact table contains sales transactions. Which of the following should be stored in a dimension table?

Accepted Answer

Product category. In a star schema, dimension tables store descriptive attributes used for filtering, grouping, and labeling fact data. Product category is a descriptive attribute of the product dimension, so it belongs in a dimension table. Fact tables store numeric, additive measures and foreign keys that reference dimensions.

Answer

Sales amount

Answer

Customer ID

Answer

Transaction date

Question 4

Which TWO chart types are appropriate for comparing proportions of a whole? (Select TWO.)

Accepted Answer

100% stacked bar chart. A 100% stacked bar chart (B) is correct because it normalizes each bar to 100%, so every segment represents a category's share of the total, making it ideal for comparing part-to-whole proportions across multiple groups. A pie chart (E) is correct because it divides a single circle into slices whose arc angles are proportional to each category's percentage of the whole, directly visualizing composition. The waterfall chart (A) is not appropriate here because it shows how sequential positive and negative values accumulate to a running total, not proportions of a whole. The scatter plot (C) is used to show the relationship or correlation between two numeric variables, and the line chart (D) is used to display trends over a continuous dimension such as time, so neither compares parts of a whole.

Answer

Waterfall chart

Answer

Scatter plot

Answer

Line chart

Question 5

You are designing a Power BI report that will be viewed on mobile devices. The report contains a complex scatter plot with many data points. Users complain that the visual is hard to interact with on small screens. What is the best approach to improve the mobile experience?

Accepted Answer

Create a separate mobile layout and replace the scatter plot with a simpler visual like a line chart.. The correct option is D: create a separate mobile layout and replace the scatter plot with a simpler visual like a line chart. Power BI's mobile layout feature lets you design a phone-optimized view of the report, and swapping a dense scatter plot for a simpler visual such as a line chart reduces clutter and improves touch interaction on small screens. Options A, B, and C do not solve the core problem: bookmarks still show the complex scatter plot, a slicer does not reduce visual complexity, and enlarging the scatter plot to fill the screen makes the many data points even harder to interact with on a small display.

Answer

Use bookmarks to switch between the scatter plot and a table.

Answer

Keep the scatter plot but add a slicer to filter data.

Answer

Increase the size of the scatter plot to fill the screen.

Question 6

You have the DAX measure shown. The measure returns blank for some periods even though there are sales in the current period. What is the most likely cause?

Accepted Answer

The Date table does not contain all dates from the previous year.. The correct option is A: the Date table does not contain all dates from the previous year. A typical year-over-year DAX measure uses SAMEPERIODLASTYEAR or DATEADD over the marked Date table, and if the Date table is missing dates from the prior year, the time-intelligence function returns an empty set, so the measure evaluates to blank even though current-period sales exist. Time intelligence in DAX requires a contiguous, complete Date table marked as a date table; gaps in prior-year dates break the shifted filter context. Option B is too generic and does not explain blanks tied specifically to prior-year periods. Option C is unlikely because a variable referencing current sales would still evaluate in the current context. Option D is incorrect because DIVIDE returns blank only when the denominator is zero or blank, which would not selectively blank prior-year comparisons when current sales exist.

Answer

The measure is not properly filtered by the current context.

Answer

The variable CurrentSales is not evaluated correctly.

Answer

The DIVIDE function returns blank when denominator is zero.

Question 7

You need to create a measure that calculates the year-over-year growth percentage for sales. Which DAX function combination is most appropriate?

Accepted Answer

CALCULATE with SAMEPERIODLASTYEAR and DIVIDE. SAMEPERIODLASTYEAR returns the equivalent date range shifted back exactly one year, and wrapping it in CALCULATE lets you evaluate the prior-year sales in the current row context; DIVIDE then safely computes the growth ratio (Current − PriorYear) / PriorYear without divide-by-zero errors. This is the standard DAX pattern for year-over-year percentage growth in Power BI and Analysis Services Tabular models. Option A is less appropriate because DATEADD requires an explicit number and interval and is more error-prone for a simple YoY shift, while SAMEPERIODLASTYEAR is purpose-built for this. Option C's TOTALYTD computes a year-to-date aggregate, not a prior-year comparison, so it cannot produce YoY growth. Option D's PREVIOUSMONTH shifts by one month, not one year, so it answers a month-over-month question instead.

Answer

CALCULATE with DATEADD and SUM

Answer

TOTALYTD and DIVIDE

Answer

PREVIOUSMONTH and SUM

Question 8

Which THREE of the following are valid considerations for choosing between Import and DirectQuery storage modes? (Select three.)

Accepted Answer

Import mode provides faster query performance for aggregated data. Option A is correct because Import mode loads a compressed copy of the data into the Power BI in-memory engine (VertiPaq), so queries against aggregated data are served from memory and are typically much faster than DirectQuery, which pushes queries to the source. Option C is correct because Import mode datasets are constrained by capacity memory limits — for example, a 1 GB dataset size limit per dataset in shared/Pro capacity — which is a genuine factor when deciding between the two modes. Option E is correct because DirectQuery sends queries directly to the underlying source at query time, so it reflects near real-time data changes without the scheduled refresh latency required by Import mode. Option B is wrong because DirectQuery does not support all DAX functions; some functions are restricted or behave differently, and certain calculated columns/measures are unsupported. Option D is wrong because DirectQuery can query large data sources — it is often chosen precisely to avoid importing huge volumes, since processing happens at the source.

Answer

DirectQuery mode supports all DAX functions without limitations

Answer

DirectQuery mode cannot query large data sources

Question 9

You have a report that contains a map visual showing sales by city. Several cities are missing from the map because the location data is ambiguous. What should you do to resolve this?

Accepted Answer

Create a calculated column that combines city and country to provide unambiguous location.. The correct option is B: create a calculated column that combines city and country to provide unambiguous location. When a map visual can't place a city because multiple places share the same name, supplying a more specific, hierarchical location string (for example, concatenating City and Country) gives the geocoding engine enough context to resolve each point uniquely. Option A is unnecessary and less precise here because adding raw latitude/longitude is a heavier modeling change than disambiguating the existing location field, and it doesn't address the root cause of ambiguous city names. Option C does not fix the data ambiguity—it merely abandons the map visualization. Option D is irrelevant, since bubble size affects rendering of plotted points, not the geocoding of missing locations.

Answer

Add latitude and longitude fields to the model.

Answer

Replace the map visual with a table.

Answer

Adjust the bubble size to make missing points visible.

Question 10

Which THREE actions can help optimize a Power BI report's performance? (Select THREE.)

Accepted Answer

Reduce the number of visuals on a single page.. Option B is correct because each visual on a report page issues its own DAX queries against the dataset, so reducing the number of visuals per page lowers the number of concurrent queries and the rendering workload, improving report responsiveness. Option D is correct because pre-summarizing data in the source (for example, using SQL GROUP BY, views, or Power Query aggregation) reduces the volume of data loaded into the model and lets the engine scan smaller tables, which speeds up refresh and query execution. Option E is correct because cross-highlighting and cross-filtering force Power BI to re-query and re-render every affected visual whenever a selection is made; disabling them where interactivity isn't needed avoids that cascading query overhead. Option A is not appropriate because calculated columns are computed at refresh time, stored in the model, and consume memory, whereas measures are evaluated at query time and are the recommended approach for complex aggregations. Option C is not appropriate because importing all columns bloats the model, increases memory usage, and slows refresh and queries; best practice is to import only the columns actually needed.

Answer

Use calculated columns instead of measures for complex aggregations.

Answer

Import all columns from source tables to avoid missing data.

Question 11

A Power BI report uses a composite model combining Import and DirectQuery sources. When a user filters a visual, the report takes a long time to update. The admin wants to diagnose performance issues. Which tool should the admin use?

Accepted Answer

Performance Analyzer in Power BI Desktop.. The correct option is B, Performance Analyzer in Power BI Desktop, because it captures per-visual query timings and DAX query durations as the user interacts with the report, letting the admin pinpoint which visual or filter is slow in a composite model. It records each visual's rendering and query events, so the admin can see whether the delay comes from the Import or DirectQuery portion. DAX Studio (A) is useful for tuning individual DAX queries but does not profile the report's visual-by-visual interaction flow. The Power BI activity log (C) tracks usage and audit events, not query performance per visual, and the on-premises data gateway log (D) only covers gateway traffic for DirectQuery/refresh, not the full report rendering path.

Answer

DAX Studio.

Answer

Power BI activity log.

Answer

On-premises data gateway log.

Question 12

You need to create a date table in Power BI using DAX. Which function should you use to generate a continuous list of dates?

Accepted Answer

CALENDAR.. The CALENDAR function in DAX generates a single-column table of dates that starts from a specified start date and ends at a specified end date, creating a continuous list of all dates in that range. This is the correct function to use when you need to create a date table for time intelligence calculations in Power BI.

Answer

CALCULATE.

Answer

TODAY.

Answer

DATEVALUE.

Question 13

You need to prepare data from a folder containing multiple CSV files with identical structure. What is the most efficient way to load all files into a single table?

Accepted Answer

Use the 'From Folder' data source and then click 'Combine & Transform Data'. The 'From Folder' data source in Power Query automatically detects multiple CSV files with identical structure and provides a 'Combine & Transform Data' button that merges them into a single table in one step. This is the most efficient method as it eliminates the need for manual imports or scripting, leveraging Power Query's built-in file combination logic.

Answer

Import each CSV file separately and then append them

Answer

Write a Python script in Power Query to read and combine files

Answer

Use a dataflow to connect to the folder and apply the 'Combine Files' transformation

Question 14

Refer to the exhibit. You are reviewing a DAX expression used in a Power BI measure. You need to ensure that only users in the 'West' region see data for that region. Which approach should you use?

Accepted Answer

Implement row-level security (RLS) in the dataset with a role filter.. Row-level security (RLS) in the dataset with a role filter is the correct approach because it enforces data restrictions at the model level, so users in the 'West' region automatically see only rows where the region equals 'West' regardless of which report or visual they open. RLS roles are defined in Power BI Desktop and assigned to users or groups in the Power BI Service, providing centralized and secure filtering. Using CALCULATE with a report-page filter (B) only affects calculations or visuals, not the underlying data visible to users, so it can be bypassed. A measure with a conditional statement checking the user's email (C) is not a security boundary and cannot reliably restrict row visibility. A calculated column (D) computes values at refresh time and does not filter data per user, so it cannot enforce regional access.

Answer

Use the CALCULATE function with a filter on the report page.

Answer

Modify the measure to include a conditional statement that checks the user's email.

Answer

Create a calculated column with the same logic.

Question 15

A Power BI admin receives a support ticket that a user cannot see any data in a report that uses row-level security (RLS). The report is based on a dataset with a single table 'Sales' and RLS roles defined. The user is assigned to the role 'SalesManager' which filters Sales[Region] = 'West'. The dataset uses Import mode. The user can see the report but all visuals show blank. What is the most likely cause?

Accepted Answer

The RLS filter removes all rows for the user's role.. The RLS filter removes all rows for the user's role. In Import mode, RLS is enforced by DAX filters applied at query time, so if the 'SalesManager' role filters Sales[Region] = 'West' and the user's identity maps to no rows where Region equals 'West' (for example, due to case/spacing mismatches or the user not being in the intended region), every visual returns blank rather than an error. The other options do not fit: B would typically block report access entirely or show a permission error, not blank visuals; C describes a dashboard tile scenario, but the ticket says the user can see the report; and D is not a valid cause because refreshing with an RLS bypass account affects data loading, not the query-time RLS filtering applied to the user.

Answer

The user does not have permission to view the report page.

Answer

The user is viewing a dashboard tile instead of the report.

Answer

The dataset was refreshed using an RLS bypass account.