Microsoft Power BI PL-300 Exam Questions 2025

This solution does not meet the goal because it is highly inefficient for a table with over 100 million records. While Power Query can use query folding to translate the filter step into a native SQL WHERE clause, the Power Query Editor's interactive experience would be severely degraded. Before the filter is even applied, the editor attempts to load metadata and data previews from the entire source table. For a table of this size, this process would be extremely slow and likely result in timeouts, making the development process impractical. The best practice is to filter the data at the source from the very beginning.

A. Yes: This is incorrect. It overlooks the significant performance bottleneck and potential timeouts that occur during the interactive development phase within the Power Query Editor when it first connects to such a large, unfiltered table.

1. Microsoft Docs

Power BI documentation

"Data import best practices in Power BI": Under the "Data transformations" section

the guidance states: "It's best practice to push transformations back to the data source... For relational sources

you can achieve this goal by using a native SQL query." This supports the principle that for large-scale data

relying on UI-driven folding is less robust than explicitly defining the data subset in a native query from the start.

2. Microsoft Docs

Power BI documentation

"Power Query query folding": This document explains the mechanism of query folding. While it confirms that filter operations on SQL sources are typically folded

it does not address the practical performance implications of the development environment when initially connecting to massive tables before those foldable steps are applied. The impracticality of the interactive steps is the reason the proposed solution fails.

3. Microsoft SQLCAT

"Power BI performance best practices": This guide

written by Microsoft's Customer Advisory Team (CAT)

emphasizes performance. In the section on "Data model design

" it recommends minimizing the amount of data imported. The most direct way to do this for a sample is to write a native query (SELECT TOP N ...)

which avoids any interaction with the full

large table within the Power Query interface. The proposed solution contradicts this best practice by first connecting to the entire table.

The goal is to modify existing queries to use the DataSourceExcel parameter, thereby eliminating hard-coded file paths. The proposed solution, creating a new query that references the parameter, does not achieve this. The original queries with the hard-coded paths remain unchanged. To meet the goal, you must edit the Source step of each existing query within the Power Query Editor. In the source settings, the static file path needs to be replaced with a reference to the DataSourceExcel parameter. This centralizes the data source definition, allowing for easy updates.

A. Yes: This is incorrect because creating a new query does not modify the existing queries that contain the hard-coded data source paths, which is the stated goal.

1. Microsoft Learn. (2023). Use parameters in Power BI Desktop. In the section titled "Use a parameter for a data source

" the documentation explicitly details the process of modifying an existing query's source step to use a parameter. It states

"You can then use that parameter in the data source connection string... In Power Query Editor

select the query that uses the data source... Select the gear icon next to the Source step." This confirms the correct procedure is to edit the existing source

not create a new query.

2. Microsoft Learn. (2023). Power Query M formula language - Accessing data functions. The documentation for functions like Excel.Workbook and File.Contents shows that the file path is an argument. To parameterize this

the argument must be replaced with the parameter name (e.g.

Source = Excel.Workbook(File.Contents(DataSourceExcel)

null

true)). This change must be made within the existing query's M code.

A single dataset is the minimum required and simplifies management, security, and maintenance. The Import storage mode is the optimal choice given the scenario's constraints. It loads a compressed snapshot of the data into Power BI's in-memory VertiPaq engine, providing the best possible query performance for interactive reports. Since the data volume is under 1 GB and only needs daily refreshing, Import mode is superior to DirectQuery, which is designed for very large datasets or near real-time requirements and would result in slower report performance.

A. two imported datasets

This is incorrect because if all data can be modeled together, using a single dataset is more efficient and minimizes management overhead.

B. a single DirectQuery dataset

This is incorrect because Import mode offers significantly better performance for datasets of this size. DirectQuery would introduce performance latency by querying the source for every visual interaction.

C. two DirectQuery datasets

This is incorrect for two reasons: it unnecessarily complicates management with multiple datasets, and DirectQuery is not the optimal performance choice for this scenario.

1. Microsoft Learn. (2024). Dataset modes in the Power BI service.

Reference: Under the "Import/cached" section

it states

"This mode offers the fastest performance... Good for smaller datasets that can be entirely imported into the Power BI memory." This supports the choice of Import for performance with sub-1GB datasets.

2. Microsoft Learn. (2023). Use DirectQuery in Power BI Desktop.

Reference: In the "When to use DirectQuery" section

it highlights use cases such as "data is changing frequently

and near 'real-time' reporting is needed" or "data is very large." The assumed scenario does not meet these criteria

making DirectQuery suboptimal.

3. Microsoft Learn. (2023). Data reduction techniques for Import modeling.

Reference: This document emphasizes the importance of keeping import models as lean as possible to "ensure fast performance

" "reduce memory consumption

" and "speed up data refresh." This reinforces the best practice of using a single

optimized imported dataset when possible.

The Import storage mode is the most common and provides the highest query performance. It loads a copy of the data into the Power BI dataset, where it is compressed and optimized by the in-memory VertiPaq engine. This mode allows for the full use of Power Query transformations and the entire DAX function library. It also enables the integration of data from multiple, diverse data sources into a single, cohesive semantic model. Given these advantages in performance and functionality, Import is the default and often preferred choice for creating a new semantic model unless specific requirements like near-real-time data or massive data volumes necessitate other modes.

A. A live connection connects to a pre-existing data model (like SSAS or another Power BI dataset) and does not allow for creating a new model or combining sources.

B. Dual is a table-level setting within a composite model that allows a table to act as either Import or DirectQuery, used for performance optimization, not a mode for the entire model.

D. DirectQuery leaves data at the source and is used for very large datasets or near-real-time needs, but it has significant performance and DAX/Power Query limitations.

1. Microsoft Documentation. (2023). Dataset modes in the Power BI service. Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/power-bi/connect-data/service-dataset-modes-understand. (See section: "Import mode").

2. Microsoft Documentation. (2024). Use DirectQuery in Power BI Desktop. Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/power-bi/connect-data/desktop-directquery-about. (See section: "Limitations of DirectQuery").

3. Microsoft Documentation. (2024). Use composite models in Power BI Desktop. Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/power-bi/transform-model/desktop-composite-models. (See section: "Storage mode").

4. Microsoft Documentation. (2023). Connect to Power BI datasets from Power BI Desktop. Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/power-bi/connect-data/desktop-report-lifecycle-datasets. (See section: "Live connection to the Power BI service").

The goal is to parameterize the file path of an Excel data source within Power Query. This is an action performed in the Power Query Editor to manage data sources dynamically. The proposed solution, adding a Power Apps custom visual, is an action performed on the Power BI report canvas. The Power Apps visual is used to embed an interactive application within a report; it has no capability to modify the M code or the data source connection settings in the Power Query Editor. Therefore, this solution does not meet the goal.

A. Yes: This is incorrect because adding a Power Apps visual is a report design feature for user interactivity and has no functional connection to managing or parameterizing data sources in the Power Query Editor.

1. Microsoft Learn. (2023). Use parameters in Power BI. This document details the correct procedure for creating and using parameters to manage data sources. The process involves using the "Manage Parameters" feature within the Power Query Editor

not adding visuals to the report.

Reference Section: "Create a parameter" and "Use a parameter for a data source".

2. Microsoft Learn. (2023). Power Apps visual for Power BI. This official documentation explains that the Power Apps visual allows you to embed a canvas app in a Power BI report. Its purpose is to create interactive

write-back scenarios on the report canvas

not to manage the report's underlying data source paths.

Reference Section: "Overview of the Power Apps visual".

A trend line is an analytical feature that can be added directly to a scatter plot within Power BI. Its primary function is to visually represent the overall trend or relationship between the two numeric variables plotted on the X and Y axes. Adding a trend line is a visual-level modification that does not alter the underlying data model. Therefore, it directly helps users identify patterns in the scatter plot as requested, without affecting the accuracy of the card or the bar chart, which rely on the complete, unfiltered dataset.

A. Applying a row filter in Power Query Editor modifies the source data loaded into the model, which would incorrectly change the results of all visuals, violating the stated requirement.

C. High-density sampling is a performance feature for rendering large numbers of data points, not an analytical tool for identifying trends. It helps visualize the shape but doesn't add an interpretive layer.

D. Adding a field to the size bucket converts the visual to a bubble chart. This introduces a third data dimension, which could obscure the primary relationship between transaction amount and profit.

1. Microsoft Learn. (2023). Use trend lines in Power BI.

Reference Details: In the "Create and use trend lines" section

it states

"A trend line is a line that shows the trend of a series of data... Trend lines can help you spot trends in your data

which can help you make better predictions." This confirms that trend lines are a primary tool for pattern identification.

2. Microsoft Learn. (2023). High-density sampling in Power BI scatter charts.

Reference Details: The "How high-density scatter sampling works" section explains

"The high-density sampling algorithm... starts by creating a grid of points... The algorithm then removes points in that grid that are empty. This approach preserves the overall shape of the data..." This highlights its purpose for performance and visual representation of shape

not analytical trend analysis.

3. Microsoft Learn. (2023). About Power Query in Power BI.

Reference Details: The "Where you can use Power Query" section clarifies that transformations made in the Power Query Editor are applied to the data before it is loaded into the Power BI model. This confirms that a filter applied here (Option A) would affect the entire report.

Power BI Q&A operates directly on a semantic model, which is referred to as a dataset in the Power BI service. To customize the Q&A experience, such as adding featured or suggested questions, you must modify the settings of the underlying dataset. These settings allow you to guide users by providing pre-defined questions, which helps them understand the scope of the data and the types of queries they can successfully run. This configuration is centralized at the dataset level, ensuring consistency across all reports and dashboards that use it.

A. Subscriptions: Subscriptions are used for scheduling and distributing reports and dashboards via email, not for configuring the Q&A feature.

B. Dashboards: Dashboard settings control properties like the theme and tile arrangement. While Q&A is used on a dashboard, its underlying configuration is managed in the dataset.

D. Workbooks: Workbooks refer to Excel files that can be used as a data source. The Q&A settings are applied to the Power BI dataset, not the source workbook itself.

1. Microsoft Learn. (2023). Create featured questions for Power BI Q&A.

Reference: In the "Create featured questions" section

Step 1 explicitly states: "In the Power BI service

select the ellipsis (...) next to the dataset you want to create featured questions for

and then choose Settings." This directly confirms that the configuration is done in the Dataset settings.

2. Microsoft Learn. (2023). Intro to Q&A featured questions.

Reference: The article states

"In the dataset settings page

you can add your own featured questions." This reinforces that the dataset is the correct location for this setting.

3. Microsoft Learn. (2023). Q&A setup for Power BI.

Reference: Under the "Get to the Q&A setup pages" section

the instructions are: "To access the Q&A setup pages

in the Power BI service

select the ellipsis (...) next to a dataset and choose Settings." This document covers the entire Q&A setup process

which is centered on the dataset.

The visual in the exhibit displays a quantitative value (Sum of Sales) progressing over a time-based category (Quarter). The key feature is that the area between the x-axis and the trend line is filled in, which is the defining characteristic of an area chart. This type of chart emphasizes the magnitude of change over time. An area chart can be created directly in Power BI by placing the 'Quarter' field on the X-axis and the 'Sales' measure on the Y-axis, requiring no additional DAX measures, thus meeting all the requirements of the question.

A. ribbon chart: A ribbon chart is used to visualize rank change over time. The exhibit does not show multiple categories or any indication of changing ranks.

B. line chart: A line chart connects data points with a line but does not fill the area below it. The exhibit explicitly shows a filled area.

C. 100% stacked area chart: This chart type shows the proportional contribution of different series to a whole, with the Y-axis always totaling 100%. The exhibit shows a single series with absolute values.

1. Microsoft Learn. (2023). Area charts in Power BI.

Reference: In the "Create a basic area chart" section

the documentation states

"The basic area chart...is based on the line chart. The area between the axis and line is filled with colors to indicate volume." This directly describes the visual in the exhibit.

2. Microsoft Learn. (2023). Ribbon charts in Power BI.

Reference: The "When to use a ribbon chart" section explains that these charts are best for showing "how a given category's rank changes throughout the visualization's x-axis." This function is not represented in the question's exhibit.

3. Microsoft Learn. (2023). Line charts in Power BI.

Reference: The documentation describes and illustrates line charts as connecting data points with a line

which visually differs from the filled-in area chart shown in the question's exhibit.

The goal is to create a reference line representing the median salary. The median is the statistical measure that represents the middle value of a dataset, which is equivalent to the 50th percentile. Power BI's Analytics pane for visuals like the clustered bar chart allows for the creation of dynamic reference lines. By adding a percentile line, using the Salary measure as the basis, and setting the percentile value to 50%, you are instructing Power BI to calculate and display a line at the median salary. This correctly meets the stated requirement.

B. No: This option is incorrect. The proposed solution is a standard and accurate method for displaying the median value on a Power BI visual. The 50th percentile is, by definition, the median.

1. Microsoft Learn. (2023). Use the Analytics pane in Power BI Desktop.

Section: "Apply a percentile line"

Content: This document explicitly describes the functionality to add a percentile line to a visual. It states

"Select Percentile line from the Add line dropdown... Then select the measure you want to use for the percentile... and the percentile you want." This confirms the technical feasibility of the proposed solution.

2. Microsoft Learn. (2023). PERCENTILEX.INC function (DAX).

Section: "Remarks"

Content: The documentation for the DAX function that calculates percentiles states

"If k is zero or blank

percentile rank of 1/(n-1) returns the smallest value. If zero

it is out of range and an error is returned. If k is non-numeric or outside the range 0 to 1

an error is returned." Setting the k-value (percentile) to 0.5 calculates the 50th percentile

which is the median. This confirms the underlying statistical principle used by Power BI.

3. The Pennsylvania State University. (n.d.). STAT 200: Elementary Statistics

Lesson 2: Measures of Central Tendency and Dispersion.

Section: "Percentiles"

Content: This university courseware defines the relationship between median and percentile: "The median is the 50th percentile; the point in the data where 50% of the data fall below that point and 50% fall above it." This provides academic validation for the core premise of the solution.

Row-Level Security (RLS) roles are defined within a Power BI dataset in Power BI Desktop. After the report and its dataset are published to the Power BI service, you must configure the security by assigning users or security groups to these roles. This configuration is performed on the dataset's security settings page. The dataset is located within a Power BI workspace. In this scenario, the "Sales reports" resource is the Power BI workspace where the dataset resides. Therefore, the assignment of members to the "Consultants" role is managed within the context of the "Sales reports" workspace.

A. Group2: A security group is an entity that can be assigned to an RLS role to grant its members access, but the role itself is not assigned to the group.

B. Team1: A Microsoft Team's underlying Microsoft 365 group can be assigned to an RLS role, but the role is not assigned to the Team.

D. Group 1: A Microsoft 365 group can be assigned to an RLS role, but the role is not assigned to the group.

1. Microsoft Learn. (2023). Row-level security (RLS) with Power BI.

Reference: Under the section "Manage security on your model

" the documentation states: "After you publish your report to the Power BI service

you set up RLS role assignment there. In the Power BI service

you can add members to a role that you created in Power BI Desktop." The procedure described involves navigating to the workspace

selecting the dataset

and then choosing the "Security" option to assign members to the role. This confirms the workspace is the container where this action takes place.

URL: https://learn.microsoft.com/en-us/power-bi/connect-data/desktop-row-level-security

2. Microsoft Learn. (2024). Restrict data access with row-level security (RLS) for Power BI.

Reference: In the section "Manage security on your model

" the steps are outlined: "1. In the Power BI service

go to the workspace where you saved your report. 2. In the workspace content list

find the dataset... 3. Select More options (...) for the dataset

and then select Security." This explicitly shows that the management of RLS role assignments occurs on the dataset within its workspace.

URL: https://learn.microsoft.com/en-us/power-bi/enterprise/service-admin-rls

3. Microsoft Learn. (2023). Set up row-level security in the Power BI service.

Reference: In this training module

Step 2 of the procedure is: "In the Power BI service

browse to the workspace and then open the menu for the dataset... Select Security." This reinforces that the workspace ("Sales reports" in the question) is the location where you access the dataset's security settings to manage RLS roles.

URL: https://learn.microsoft.com/en-us/training/modules/power-bi-admin-rls/3-setup-rls