Microsoft Power BI PL-300 Exam Questions 2025

A. subscriptions: Subscriptions are used to schedule and email snapshots of reports and dashboards to users. They do not provide a mechanism for feedback or collaborative issue tracking.

C. alerts: Data alerts are automated notifications triggered when data in a dashboard tile crosses a predefined threshold. They are not a tool for users to manually report issues.

D. chat in Microsoft Teams: While Power BI content can be shared in Teams for discussion, this creates a separate communication channel. The comments feature is a more integrated, native solution within the Power BI service itself.

1. Microsoft Learn. (2023). Add comments to dashboards and reports in the Power BI service.

Section: "Add a general comment" and "Comment on a specific visual"

Content: This document explicitly states, "You can add comments to an entire dashboard... You can also @mention others to pull them into the conversation." It confirms that comments are a collaborative feature visible to those with access.

2. Microsoft Learn. (2023). Email subscriptions for Power BI reports and dashboards.

Section: "Subscribe to a report or dashboard"

Content: This source details the functionality of subscriptions, which is to "receive an email with a snapshot of the report or dashboard." This confirms it is a content delivery system, not a communication tool.

3. Microsoft Learn. (2023). Data alerts in the Power BI service.

Section: "Set data alerts in the Power BI service"

Content: This document explains that alerts are used to "get notified when data in your dashboards changes above or below limits you set." This confirms alerts are for automated data monitoring, not user-initiated communication.

A. Modifying dataset permissions affects all reports and users connected to that shared dataset, violating the requirement that other reports should not be affected.

B. This option explicitly allows data export, which increases the risk of data exfiltration, directly contradicting the goal of the question.

D. Row-level security (RLS) is applied at the dataset level, which would affect all reports using that dataset. It restricts data access but does not prevent the export of visible data.

1. Microsoft Learn. (2023). Change report settings in the Power BI service.

Section: "Export data"

Content: This document details the report-level settings for data export. It explicitly lists the option to set export permissions to "None," which "prevents your report readers from exporting any data from your report." This confirms it is a report-specific control.

2. Microsoft Learn. (2024). Build permission for shared datasets.

Section: "Ways to get Build permission"

Content: This document clarifies that "Build permission is a dataset-level permission." Modifying it, as suggested in option A, would impact anyone trying to build content from the shared dataset, thus affecting more than just report1.

3. Microsoft Learn. (2024). Row-level security (RLS) with Power BI.

Section: "Set up RLS"

Content: The documentation explains that RLS is configured on the model (dataset) in Power BI Desktop and then managed in the Power BI service. This confirms RLS is a dataset-level feature and would apply to all reports using that dataset.

The DAX measure [Accounts] is designed to count the number of unique accounts. A standard implementation for this would be DISTINCTCOUNT(Balances[AccountID]). This measure operates within the filter context provided by the visual.

• Day Level: When the table visual is at the day level, the filter context for the measure is a single day. The measure will count the distinct accounts that have a record in the Balances table for that specific day, which correctly represents the number of accounts live on that day.
• Month and Year Level: When the context is expanded to a month or a year, the DISTINCTCOUNT function will count every unique account that had a balance record on at least one day within that period. It does not ensure the account was active for every single day of the period. An account active for only one day in a year will be counted the same as an account active for all 365 days. Therefore, the measure does not show accounts that were live throughout the entire month or year.

Microsoft Corporation. (n.d.). DISTINCTCOUNT function (DAX). Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/dax/distinctcount-function-dax

This document states that the function "counts the number of distinct values in a column." When applied over a date range, it counts all distinct values that appear at any point within that range.

Microsoft Corporation. (n.d.). Understanding filter context. Power BI Documentation. Retrieved from https://learn.microsoft.com/en-us/power-bi/transform-model/desktop-evaluation-context#filter-context

This resource explains how visuals, such as a table grouped by a date hierarchy, create a filter context that constrains DAX calculations to a subset of data (e.g., a specific day, month, or year).

Russo, M., & Ferrari, A. (2019). The Definitive Guide to DAX: Business intelligence for Microsoft Power BI, SQL Server Analysis Services, and Excel (2nd ed.). Microsoft Press.

Chapter 4, "Evaluation Contexts," explains in detail how filter context is applied at different granularities and how measures like DISTINCTCOUNT operate within that context. It confirms that a distinct count over a period includes items present at any point in the period, not necessarily throughout.

Summarizing Orders (Yes): This is a standard data reduction technique. By summarizing or aggregating the Orders table (which is likely at a transaction line level), you reduce the total number of rows. A new table grouped by CustomerID, OrderID, and OrderDate would contain one row per order instead of one row per product line within an order. This significantly reduces the model size. To support the "Total sales over time" analysis, a new column for the total sales amount would be created during this summarization process. The other analyses—counting orders and analyzing customers—remain possible because OrderID, CustomerID, and OrderDate are retained.

Removing CustomerID (No): Removing the CustomerID column from the Orders fact table would break the relationship with the Customers dimension table. While this would reduce the model size, it would make it impossible to perform the "New and repeat customer counts" analysis, as there would be no way to link an order to a specific customer.

Removing UnitPrice and Discount (No): These columns are necessary to calculate the Total Sales measure (e.g., Sales = Quantity * UnitPrice * (1 - Discount)). Removing them would make it impossible to support the "Total sales over time" analysis, even though it would reduce the model size.

Microsoft Power BI Documentation: In the guidance on data reduction techniques for Import modeling, summarizing data is a key strategy. The documentation states, "Use Power Query to group by columns, and aggregate your data... It can result in a significant data reduction." This directly supports the first statement, as summarizing is a valid and effective method to reduce model size while maintaining analytical capability by pre-aggregating measures. (See: Microsoft Docs, Power BI guidance, Data reduction techniques for Import modeling).

Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley. Chapter 2 discusses the grain of a fact table. The process described in the first statement is equivalent to raising the grain of the fact table from an individual transaction line to the order header. This is a fundamental technique in dimensional modeling to manage performance and data volume for large datasets. Removing foreign keys (Statement 2) or measure source columns (Statement 3) violates basic dimensional modeling principles for supporting the required analyses.

B. A parameter is a user-controlled input for interactive analysis, not a security feature. A user could easily change the parameter value to view data from other departments.

C. The CURRENTGROUP DAX function is used within grouping functions like SUMMARIZECOLUMNS to perform calculations on grouped data; it is not related to user-based security.

D. A slicer is a visual filter on the report canvas that users can freely interact with. It does not enforce security, as a user could modify or remove the slicer.

1. Microsoft Learn. (2024). Restrict data access with row-level security (RLS) for Power BI. Microsoft Power BI Documentation. Retrieved from https://learn.microsoft.com/en-us/power-bi/enterprise/service-admin-rls.

Reference Details: The section "Define roles and rules in Power BI Desktop" explicitly states, "You can define roles and rules within Power BI Desktop. When you publish to Power BI, it also publishes the role definitions... To define security roles, follow these steps. 1. Import data into your Power BI Desktop report, or configure a DirectQuery connection. ... 2. From the Modeling tab, select Manage Roles." This directly supports the process described in option A.

2. Microsoft Learn. (2024). Implement row-level security. PL-300: Design and build a data model in Power BI learning path. Retrieved from https://learn.microsoft.com/en-us/training/modules/design-build-tabular-model-power-bi/7-implement-row-level-security.

Reference Details: This official PL-300 courseware notes, "Row-level security (RLS) is a Power BI feature that can be used to restrict data access for given users. Filters restrict data access at the row level, and you can define filters within roles... You will implement RLS by creating one or more roles. A role has a unique name in the model, and it will typically include one or more rules."

3. Microsoft Learn. (2024). Row-level security (RLS) guidance in Power BI Desktop. Power BI Guidance Documentation. Retrieved from https://learn.microsoft.com/en-us/power-bi/guidance/rls-guidance.

Reference Details: The section "Implement RLS" details the process: "In Power BI Desktop, you can create a role by navigating to the Modeling ribbon and selecting Manage roles. In the Manage roles window, you can create, modify, or delete roles... You add a rule to a table by using a DAX expression that must return a TRUE or FALSE value." This confirms that creating roles and defining rules is the correct procedure.

The UnitsInStock column has its Summarize by property set to None. When a numeric column with this setting is added to a table visual, Power BI does not aggregate the data. Instead, it displays each individual value on a separate row. Since the column has 75 non-null values, the table will display 75 rows.

Changing the Summarize by setting from 'None' to an aggregation function (such as Sum, Average, or Count) alters this behavior. When the column is subsequently added to a table, Power BI will perform the specified calculation and display a single, aggregated result (assuming no other grouping columns are present). This changes the output from 75 individual rows to a single summary row, thus reducing the number of rows.

Microsoft Power BI Documentation: In the "Use aggregation in Power BI Desktop" article, it states: "If you set the summarization to Do not summarize, then whenever you add that column to a visual, Power BI will simply show each of its values." This confirms that without summarization, all individual values are listed.

Source: Microsoft Learn, Power BI Documentation, "Use aggregation (sum, average, etc.) in Power BI Desktop," Section: "Change the aggregation type."

Microsoft Power BI Documentation: The default behavior for numeric fields is aggregation. As explained in the documentation, "When you add a numeric field...to a visual in a Power BI report, it's aggregated by default. It might be a sum, average, count...". Enabling summarization (by changing it from 'None') causes the data to be aggregated into fewer rows, typically one if it's the only field in the values well.

Source: Microsoft Learn, Power BI Documentation, "Intro to aggregations in Power BI," Section: "Why use aggregations?"

The process of implementing Row-Level Security (RLS) in Power BI follows a specific workflow. First, you must bring your data into Power BI Desktop. Once the data model is available, you define the security rules by creating roles and applying DAX filter expressions to the tables. After defining the roles within the .pbix file, the report must be published to the Power BI service. The final step, which can only be done in the service, is to navigate to the dataset's security settings and assign specific users or groups to the roles you created, thereby activating the security restrictions for them.

Microsoft. (2024). Row-level security (RLS) with Power BI. Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/power-bi/enterprise/service-admin-rls.

Details: The "Set up roles in Power BI Desktop" section outlines the initial steps: "1. Import data into your Power BI Desktop report... 2. From the Modeling tab, select Manage Roles." This supports the first two steps in the sequence. The "Manage security on your model" section details the subsequent actions: "...you need to publish your report to the Power BI service... In the Power BI service, you can assign members to the role." This confirms the final two steps.

A. Changing summarization is a metadata setting that affects default report behavior; it does not reduce the data model's storage size.

B. Changing the UnitCost data type to Integer would truncate decimal values (e.g., 12.02 becomes 12), resulting in a loss of information.

C. Removing the relationship between the tables would break the ability to analyze inventory by date, which is a critical loss of analytical capability.

1. Microsoft Learn. (2023). Data reduction techniques for Import modeling. In Power BI guidance.

Reference: Under the section "Remove unnecessary columns," the documentation states, "Columns that don't serve a purpose should be removed from your model... It's a best practice to remove any columns that aren't used for relationships, measures, or displaying in visuals." In this scenario, MovementDate is redundant because DateKey serves the relationship purpose.

2. Microsoft Learn. (2023). Understand star schema and the importance for Power BI. In Power BI guidance.

Reference: This document describes that fact tables should contain foreign keys (like DateKey) and numeric measures. Descriptive attributes (like a full date) should reside in the related dimension tables. The presence of MovementDate in the fact table violates this principle when a proper Date dimension exists, confirming its redundancy.

In dimensional modeling, the goal is to create a star schema consisting of a central fact table and multiple surrounding dimension tables.

1. Fact Tables: Contain quantitative measures and foreign keys to dimension tables. The Attendance data from View2 is transactional and forms the basis of the Attendance fact table.
2. Dimension Tables: Contain descriptive, categorical attributes that describe the factual data. Attributes like teacher names, class subjects, and period times are used to slice and dice the measures in the fact table.

Teacher First Name is a descriptive attribute of a teacher entity. The best practice is to create a separate Teacher dimension containing fields like Teacher ID, Teacher First Name, and Teacher Last Name. This avoids data redundancy and organizes the model logically.

Period Number is required to analyze attendance counts by period. For the Attendance fact table to be filterable by period, it must contain a key related to the period. Since the source data for the facts (View2) already includes Period Number, it should be included in the Attendance fact table as a foreign key that links to a Period dimension.

Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.

Chapter 2, "Retail Sales": This chapter introduces the core concepts of fact and dimension tables. It explains that descriptive textual attributes (like a teacher's name) belong in dimension tables, while numeric keys that connect facts to dimensions belong in the fact table (p. 25-29).

Microsoft Power BI Documentation. (2023). "Understand star schema and the importance for Power BI."

Section: "Fact tables": The documentation states, "The fact table contains dimension key columns that relate to dimension tables...". This confirms that keys needed for slicing data, like Period Number, must be present in the fact table to link to their respective dimension tables.

Section: "Dimension tables": This section clarifies that dimension tables "store the attributes that describe your business entities," which directly applies to placing Teacher First Name in a Teacher dimension.

A. Publish the datasets in an app: This bundles content for consumption but does not affect the ranking of datasets in discovery lists.

B. Promote the datasets: While promotion increases visibility, certification provides a higher level of endorsement and a higher rank in search results, aligning with "corporate quality standards."

C. Feature the dataset on the home page: This provides a shortcut on the Power BI Home page but does not influence the order of datasets in search results.

1. Microsoft Learn. (2023, October 24). Endorsement: Promote and certify Power BI content. In the "How endorsement works" section, it states, "Certified content is ranked first in lists and search results, and is marked with a certification badge." It also notes, "Certification is a bigger deal than promotion. You can certify content only if your Power BI admin has configured and enabled certification for your organization." This directly supports that certification is the correct action for datasets verified against corporate standards to achieve top ranking.

2. Microsoft Learn. (2023, October 24). Enable content certification. In the "Set up content certification" section, it explains, "Certification is a way to highlight quality content. To ensure that only authoritative content gets certified, Power BI admins decide who can certify content." This aligns with the scenario's need to elevate datasets that meet "corporate quality standards."

3. Microsoft Learn. (2023, October 24). Data discovery using the data hub. The article illustrates how endorsed datasets are displayed prominently in the Data hub, stating, "The list of datasets shows you the workspace they reside in, as well as whether they're certified or promoted." This confirms that certification directly impacts visibility in user search experiences.