PL-300 Exam Questions and Answers

The on-premises data source is misconfigured, causing the refresh to load data from an outdated source.

Correct. A misconfigured on-premises data source in the Power BI service — such as a gateway data source entry pointing to the wrong server, database, or folder, or using credentials for a different environment — can cause the refresh engine to successfully connect to and load data from an outdated or unintended location. Because the refresh completes without error, the service reports success even though the dataset is populated from the wrong source. This perfectly matches the scenario: a successful refresh that yields stale data.

Configure a deployment pipeline with parameter rules to override the data source.

Power BI Deployment Pipelines allow you to assign parameter rules (e.g., for data source parameters like Server and Database) that override values automatically when content is deployed from dev to test to production. This is the correct choice because it is the only option that truly meets the requirement of the report automatically using the production connection string without any manual adjustment. The rules are configured once in the pipeline and then applied consistently on every deployment, making the process repeatable and governed.

Add an Microsoft Entra ID group to the workspace role.

Adding an Microsoft Entra ID group to the workspace role is the correct, recommended approach for scalable access management in Power BI. When you assign the group to a role such as Viewer, Contributor, Member, or Admin, all current and future members of that group automatically receive the corresponding permissions on the workspace and its content. This centralizes identity governance in Microsoft Entra ID: adding or removing a user from the group instantly reflects in Power BI, with no per-workspace or per-report edits needed. It aligns with enterprise security best practices and simplifies auditing and compliance.

The data source credentials are incorrect or expired.

Incorrect or expired data source credentials are the most frequent cause of a Power BI scheduled refresh failure. When you configure a dataset for refresh, Power BI stores the authentication values—Windows credentials, database passwords, or OAuth tokens—and if those are changed or time out, the service cannot authenticate to the source and the refresh operation fails with an error. Updating the credentials in the dataset settings under 'Edit credentials' resolves the issue, and you should verify that the account still has the same permissions.

The dataset parameters were not configured in the deployment pipeline rules.

Dataset parameters, such as server name or database name, can be overridden per stage using deployment pipeline rules. If no rule is configured for the relevant parameter, Test inherits the parameter values from Development, causing the dataset to keep pointing to the Dev data source. Creating a parameter rule that assigns the Test SQL server/database value for that parameter is the required fix. Without this rule, the pipeline will successfully deploy the dataset but still reference the wrong environment.

Power BI Pro

Power BI Pro is the correct minimum license because scheduled refresh in the Power BI service is a Pro-only feature. With a Pro license, you can create datasets, publish them to shared capacity, and configure a refresh schedule without needing Premium capacity or Premium Per User. A Pro license is sufficient for the standard, recurring data refresh scenario in shared capacity.

Perform aggregation in SQL before importing.

Performing aggregation in SQL before importing is a classic pushdown optimization that leverages the database engine to pre-summarize the sales data, so only the aggregated results are transferred to Power BI. This dramatically reduces the row count and the size of the imported dataset, leading to faster refreshes, lower model memory usage, and quicker report responses. It also offloads compute from Power BI to the SQL server, which is generally more scalable for large fact tables.

The source table has duplicate 'OrderID' values.

The 'Orders' table has a column designated as a key (likely OrderID) that must contain unique values for the model's relationships to work. When the refresh tries to load data, the VertiPaq engine checks uniqueness on that key; duplicate OrderID values in the source violate that constraint, causing the refresh to fail with a duplicate key error. This often happens when the source view or query returns repeated rows due to joins or missing DISTINCT, and it is the direct and primary cause of the reported error.

Append Queries

Append Queries is the correct tool because it stacks rows from two or more queries vertically, creating a single output table that contains every record from each input. In Power Query, this operation—equivalent to UNION ALL in SQL—is used when the inputs share a common column schema, such as merging January and February sales records. Appending does not alter existing rows or add columns; it simply lengthens the dataset.

Create a separate date table in Power Query with a fiscal year column.

A separate date table created in Power Query is the recommended way to handle fiscal-year reporting because it provides a clean, continuously dated calendar dimension that can be marked as the date table in your model. You can add a fiscal year column with a simple conditional formula that adjusts the calendar year based on your fiscal year start month. This supports DAX time intelligence functions like TOTALYTD and DATEADD, and keeps the date dimension independent from fact tables.

Disable load for intermediate queries used only for reference.

Disabling load on intermediate queries prevents their result sets from being materialised into the dataset, eliminating unnecessary storage and refresh work. Only the final query's output is loaded, so the refresh engine processes less data and completes faster, directly improving refresh performance.

Filter rows at the source to reduce data volume.

Filtering rows at the source reduces the volume of data transferred and processed during each refresh, directly satisfying the performance constraint in the stem. Less data means shorter extraction, transformation and load times, particularly valuable for large or wide tables where unnecessary historical rows inflate refresh duration.

Remove unused columns from the table in Power Query.

Power Query column removal reduces the data volume loaded and compressed into the VertiPaq model, cutting refresh time proportionally. With no calculated columns or measures to preserve, dropping unused columns is the most direct lever on refresh duration for a single-table import model.

There is no active relationship between the Sales and Regions tables.

In Power BI, an active relationship is what enables automatic filter propagation between tables; without one, Power BI cannot traverse from the Sales table to the Regions table to retrieve RegionName. When a foreign key column like RegionID is placed in a visual and no active relationship links it to the Regions table, Power BI simply shows the raw ID value because it has no way to perform the lookup. The correct fix is to create or activate a relationship between Sales[RegionID] and Regions[RegionID] (typically with a single-direction filter), so that the relationship becomes active and the report can display the corresponding RegionName.

Line chart

A line chart encodes data points along a continuous time axis and connects them with straight lines, allowing the eye to perceive direction, rate of change, and periodicity. Because the x-axis is continuous and time-ordered, Power BI can compute and overlay a trend line using linear regression, moving average, or other built-in analytics in the Analytics pane. It is the default and recommended visual for time-series trend analysis because it preserves the sequential structure of the data.

There is no visual-level filter to exclude blank values.

To show only employees who have at least one sales record, a visual-level filter must be applied on the Amount field to exclude blank values (e.g., 'Amount is not blank' or 'Amount > 0'). Without such a filter, the table visual displays every row from the Employee dimension, even those without any related Sales rows, because Power BI's default behavior is to show all dimension rows unless a filter explicitly removes them. A visual-level filter on a measure or column from the fact table is the standard technique to restrict the visual to only employees with sales.

CALCULATE(SUM(Sales[Amount]), DATESINPERIOD('Date'[Date], MAX('Date'[Date]), -12, MONTH))

DATESINPERIOD is the correct time-intelligence function here because it returns a contiguous interval ending at MAX('Date'[Date]) and extending back 12 full calendar months, respecting month boundaries rather than fixed day counts. With -12 and MONTH, the filter context established by CALCULATE adjusts the Sales[Amount] summation to include exactly the trailing 12 months relative to the latest visible date, which is exactly what a rolling 12-month total requires.

SAMEPERIODLASTYEAR

SAMEPERIODLASTYEAR is a time-intelligence function that returns a table of dates shifted exactly one year back from the current filter context, preserving the same day, month, and quarter boundaries. When used inside CALCULATE, it recalculates a measure over that prior-year date range, making it the precise foundation for a year-over-year growth calculation. Because it respects the current filter context and automatically handles leap years by shifting to the nearest valid date, it is the standard choice for comparing any arbitrary period—month, quarter, or cumulative range—to the same period in the previous year.

Use a DAX filter that references the USERPRINCIPALNAME() function

With RLS, you create a role whose DAX filter uses USERPRINCIPALNAME() to identify the current user—for example, filtering a 'Manager' column to match the user's UPN, or using LOOKUPVALUE to return all employees reporting up to that manager. Because USERPRINCIPALNAME() is evaluated per user at query time, a single role dynamically resolves the correct data scope for any manager without per-user configuration. This is the standard pattern for hierarchical or manager-based row-level security.

Enable 'Certification' for dataflows in the Power BI tenant settings.

Enabling the 'Certification' tenant setting for dataflows activates the endorsement feature that lets authorized reviewers officially certify reusable dataflows. Once certified, those dataflows are the trusted building blocks that dataset authors can be required to use, and the tenant switch is a prerequisite for applying governance policies that mandate certified dataflows. Without this setting, dataflow certification is impossible, making it the correct control for enforcing that datasets use only certified dataflows.

Enable 'Certification' for data sources in the Power BI tenant settings.

Enabling 'Certification' for data sources in tenant settings lets an organization mark supported external connections, such as SQL Server databases, as certified so that dataset authors can identify and prefer approved sources. This directly supports the goal of ensuring datasets use only certified data sources, and it is the complementary setting to dataflow certification when governance must span both the raw source connection and the transformation layer. It provides the metadata foundation needed to implement policies that require certified data sources.

Ask the user to update the data source credentials in the Power BI service dataset settings.

The error indicates stored credentials expired or were changed, so refreshing them in the dataset settings restores authentication. Because the source uses Windows authentication, the user must re-enter valid credentials in the Power BI service, directly resolving the refresh failure before investigating gateways or permissions.

Enable the tenant setting 'Apply sensitivity labels to exported data' in the Power BI admin portal.

The tenant-level admin setting named 'Apply sensitivity labels to exported data' directly controls whether an export carries the source report's sensitivity label. When enabled, files exported from labeled items in the Power BI service receive the corresponding label, which preserves data classification and protection outside the service. Without this setting, exported content loses the label even if the original report is labeled.

Disable 'Share content with external users' in the tenant settings.

The correct approach is to disable 'Share content with external users' in the tenant settings, because this switch directly governs whether users can share dashboards and reports with external email addresses. When turned off, the Share dialog rejects external recipients entirely, blocking both direct sharing and app access via external users. This is the primary tenant-level control for preventing outside access to dashboards without disabling internal collaboration.

Disable 'Publish to web' in the tenant settings.

Disabling 'Publish to web' in the tenant settings is also required, because this setting prevents users from generating public embed links that make reports or dashboards accessible to anyone on the internet, bypassing normal sharing rules. Even if external sharing is disabled, a published web link remains exposed until this feature is turned off and existing embed codes are removed. This addresses a different attack surface than the share dialog, making it a vital complement to external user restrictions.

The OAuth2 token used for the data source credentials has expired.

OAuth2 tokens are issued with a limited lifetime (usually 1 hour to 90 days depending on the provider) and are used for authentication when connecting to cloud data sources. When a token expires, the Power BI service cannot refresh the dataset automatically because it lacks the ability to prompt for reauthentication. A manual refresh opens a dialog that lets the dataset owner re-authenticate, renewing the token and successfully completing the refresh.

Build

Build permission specifically grants the right to connect to the dataset as a source for new reports and to create new visualizations in the Power BI service or Power BI Desktop. It is the minimal permission because it satisfies the user's need to author new sales reports without exposing write capabilities or allowing resharing. With Build, the user can create and save new reports, but cannot modify the dataset or grant access to others.

Create a one-to-many relationship from the date table to the sales table and mark the date table as a date table.

This is the correct design: Power BI time intelligence functions (e.g., DATESYTD, DATEADD) rely on a date table that is explicitly marked with the Mark as Date Table option, and a one-to-many relationship from the date table to the sales table ensures each date filters its associated sales rows unambiguously. Marking the date table lets the engine identify the date column for time-based calculations, while the one-to-many cardinality matches the logical model where each calendar day can appear in many fact records. This star-schema pattern supports reliable, accurate time-series reporting.

The measure might be using ALL or ALLEXCEPT that removes the filter context from the Date table.

A measure that uses ALL('Date') or ALLEXCEPT('Date') inside CALCULATE explicitly removes the filter context from the entire Date table or from all Date columns except those specified. For instance, CALCULATE(SUM(Orders[Amount]), ALL('Date')) would ignore a slicer on Date[Year], because ALL('Date') clears every filter applied to the Date table, including the Year column. ALLEXCEPT('Date', 'Date'[Month]) would preserve a Month filter but still ignore Year if Year is not in the exceptions list. This is the classic cause when a measure appears unresponsive to slicer selections, even though the underlying relationships and directions are perfectly normal.

Use Power Query in Power BI Desktop to import both sources and merge/append queries as needed.

Power Query in Power BI Desktop allows importing data from both on-premises SQL Server and cloud-based Salesforce, enabling merging/append operations to shape data into a star schema. This in-memory model supports all relationships and calculations needed.

Add a calculated column in Power Query: NetAmount = Quantity * UnitPrice - Discount, then create a measure: SUM(Sales[NetAmount])

Creating a calculated NetAmount column in Power Query (M) evaluates Quantity * UnitPrice - Discount once at refresh time, storing the result as a static column in the data model. The subsequent measure SUM(Sales[NetAmount]) simply aggregates those pre-computed values, avoiding row-by-row evaluation at report time and improving query performance for large fact tables. Because the calculation is pushed to the query engine instead of the DAX engine, it also keeps the code simpler and avoids iterator overhead during visual rendering.

Remove unnecessary columns from the fact table.

Removing unnecessary columns from the fact table is correct because DirectQuery operates by pushing queries back to the source, and every extraneous column widens the SELECT statement, increasing network transfer and source-side processing. A narrower fact table means fewer columns are scanned and materialized for each visual interaction, which directly reduces query latency and memory overhead. This is a standard column-pruning practice for DirectQuery performance tuning.

OrderID

OrderID is a natural business key that identifies an order, not a numeric measure or a surrogate foreign key. In a star schema, natural keys and descriptive attributes should live in the appropriate dimension table, such as an Order dimension, so that the fact table contains only relationship keys and measures. Including OrderID directly in the fact table violates star schema normalization and reduces maintainability. Thus, OrderID is the item that should NOT be placed directly in the fact table.