12 real PL-300 sample questions, each with a worked explanation and a rationale for every option, right and wrong. No account, no card. This is the reasoning the PL-300 tests: knowing why the tempting answer is wrong, not just spotting the right one.
The real PL-300 is Typically 40 to 60 questions questions in 100 minutes, pass mark 700 / 1000. For a domain-by-domain breakdown and a study plan, read the PL-300 study guide. The full bank has 224 questions.
lock_openFree sampleModel the Datamedium
Salespeople can be assigned to multiple sales regions, and each region has multiple salespeople. You want to relate Salesperson and Region while keeping the model robust and following Microsoft's recommended pattern. Which approach should you use?
- ACreate a bridging table that holds salesperson and region keys and relate each dimension to the bridge.check_circle Correct
- BConfigure a direct many-to-many relationship between Salesperson and Region with single cross-filter direction.
- CDenormalise Region attributes into the Salesperson table so the model only needs one dimension table.
- DPromote Salesperson to a fact table and load Region as a dimension with one-to-many cardinality.
Use a bridging (factless fact) table to relate two dimensions in a many-to-many association. The star-schema guidance describes a factless fact table acting as a bridging table as the best practice when relating two dimensions. The bridge stores salesperson and region keys with duplicates allowed in both columns and each dimension joins to the bridge.
Why A is correct: Correct. The star-schema guidance describes a factless fact table acting as a bridging table as the best practice when relating two dimensions.
Why B is wrong: Direct many-to-many is supported but the guidance recommends a bridging (factless fact) table when relating two dimensions, because it preserves clearer filter propagation and is documented as the best practice.
Why C is wrong: Denormalisation collapses the relationship and breaks the ability to filter independently by region; it does not represent the many-to-many association.
Why D is wrong: Salesperson is a dimension entity, not a measurable event; making it a fact table misclassifies it and does not solve the many-to-many problem.
lock_openFree sampleModel the Datamedium
You want a measure to ignore an existing relationship between Customer and Sales without changing the model. Which DAX function lets the calculation disable filter propagation for that one relationship?
- ACROSSFILTER with a direction of None applied inside CALCULATE.check_circle Correct
- BUSERELATIONSHIP applied inside CALCULATE to swap to an inactive copy.
- CTREATAS applied to inject filters from an unrelated table.
- DCOMBINEVALUES applied to merge keys into a single composite column.
CROSSFILTER with None disables a relationship for the scope of a CALCULATE without removing it from the model. The CROSSFILTER DAX function modifies the relationship cross-filter direction to one or both, or disables filter propagation when set to None. It is the documented way to change or ignore relationships during the evaluation of a specific calculation.
Why A is correct: Correct. The CROSSFILTER DAX function modifies the relationship cross-filter direction to one or both, or disables filter propagation when set to None.
Why B is wrong: USERELATIONSHIP engages an inactive relationship; it does not disable an active one for the duration of an expression.
Why C is wrong: TREATAS creates a virtual relationship from an unrelated table; it does not disable an existing model relationship.
Why D is wrong: COMBINEVALUES supports multi-column relationships in DirectQuery; it has no role in disabling propagation.
lock_openFree sampleModel the Datamedium
A data steward proposes relying on Power BI model relationships to enforce referential integrity between Sales and Product. The team is asked whether this is safe. Do Power BI model relationships enforce data integrity?
- AYes
- BNocheck_circle Correct
Model relationships only propagate filters; integrity must be enforced upstream. Microsoft documentation states explicitly that model relationships do not enforce data integrity. Enforcement remains the responsibility of the data source or upstream pipeline; the model relationship only governs filter propagation.
Why A is wrong: Answering Yes would lead the steward to remove genuine integrity controls and rely on the model, which can silently let orphan facts into the warehouse.
Why B is correct: Correct. Microsoft documentation states explicitly that model relationships do not enforce data integrity.
lock_openFree samplePrepare the Datamedium
An analyst uses Power BI dataflows in the service and also Power Query in Power BI Desktop. The Power Query Online experience and Power Query for Desktop expose almost the same user experience for shaping data in every scenario. Is this statement correct?
- AYescheck_circle Correct
- BNo
Power Query Online and Power Query Desktop deliver almost the same user experience for shaping data. The Power Query overview explicitly notes that although two Power Query experiences exist, Online and Desktop, both provide almost the same user experience in every scenario.
Why A is correct: Correct. The Power Query overview explicitly notes that although two Power Query experiences exist, Online and Desktop, both provide almost the same user experience in every scenario.
Why B is wrong: The two experiences differ in a few features, such as fuzzy grouping being Online-only and Percentile and Count distinct values aggregations being Online-only, but the overall ribbon-driven shaping experience is parity-matched.
lock_openFree samplePrepare the Dataeasy
A report author wants Automatic Page Refresh on a page that uses an Import semantic model. Automatic Page Refresh in Power BI is supported on Import-mode pages. Is this statement correct?
- AYes
- BNocheck_circle Correct
Automatic Page Refresh works only on DirectQuery data sources, not Import. The documentation states Automatic Page Refresh is only available for DirectQuery data sources; the minimum refresh interval depends on workspace type and capacity admin settings.
Why A is wrong: Answering 'Yes' would be incorrect; per the documented behaviour, the keyed answer above is the only consistent reading of the scenario.
Why B is correct: Correct. The documentation states Automatic Page Refresh is only available for DirectQuery data sources; the minimum refresh interval depends on workspace type and capacity admin settings.
lock_openFree samplePrepare the Datamedium
An analyst designs a composite model with a DirectQuery Sales fact and Dual Date and Customer dimensions. Which behaviors does the documentation attribute to Dual mode tables? (Select 2 answers)
- ADual tables can return cached data when queried alone and can revert to DirectQuery when needed.check_circle Correct
- BCombining a Dual column with a DirectQuery fact column submits the aggregation to the source.check_circle Correct
- CDual tables always return data from the in-memory cache regardless of which other tables join the query.
- DDual tables are unsupported alongside DirectQuery fact tables in the same composite semantic model.
- EDual tables are limited to row-level DAX expressions and cannot reference columns on other tables.
Dual tables hit the cache when queried alone but fold joins to source when combined with DirectQuery facts. Storage-mode documentation says queries that refer to Dual tables alone can return cached data and otherwise revert to DirectQuery, and that a query combining a Dual column with a DirectQuery table column submits the SUM/GROUP BY to the source and returns only the aggregate. Dual is recommended specifically to keep relationships regular.
Why A is correct: Correct. Dual tables can return cached data when queried alone and can revert to DirectQuery when needed is one of the keyed answers. Storage-mode documentation says queries that refer to Dual tables alone can return cached data and otherwise revert to DirectQuery, and that a query combining a Dual column with a DirectQuery table column submits the SUM/GROUP BY to the source and returns only the aggregate.
Why B is correct: Correct. Combining a Dual column with a DirectQuery fact column submits the aggregation to the source is one of the keyed answers. Storage-mode documentation says queries that refer to Dual tables alone can return cached data and otherwise revert to DirectQuery, and that a query combining a Dual column with a DirectQuery table column submits the SUM/GROUP BY to the source and returns only the aggregate.
Why C is wrong: Dual tables do not always hit the cache; queries that combine a Dual column with a DirectQuery column are sent to the source so the join can be pushed down.
Why D is wrong: Dual is explicitly the recommended mode for shared dimension tables that participate with DirectQuery fact tables to keep relationships regular.
Why E is wrong: The row-level calculated-column restriction is described for DirectQuery; calculated columns on imported (and Dual when behaving as Import) tables can reference other tables.
lock_openFree sampleVisualize and Analyze the Dataeasy
A pricing team needs a visual that reveals correlations and clusters between unit price and units sold across hundreds of SKUs. Which built-in visual matches the documented intent?
- AUse a stacked column chart with unit price as the legend across each SKU.
- BUse a scatter chart with unit price on one axis and units sold on the other.check_circle Correct
- CUse a donut chart with each SKU contributing a slice of the total.
- DUse a card visual showing the average of unit price across the SKU population.
Scatter charts are the documented choice for surfacing correlation between two numerical measures. Scatter charts plot data points at the intersection of two numerical values, revealing correlations and clusters. That is the documented use case for comparing price and volume across many SKUs.
Why A is wrong: Stacked columns compare category totals, not the intersection of two numerical measures across many points.
Why B is correct: Correct. Scatter charts plot data points at the intersection of two numerical values, revealing correlations and clusters.
Why C is wrong: Donut charts show part-to-whole composition for a small number of categories, not correlation.
Why D is wrong: Card visuals display a single fact and cannot reveal pairwise correlation or clustering between two measures.
lock_openFree sampleVisualize and Analyze the Datamedium
A measure named StatusColor returns hex codes such as #5BA300 or #E91C1C from a SWITCH expression. You want the table column's background to use those colours directly without writing any rules. Which Format style do you choose?
- AChoose Gradient, then base the formatting on StatusColor with Sum summarization.
- BChoose Field value, then base the background on the StatusColor measure.check_circle Correct
- CChoose Rules and write one If value rule per hex code returned by the measure.
- DChoose Data bars, then map bar colour to the StatusColor measure output.
Field value formatting consumes colour outputs from a measure or column. Color by field value passes a measure's colour-name, hex, RGB, or HSL output straight through to background or font colour. Combining it with a SWITCH measure that emits hex codes is the documented pattern.
Why A is wrong: Gradient interpolates colours across a numeric range; it cannot consume hex codes returned by a measure.
Why B is correct: Correct. Color by field value passes a measure's colour-name, hex, RGB, or HSL output straight through to background or font colour.
Why C is wrong: Rules compare numeric ranges against thresholds; they cannot dispatch on the hex strings emitted by the measure.
Why D is wrong: Data bars draw bar length and a single fixed colour from the value, not hex strings from a separate measure.
lock_openFree sampleVisualize and Analyze the Dataeasy
A report author wants to apply the same Region slicer selection across three report pages. In Power BI Desktop, which ribbon path opens the pane used to configure this behaviour?
- AOpen the Format ribbon and select Edit interactions to display per-visual sync controls.
- BOpen the View ribbon and select Sync slicers to display the Sync slicers pane.check_circle Correct
- COpen the Modeling ribbon and select Manage relationships to link the slicer field across pages.
- DOpen the Home ribbon and select Transform data to enable cross-page slicer behaviour.
Sync slicers is enabled from the View ribbon in Power BI Desktop. In Power BI Desktop, on the View ribbon, you select Sync slicers, which displays the Sync slicers pane between the Filters and Visualizations panes. From there you control Sync and Visible columns per page.
Why A is wrong: Edit interactions controls which visuals on the same page a slicer filters; it does not configure sync across pages.
Why B is correct: Correct. In Power BI Desktop, on the View ribbon, you select Sync slicers, which displays the Sync slicers pane between the Filters and Visualizations panes.
Why C is wrong: Relationships govern the semantic model, not whether a slicer's selection follows the user across pages.
Why D is wrong: Transform data opens Power Query; it has nothing to do with slicer synchronisation.
lock_openFree sampleDeploy and Maintain Assetsmedium
An analyst with the Contributor role tries to publish a brand-new app from a workspace and finds the Publish app command is unavailable. Which role grants the least privilege required to publish a new app?
- AAssign the analyst the Contributor role on the workspace.
- BAssign the analyst the Member role on the workspace.check_circle Correct
- CAssign the analyst the Viewer role on the workspace.
- DAssign the analyst a tenant-level Power BI admin role to publish the app.
Member is the least-privileged workspace role that can publish or unpublish an app for the first time. The workspace role matrix shows that publishing, unpublishing, and changing permissions for an app is a Yes for Admin and Member only. Contributors can update an app when explicitly delegated, but cannot publish the first version.
Why A is wrong: Contributors can update an app only if a workspace Admin has delegated update rights; they cannot create or publish an app for the first time.
Why B is correct: Correct. The workspace role matrix shows that publishing, unpublishing, and changing permissions for an app is a Yes for Admin and Member only.
Why C is wrong: Viewers can only read and interact with items; they have no app-publishing capability whatsoever.
Why D is wrong: Tenant admin roles oversee policy; they are not the least privilege for publishing an app from a single workspace.
lock_openFree sampleDeploy and Maintain Assetsmedium
A workspace Member is asked to change another user's existing role from Contributor to Viewer. The Member can change an existing user's role directly using the Manage access pane. Is this statement correct?
- AYes
- BNocheck_circle Correct
Only workspace Admins can change an existing user's role; Members can add but not remove or modify role assignments. Members can add users with lower permissions but cannot remove users from any workspace role or change an existing user's role. The role-change workflow requires an Admin to remove and re-add the user with the new role.
Why A is wrong: The matrix shows Add or remove any user in a workspace role is a Yes only for Admin; Members are limited to additive grants of lower-permission roles.
Why B is correct: Correct. Members can add users with lower permissions but cannot remove users from any workspace role or change an existing user's role.
lock_openFree sampleDeploy and Maintain Assetshard
A workspace Admin removes a report from a published app and then republishes the app. What happens to consumer-side state for that app?
- ACustomizations are preserved automatically and re-applied when consumers next open the app.
- BApp users lose all their customizations such as bookmarks and comments, and the loss cannot be undone.check_circle Correct
- COnly bookmarks are removed while personal comments and pins remain available in the app.
- DCustomizations follow the consumer's identity and re-attach if the report is added back later.
Removing a report from an app and republishing drops all consumer customizations permanently. Removing a report and updating the app causes app users to lose all customizations including bookmarks and comments. The documentation explicitly states these customizations cannot be restored, even by re-adding the report later.
Why A is wrong: The documentation states customizations are lost when a report is removed and the app is updated; they are not preserved.
Why B is correct: Correct. Removing a report and updating the app causes app users to lose all customizations including bookmarks and comments.
Why C is wrong: All customizations are dropped together; there is no partial preservation policy for comments versus bookmarks.
Why D is wrong: Re-adding the report does not restore lost customizations; once dropped they cannot be restored.
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