Exam Bundle

Visuals display SKU values without automatic sum or average aggregation. That check would be meaningful for the separate case: A dimension has a column of SKU labels for grouping but report authors should not aggregate it numerically. It would not directly verify this scenario's selected implementation: Create a one-to-many relationship from unique Product to repeating Sales keys.

Sales transactions across different times on the same day appear under the correct calendar date. That check would be meaningful for the separate case: A date dimension is accidentally related to Sales[TimeStamp] containing nonzero times. It would not directly verify this scenario's selected implementation: Create a one-to-many relationship from unique Product to repeating Sales keys.

InvoiceDate and ShipDate slicers independently narrow the intended date roles in one visual. That check would be meaningful for the separate case: Sales has InvoiceDate and ShipDate but the business requires separate simultaneous slicers for both roles. It would not directly verify this scenario's selected implementation: Create a one-to-many relationship from unique Product to repeating Sales keys.

The check for 'Keep the inactive ShipDate relationship and activate it within the relevant DAX measure using USERELATIONSHIP' is 'The shipping measure follows ShipDate while ordinary sales measures continue following OrderDate' for An analyst only needs an occasional shipping-date measure while an active relationship already uses OrderDate. That does not establish 'The relationship's one-side keys are unique and refresh does not fail on duplicates'; the stated requirement calls for 'Resolve duplicate RegionCode values or introduce a truly unique dimension key'.

'Use ProductKey as the relationship key and keep descriptions for grouping/display' addresses the mechanism measured by 'A product rename does not break the sales relationship or create duplicate product entities' for A product dimension includes changing descriptions but only ProductKey uniquely identifies rows. The current outcome is 'The relationship's one-side keys are unique and refresh does not fail on duplicates' after 'Resolve duplicate RegionCode values or introduce a truly unique dimension key', which the distractor leaves unverified.

'Report creators see friendly descriptors and relationships still propagate filters' would demonstrate 'Hide relationship-key fields while keeping them available to the model relationships' for A model exposes technical surrogate keys in the report field list, confusing self-service analysts, but not 'The relationship's one-side keys are unique and refresh does not fail on duplicates'. The latter requires 'Resolve duplicate RegionCode values or introduce a truly unique dimension key' in this scenario.

Report creators see friendly descriptors and relationships still propagate filters. That check would be meaningful for the separate case: A model exposes technical surrogate keys in the report field list, confusing self-service analysts. It would not directly verify this scenario's selected implementation: Resolve duplicate RegionCode values or introduce a truly unique dimension key.

The shipping measure follows ShipDate while ordinary sales measures continue following OrderDate. That check would be meaningful for the separate case: An analyst only needs an occasional shipping-date measure while an active relationship already uses OrderDate. It would not directly verify this scenario's selected implementation: Resolve duplicate RegionCode values or introduce a truly unique dimension key.

A product rename does not break the sales relationship or create duplicate product entities. That check would be meaningful for the separate case: A product dimension includes changing descriptions but only ProductKey uniquely identifies rows. It would not directly verify this scenario's selected implementation: Resolve duplicate RegionCode values or introduce a truly unique dimension key.

Using 'Create a continuous common Date table covering the full required period' supports the result 'A year-month view includes dates with no fact rows and time calculations use a continuous calendar' for A sales report shows totals by day but has no complete calendar table and dates disappear on days with no sales. It does not implement 'Introduce a customer dimension with unique keys and connect each fact using controlled one-to-many relationships' or prove 'Customer selections filter both facts through the conformed dimension without unintended fanout' for this question.

'Use a DAX measure evaluated in filter context rather than a static calculated column' is validated when 'Changing slicers recalculates the reported rate without a data refresh' for A report calculates a tax rate that depends on the current slicer selections rather than on each stored source row. It does not demonstrate 'Customer selections filter both facts through the conformed dimension without unintended fanout', which requires 'Introduce a customer dimension with unique keys and connect each fact using controlled one-to-many relationships'.

A successful 'Model the snapshot grain explicitly and calculate an appropriate period-end measure' would show 'Year-level headcount reflects the selected endpoint rather than the sum of twelve monthly snapshots'. This case needs 'Introduce a customer dimension with unique keys and connect each fact using controlled one-to-many relationships' and evidence that 'Customer selections filter both facts through the conformed dimension without unintended fanout', not the other outcome.

A year-month view includes dates with no fact rows and time calculations use a continuous calendar. That check would be meaningful for the separate case: A sales report shows totals by day but has no complete calendar table and dates disappear on days with no sales. It would not directly verify this scenario's selected implementation: Introduce a customer dimension with unique keys and connect each fact using controlled one-to-many relationships.

Year-level headcount reflects the selected endpoint rather than the sum of twelve monthly snapshots. That check would be meaningful for the separate case: A quarterly headcount metric is stored as one row per department and month, and monthly figures should not be summed across time. It would not directly verify this scenario's selected implementation: Introduce a customer dimension with unique keys and connect each fact using controlled one-to-many relationships.

Changing slicers recalculates the reported rate without a data refresh. That check would be meaningful for the separate case: A report calculates a tax rate that depends on the current slicer selections rather than on each stored source row. It would not directly verify this scenario's selected implementation: Introduce a customer dimension with unique keys and connect each fact using controlled one-to-many relationships.

The check for 'Create an appropriate calculated column or perform the categorization in Power Query' is 'The category field can serve as a grouping or slicer column after model processing' for A category is fixed for every product row at model refresh time and must be usable for grouping. That does not establish 'InvoiceDate and ShipDate slicers independently narrow the intended date roles in one visual'; the stated requirement calls for 'Create role-playing date dimensions for Invoice and Ship analysis'.

'Keep each fact at its native documented grain rather than treating target rows as daily transactions' addresses the mechanism measured by 'Monthly target comparisons aggregate correctly without multiplying one target by the number of transaction days' for A finance model imports monthly targets at Store-Month grain and daily transactions at Store-Day grain. The current outcome is 'InvoiceDate and ShipDate slicers independently narrow the intended date roles in one visual' after 'Create role-playing date dimensions for Invoice and Ship analysis', which the distractor leaves unverified.

'Customer and date slicers filter deterministically without ambiguous propagation' would demonstrate 'Use single-direction dimension-to-fact relationships unless a supported special case requires otherwise', but not 'InvoiceDate and ShipDate slicers independently narrow the intended date roles in one visual'. The latter requires 'Create role-playing date dimensions for Invoice and Ship analysis' in this scenario.

Monthly target comparisons aggregate correctly without multiplying one target by the number of transaction days. That check would be meaningful for the separate case: A finance model imports monthly targets at Store-Month grain and daily transactions at Store-Day grain. It would not directly verify this scenario's selected implementation: Create role-playing date dimensions for Invoice and Ship analysis.

The category field can serve as a grouping or slicer column after model processing. That check would be meaningful for the separate case: A category is fixed for every product row at model refresh time and must be usable for grouping. It would not directly verify this scenario's selected implementation: Create role-playing date dimensions for Invoice and Ship analysis.

Customer and date slicers filter deterministically without ambiguous propagation. That check would be meaningful for the separate case: A table joins customer orders to two date dimensions and all relationships are configured Both, creating ambiguous filtering. It would not directly verify this scenario's selected implementation: Create role-playing date dimensions for Invoice and Ship analysis.

Using 'Set the column's data type and default summarization to suit an identifier' supports the result 'Visuals display SKU values without automatic sum or average aggregation' for A dimension has a column of SKU labels for grouping but report authors should not aggregate it numerically. It does not implement 'Keep the inactive ShipDate relationship and activate it within the relevant DAX measure using USERELATIONSHIP' or prove 'The shipping measure follows ShipDate while ordinary sales measures continue following OrderDate' for this question.

'Consider denormalizing supported lookup attributes into a single geography dimension' is validated when 'Country and region filters reach the fact through a clear, efficient dimension relationship'. It does not demonstrate 'The shipping measure follows ShipDate while ordinary sales measures continue following OrderDate', which requires 'Keep the inactive ShipDate relationship and activate it within the relevant DAX measure using USERELATIONSHIP'.

A successful 'Keep the parameter table disconnected and have the selected value read by a measure' would show 'Changing the discount slicer adjusts the calculation without filtering transaction rows directly' for A disconnected What-if parameter contains user-selectable discount percentages. This case needs 'Keep the inactive ShipDate relationship and activate it within the relevant DAX measure using USERELATIONSHIP' and evidence that 'The shipping measure follows ShipDate while ordinary sales measures continue following OrderDate', not the other outcome.

Changing the discount slicer adjusts the calculation without filtering transaction rows directly. That check would be meaningful for the separate case: A disconnected What-if parameter contains user-selectable discount percentages. It would not directly verify this scenario's selected implementation: Keep the inactive ShipDate relationship and activate it within the relevant DAX measure using USERELATIONSHIP.

Country and region filters reach the fact through a clear, efficient dimension relationship. That check would be meaningful for the separate case: Two lookup tables map country and region data with a snowflake chain, but consumers need simple geographical slicing. It would not directly verify this scenario's selected implementation: Keep the inactive ShipDate relationship and activate it within the relevant DAX measure using USERELATIONSHIP.

Visuals display SKU values without automatic sum or average aggregation. That check would be meaningful for the separate case: A dimension has a column of SKU labels for grouping but report authors should not aggregate it numerically. It would not directly verify this scenario's selected implementation: Keep the inactive ShipDate relationship and activate it within the relevant DAX measure using USERELATIONSHIP.

The check for 'Treat Sales as fact and Product and Date as dimensions for filter and aggregation behavior' is 'Product and Date slicers filter SaleAmount summaries without needing fact-to-fact joins' for A star-schema model contains a Sales table with Quantity and Amount and separate Product and Date lookups. That does not establish 'A year-month view includes dates with no fact rows and time calculations use a continuous calendar'; the stated requirement calls for 'Create a continuous common Date table covering the full required period'.

'Create a date-grain matching key and relate it to the Date table's unique date column' addresses the mechanism measured by 'Sales transactions across different times on the same day appear under the correct calendar date' for A date dimension is accidentally related to Sales[TimeStamp] containing nonzero times. The current outcome is 'A year-month view includes dates with no fact rows and time calculations use a continuous calendar' after 'Create a continuous common Date table covering the full required period', which the distractor leaves unverified.

'Report creators see friendly descriptors and relationships still propagate filters' would demonstrate 'Hide relationship-key fields while keeping them available to the model relationships' for A model exposes technical surrogate keys in the report field list, confusing self-service analysts, but not 'A year-month view includes dates with no fact rows and time calculations use a continuous calendar'. The latter requires 'Create a continuous common Date table covering the full required period' in this scenario.

Report creators see friendly descriptors and relationships still propagate filters. That check would be meaningful for the separate case: A model exposes technical surrogate keys in the report field list, confusing self-service analysts. It would not directly verify this scenario's selected implementation: Create a continuous common Date table covering the full required period.

Product and Date slicers filter SaleAmount summaries without needing fact-to-fact joins. That check would be meaningful for the separate case: A star-schema model contains a Sales table with Quantity and Amount and separate Product and Date lookups. It would not directly verify this scenario's selected implementation: Create a continuous common Date table covering the full required period.

Sales transactions across different times on the same day appear under the correct calendar date. That check would be meaningful for the separate case: A date dimension is accidentally related to Sales[TimeStamp] containing nonzero times. It would not directly verify this scenario's selected implementation: Create a continuous common Date table covering the full required period.