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This remains unsuitable for the original situation: Complex snowflake paths can make filter propagation harder to reason about. It also does not meet the changed need.

This observes the original approach but is not an additional action: Product filters reach the sales fact through one clear dimension path.

This is the already implemented original solution: Consider a flattened Product dimension for a star-schema model. Denormalizing suitable descriptive attributes can simplify model relationships and queries. It does not itself solve the new requirement.

OrderID legitimately repeats across line items, so it is not unique at that grain. The proposed shortcut 'Assume OrderID is unique in the line-level fact and create a One-side relationship' fails the documented need.

This might be appropriate after a different requirement arises: Management needs a second table with one row per order. It is not the best initial response to the stated case.

This addresses a separate source situation: A retailer records both OrderDate and ShipDate in Sales but has one Calendar table. It does not directly satisfy this case's priority.

This addresses a different case: A retailer records both OrderDate and ShipDate in Sales but has one Calendar table. It is not the specific defect in 'Assume OrderID is unique in the line-level fact and create a One-side relationship'.

This is reasoning supporting the preferred solution 'Document the existing order-line grain before creating measures and relationships', but it does not directly name the defect in the proposed shortcut.

Product familiarity is not evidence of suitability. The actual defect in this proposal is: OrderID legitimately repeats across line items, so it is not unique at that grain.

Approval of a proposed design is not evidence of working behavior. The required observation is: Line totals reconcile to order totals using explicit aggregation.

This belongs to a different case: A retailer records both OrderDate and ShipDate in Sales but has one Calendar table. It does not validate the current change.

A future test is not yet observed operational evidence. The verification must establish: Line totals reconcile to order totals using explicit aggregation.

This remains unsuitable for the original situation: OrderID legitimately repeats across line items, so it is not unique at that grain. It also does not meet the changed need.

This observes the original approach but is not an additional action: Line totals reconcile to order totals using explicit aggregation.

This is the already implemented original solution: Document the existing order-line grain before creating measures and relationships. A fact's grain determines which aggregation questions it can answer. It does not itself solve the new requirement.

This addresses a separate source situation: An Orders fact has one row per order line, but an analyst wants the model's grain to be one row per order. It does not directly satisfy this case's priority.

This might be appropriate after a different requirement arises: A page needs a single date selector for whichever metric is selected. It is not the best initial response to the stated case.

Uncontrolled role relationships can change which event date a measure uses. The proposed shortcut 'Relate dates to event columns with arbitrary active paths and no model design' fails the documented need.

This is reasoning supporting the preferred solution 'Introduce role-playing date tables or explicitly controlled relationships', but it does not directly name the defect in the proposed shortcut.

This addresses a different case: An Orders fact has one row per order line, but an analyst wants the model's grain to be one row per order. It is not the specific defect in 'Relate dates to event columns with arbitrary active paths and no model design'.

Product familiarity is not evidence of suitability. The actual defect in this proposal is: Uncontrolled role relationships can change which event date a measure uses.

A future test is not yet observed operational evidence. The verification must establish: The Order and Invoice pages filter by their intended event dates.

Approval of a proposed design is not evidence of working behavior. The required observation is: The Order and Invoice pages filter by their intended event dates.

This belongs to a different case: An Orders fact has one row per order line, but an analyst wants the model's grain to be one row per order. It does not validate the current change.

This observes the original approach but is not an additional action: The Order and Invoice pages filter by their intended event dates.

This remains unsuitable for the original situation: Uncontrolled role relationships can change which event date a measure uses. It also does not meet the changed need.

This is the already implemented original solution: Introduce role-playing date tables or explicitly controlled relationships. A single date concept can serve multiple event roles without conflating contexts. It does not itself solve the new requirement.

This addresses a separate source situation: A model needs a calendar that spans minimum and maximum transaction dates. It does not directly satisfy this case's priority.

Direct fact-to-fact ambiguity can double-count or distort totals. The proposed shortcut 'Create a many-to-many relationship directly between unrelated fact tables by name' fails the documented need.

This might be appropriate after a different requirement arises: Region assignments become time-dependent. It is not the best initial response to the stated case.

This addresses a different case: A model needs a calendar that spans minimum and maximum transaction dates. It is not the specific defect in 'Create a many-to-many relationship directly between unrelated fact tables by name'.

This is reasoning supporting the preferred solution 'Model the many-to-many mapping with an explicit bridge and test filter behavior', but it does not directly name the defect in the proposed shortcut.

Product familiarity is not evidence of suitability. The actual defect in this proposal is: Direct fact-to-fact ambiguity can double-count or distort totals.