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This addresses a different case: A measure in Sales needs each row's related Product StandardCost for a row-level extended-cost calculation. It is not the specific defect in 'Copy the same filter calculation into every nested branch without explanation'.

Product familiarity is not evidence of suitability. The actual defect in this proposal is: Repeated subexpressions make troubleshooting and maintenance harder.

This is reasoning supporting the preferred solution 'Use VAR definitions and RETURN to reuse intermediate scalar or table expressions', but it does not directly name the defect in the proposed shortcut.

A future test is not yet observed operational evidence. The verification must establish: The refactored measure returns the same audited figures as its original.

This belongs to a different case: A measure in Sales needs each row's related Product StandardCost for a row-level extended-cost calculation. It does not validate the current change. In the current question, this comparison is made against: A long complex DAX measure repeats the same filtered sales subexpression three times. After implementing Use VAR definitions and RETURN to reuse intermediate scalar or table expressions, which observation BEST demonstrates that the intended result is actually working?

Approval of a proposed design is not evidence of working behavior. The required observation is: The refactored measure returns the same audited figures as its original.

This is the already implemented original solution: Use VAR definitions and RETURN to reuse intermediate scalar or table expressions. DAX variables improve readability and can avoid repeated expression evaluation. It does not itself solve the new requirement.

This remains unsuitable for the original situation: Repeated subexpressions make troubleshooting and maintenance harder. It also does not meet the changed need.

This observes the original approach but is not an additional action: The refactored measure returns the same audited figures as its original.

This might be appropriate after a different requirement arises: The matrix gains an intermediate Category level. It is not the best initial response to the stated case.

A single selected value may not mean the current matrix row is at the leaf level. The proposed shortcut 'Use HASONEVALUE for every total-level distinction without checking context semantics' fails the documented need.

This addresses a separate source situation: An invoice table stores varying line prices and management asks for average price weighted by quantity. It does not directly satisfy this case's priority. In the current question, this comparison is made against: A subtotal row must display a different calculation than leaf Product rows in a matrix. Which approach BEST meets the stated Microsoft Power BI requirement?

Product familiarity is not evidence of suitability. The actual defect in this proposal is: A single selected value may not mean the current matrix row is at the leaf level.

This addresses a different case: An invoice table stores varying line prices and management asks for average price weighted by quantity. It is not the specific defect in 'Use HASONEVALUE for every total-level distinction without checking context semantics'.

This is reasoning supporting the preferred solution 'Use ISINSCOPE to detect the current hierarchy level', but it does not directly name the defect in the proposed shortcut.

Approval of a proposed design is not evidence of working behavior. The required observation is: Leaf rows and grand total each show their documented intended result.

A future test is not yet observed operational evidence. The verification must establish: Leaf rows and grand total each show their documented intended result.

This belongs to a different case: An invoice table stores varying line prices and management asks for average price weighted by quantity. It does not validate the current change. In the current question, this comparison is made against: A subtotal row must display a different calculation than leaf Product rows in a matrix. After implementing Use ISINSCOPE to detect the current hierarchy level, which observation BEST demonstrates that the intended result is actually working?

This observes the original approach but is not an additional action: Leaf rows and grand total each show their documented intended result.

This remains unsuitable for the original situation: A single selected value may not mean the current matrix row is at the leaf level. It also does not meet the changed need.

This is the already implemented original solution: Use ISINSCOPE to detect the current hierarchy level. ISINSCOPE can distinguish a column's role in the visual grouping hierarchy. It does not itself solve the new requirement.

This is an unsuitable alternative for a different situation: A long complex DAX measure repeats the same filtered sales subexpression three times. It does not establish the requested action, evidence, or follow-up.

Bidirectionality does not itself choose which inactive date role to activate. The proposed shortcut 'Make every date relationship bidirectional and hope PromotionDate becomes active' fails the documented need.

This addresses a separate source situation: A long complex DAX measure repeats the same filtered sales subexpression three times. It does not directly satisfy this case's priority. In the current question, this comparison is made against: A revenue KPI should use an inactive PromotionDate relationship only for promoted-sales analysis. The original need and a later change are both under review: Report users want independent promotion and order date slicers. Which THREE entries together specify the initial action, direct evidence it works, and an additional action for the changed need? (Select THREE.)

This addresses a different case: A long complex DAX measure repeats the same filtered sales subexpression three times. It is not the specific defect in 'Make every date relationship bidirectional and hope PromotionDate becomes active'.

Product familiarity is not evidence of suitability. The actual defect in this proposal is: Bidirectionality does not itself choose which inactive date role to activate.

This is reasoning supporting the preferred solution 'Use CALCULATE with USERELATIONSHIP between Date and PromotionDate', but it does not directly name the defect in the proposed shortcut.

This belongs to a different case: A long complex DAX measure repeats the same filtered sales subexpression three times. It does not validate the current change. In the current question, this comparison is made against: A revenue KPI should use an inactive PromotionDate relationship only for promoted-sales analysis. After implementing Use CALCULATE with USERELATIONSHIP between Date and PromotionDate, which observation BEST demonstrates that the intended result is actually working?

Approval of a proposed design is not evidence of working behavior. The required observation is: The promoted-sales measure tracks PromotionDate while ordinary sales tracks OrderDate.

A future test is not yet observed operational evidence. The verification must establish: The promoted-sales measure tracks PromotionDate while ordinary sales tracks OrderDate.

This observes the original approach but is not an additional action: The promoted-sales measure tracks PromotionDate while ordinary sales tracks OrderDate.

This remains unsuitable for the original situation: Bidirectionality does not itself choose which inactive date role to activate. It also does not meet the changed need.

This is the already implemented original solution: Use CALCULATE with USERELATIONSHIP between Date and PromotionDate. USERELATIONSHIP activates a specific inactive model relationship for the measure. It does not itself solve the new requirement.