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Product familiarity is not evidence of suitability. The actual defect in this proposal is: Overlaid charts create maintenance and accessibility complexity.

This addresses a different case: An analyst wants extra product attributes visible when hovering over a sales chart without cluttering bars. It is not the specific defect in 'Hardcode three overlaid charts and require manually hiding two'.

This is reasoning supporting the preferred solution 'Use a Field Parameter controlling the visual's chosen dimension', but it does not directly name the defect in the proposed shortcut.

This belongs to a different case: An analyst wants extra product attributes visible when hovering over a sales chart without cluttering bars. It does not validate the current change.

Approval of a proposed design is not evidence of working behavior. The required observation is: The parameter selection changes the visual axis correctly.

A future test is not yet observed operational evidence. The verification must establish: The parameter selection changes the visual axis correctly.

This is the already implemented original solution: Use a Field Parameter controlling the visual's chosen dimension. Field parameters let users switch columns or measures in supported visuals. It does not itself solve the new requirement.

This remains unsuitable for the original situation: Overlaid charts create maintenance and accessibility complexity. It also does not meet the changed need.

This observes the original approach but is not an additional action: The parameter selection changes the visual axis correctly.

This addresses a separate source situation: A report needs a running sum of sales displayed on a matrix using the current visual axis. It does not directly satisfy this case's priority. In the current question, this comparison is made against: A manufacturer wants to compare actual shipments as bars with a related target shown as a line. Which approach BEST meets the stated Microsoft Power BI requirement?

This might be appropriate after a different requirement arises: The target has a vastly different unit scale. It is not the best initial response to the stated case.

An arbitrary scatter cannot communicate the intended target-versus-actual comparison. The proposed shortcut 'Use an unrelated bubble chart without mapping target and actual fields' fails the documented need.

This is reasoning supporting the preferred solution 'Use a Line and Clustered Column combo chart where measures share a meaningful axis', 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: An arbitrary scatter cannot communicate the intended target-versus-actual comparison.

This addresses a different case: A report needs a running sum of sales displayed on a matrix using the current visual axis. It is not the specific defect in 'Use an unrelated bubble chart without mapping target and actual fields'.

Approval of a proposed design is not evidence of working behavior. The required observation is: The target line and actual bars align to the same reporting periods.

A future test is not yet observed operational evidence. The verification must establish: The target line and actual bars align to the same reporting periods.

This belongs to a different case: A report needs a running sum of sales displayed on a matrix using the current visual axis. It does not validate the current change. In the current question, this comparison is made against: A manufacturer wants to compare actual shipments as bars with a related target shown as a line. After implementing Use a Line and Clustered Column combo chart where measures share a meaningful axis, which observation BEST demonstrates that the intended result is actually working?

This observes the original approach but is not an additional action: The target line and actual bars align to the same reporting periods.

This remains unsuitable for the original situation: An arbitrary scatter cannot communicate the intended target-versus-actual comparison. It also does not meet the changed need.

This is the already implemented original solution: Use a Line and Clustered Column combo chart where measures share a meaningful axis. Combination charts compare two related measures with different mark types. It does not itself solve the new requirement.

This addresses a separate source situation: A manufacturer wants to compare actual shipments as bars with a related target shown as a line. It does not directly satisfy this case's priority. In the current question, this comparison is made against: A brand analyst must show how product rankings change year over year. Which approach BEST meets the stated Microsoft Power BI requirement?

A single aggregate cannot show multi-period category rank movement. The proposed shortcut 'Use a single total card for all brands to show rank changes' fails the documented need.

This might be appropriate after a different requirement arises: The analyst later needs exact numerical rank labels for a compliance slide. It is not the best initial response to the stated case.

This addresses a different case: A manufacturer wants to compare actual shipments as bars with a related target shown as a line. It is not the specific defect in 'Use a single total card for all brands to show rank changes'.

Product familiarity is not evidence of suitability. The actual defect in this proposal is: A single aggregate cannot show multi-period category rank movement.

This is reasoning supporting the preferred solution 'Use a Ribbon chart for rank transitions across periods', 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 ribbons visibly reorder as the ranked metric changes over years.

Approval of a proposed design is not evidence of working behavior. The required observation is: The ribbons visibly reorder as the ranked metric changes over years.

This belongs to a different case: A manufacturer wants to compare actual shipments as bars with a related target shown as a line. It does not validate the current change. In the current question, this comparison is made against: A brand analyst must show how product rankings change year over year. After implementing Use a Ribbon chart for rank transitions across periods, which observation BEST demonstrates that the intended result is actually working?

This observes the original approach but is not an additional action: The ribbons visibly reorder as the ranked metric changes over years.

This is the already implemented original solution: Use a Ribbon chart for rank transitions across periods. Ribbon charts reveal changing category positions over an ordered axis. It does not itself solve the new requirement.

This remains unsuitable for the original situation: A single aggregate cannot show multi-period category rank movement. It also does not meet the changed need.