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This observes the original approach but is not an additional action: Conversion failures are isolated and refreshed valid rows remain traceable.

This is the already implemented original solution: Use error detection and schema-aware conversion for the affected identifier. Strict type conversion can create cell errors when source values drift. It does not itself solve the new requirement.

This remains unsuitable for the original situation: Suppressing refresh does not explain or correct the schema exception. It also does not meet the changed need.

This might be appropriate after a different requirement arises: Management adopts a documented threshold for impossible durations. It is not the best initial response to the stated case.

One arbitrary example is not a valid outlier threshold. The proposed shortcut 'Use the first value as the maximum allowed duration for every record' fails the documented need.

This addresses a separate source situation: A European file encodes 31/12/2025 but the model interprets it as a US month/day date. It does not directly satisfy this case's priority. In the current question, this comparison is made against: A delivery feed has unusual extreme travel durations, and managers want to know if those are anomalies. Which approach BEST meets the stated Microsoft Power BI requirement?

This addresses a different case: A European file encodes 31/12/2025 but the model interprets it as a US month/day date. It is not the specific defect in 'Use the first value as the maximum allowed duration for every record'.

Product familiarity is not evidence of suitability. The actual defect in this proposal is: One arbitrary example is not a valid outlier threshold.

This is reasoning supporting the preferred solution 'Inspect column statistics, distributions, and source-level records before filtering', 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: Flagged extreme cases are reviewed and true operational exceptions retained.

This belongs to a different case: A European file encodes 31/12/2025 but the model interprets it as a US month/day date. It does not validate the current change. In the current question, this comparison is made against: A delivery feed has unusual extreme travel durations, and managers want to know if those are anomalies. After implementing Inspect column statistics, distributions, and source-level records before filtering, which observation BEST demonstrates that the intended result is actually working?

A future test is not yet observed operational evidence. The verification must establish: Flagged extreme cases are reviewed and true operational exceptions retained.

This observes the original approach but is not an additional action: Flagged extreme cases are reviewed and true operational exceptions retained.

This remains unsuitable for the original situation: One arbitrary example is not a valid outlier threshold. It also does not meet the changed need.

This is the already implemented original solution: Inspect column statistics, distributions, and source-level records before filtering. Profiling helps distinguish outliers from systematic data-entry problems. It does not itself solve the new requirement.

Hiding a model field does not repair broken M references. The proposed shortcut 'Hide the failed column in report view and assume the upstream query will succeed' fails the documented need.

This might be appropriate after a different requirement arises: Source owners publish a formal versioned schema contract. It is not the best initial response to the stated case.

This addresses a separate source situation: A source includes 150000 transactions but Power Query's profile uses only the top 1000 rows. It does not directly satisfy this case's priority.

This is reasoning supporting the preferred solution 'Review applied steps for references to missing columns and make handling resilient', but it does not directly name the defect in the proposed shortcut.

This addresses a different case: A source includes 150000 transactions but Power Query's profile uses only the top 1000 rows. It is not the specific defect in 'Hide the failed column in report view and assume the upstream query will succeed'.

Product familiarity is not evidence of suitability. The actual defect in this proposal is: Hiding a model field does not repair broken M references.

Approval of a proposed design is not evidence of working behavior. The required observation is: Service refresh succeeds with the revised step and any missing fields handled explicitly.

A future test is not yet observed operational evidence. The verification must establish: Service refresh succeeds with the revised step and any missing fields handled explicitly.

This belongs to a different case: A source includes 150000 transactions but Power Query's profile uses only the top 1000 rows. It does not validate the current change.

This is the already implemented original solution: Review applied steps for references to missing columns and make handling resilient. A renamed or missing column can break later Power Query transformation steps. It does not itself solve the new requirement.

This observes the original approach but is not an additional action: Service refresh succeeds with the revised step and any missing fields handled explicitly.

This remains unsuitable for the original situation: Hiding a model field does not repair broken M references. It also does not meet the changed need.

This addresses a separate source situation: A Power Query dataset needs Column A as headers and has one junk metadata row at the top. It does not directly satisfy this case's priority.

This might be appropriate after a different requirement arises: The finance team later needs a wide cross-tab for export. It is not the best initial response to the stated case.

Pivot would create the opposite shape from the requested tidy fact table. The proposed shortcut 'Use Pivot to spread monthly rows into even more columns' fails the documented need.

Product familiarity is not evidence of suitability. The actual defect in this proposal is: Pivot would create the opposite shape from the requested tidy fact table.

This addresses a different case: A Power Query dataset needs Column A as headers and has one junk metadata row at the top. It is not the specific defect in 'Use Pivot to spread monthly rows into even more columns'.

This is reasoning supporting the preferred solution 'Unpivot the monthly columns to Month and Value rows', but it does not directly name the defect in the proposed shortcut.