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The resulting table contains both historic and new event rows rather than horizontally joined fields. That check would be meaningful for the separate case: A marketing analyst wants to keep all historic records while appending new daily records from a matching query. It would not directly verify this scenario's selected implementation: Transpose the appropriate rectangular table in Power Query after clarifying headers.

The resulting record count reflects one row per incident-severity pair. That check would be meaningful for the separate case: An incident export contains 'critical, high, medium' in one cell and the analyst needs one incident-severity row per label. It would not directly verify this scenario's selected implementation: Transpose the appropriate rectangular table in Power Query after clarifying headers.

Every intended transaction matches the corresponding calendar date without time mismatches. That check would be meaningful for the separate case: A semantic model's fact table uses date-time stamps but needs to relate to a day-grain Date dimension. It would not directly verify this scenario's selected implementation: Transpose the appropriate rectangular table in Power Query after clarifying headers.

'Remove irrelevant columns before loading to reduce model size' is validated when 'Loaded tables omit unnecessary text while all required analytical fields remain'. It does not demonstrate 'All regions populate the same normalized columns without duplicate case-variant fields', which requires 'Normalize schema column names and types before an append operation'.

A successful 'Define a controlled schema normalization step before combining files' would show 'Both old and new extracts populate the target columns and schema mismatches are flagged'. This case needs 'Normalize schema column names and types before an append operation' and evidence that 'All regions populate the same normalized columns without duplicate case-variant fields', not the other outcome.

The check for 'Unpivot the priority count columns to Priority and Count rows' is 'A slicer can select one priority and filter its numeric count consistently'. That does not establish 'All regions populate the same normalized columns without duplicate case-variant fields'; the stated requirement calls for 'Normalize schema column names and types before an append operation'.

Loaded tables omit unnecessary text while all required analytical fields remain. That check would be meaningful for the separate case: A fact query includes columns for note text and debug payloads that reports never use. It would not directly verify this scenario's selected implementation: Normalize schema column names and types before an append operation.

A slicer can select one priority and filter its numeric count consistently. That check would be meaningful for the separate case: A help desk export has ticket priorities distributed as columns rather than values, preventing a single priority slicer. It would not directly verify this scenario's selected implementation: Normalize schema column names and types before an append operation.

Both old and new extracts populate the target columns and schema mismatches are flagged. That check would be meaningful for the separate case: A group of monthly extracts changes its file schema midway through the year. It would not directly verify this scenario's selected implementation: Normalize schema column names and types before an append operation.

'Set a fixed decimal or decimal number type appropriate to monetary calculations' addresses the mechanism measured by 'Sample values retain their required fractional precision after load'. The current outcome is 'The index is sequential in the chosen order and business-key uniqueness is unaffected' after 'Add an index column for tracing while preserving business identifiers', which the distractor leaves unverified.

'The fact-to-region relationship uses one key and hierarchy labels do not cause key collisions' would demonstrate 'Build a dimension with unique region keys and keep descriptive hierarchy attributes', but not 'The index is sequential in the chosen order and business-key uniqueness is unaffected'. The latter requires 'Add an index column for tracing while preserving business identifiers' in this scenario.

Using 'Unpivot language-specific name fields into Language and ProductName rows' supports the result 'The result supports language filtering without proliferating name columns'. It does not implement 'Add an index column for tracing while preserving business identifiers' or prove 'The index is sequential in the chosen order and business-key uniqueness is unaffected' for this question.

The fact-to-region relationship uses one key and hierarchy labels do not cause key collisions. That check would be meaningful for the separate case: A product export includes region names with hierarchy levels in separate columns; the analyst needs a stable region key for joins. It would not directly verify this scenario's selected implementation: Add an index column for tracing while preserving business identifiers.

Sample values retain their required fractional precision after load. That check would be meaningful for the separate case: A source's decimal sales amount is accidentally cast to Whole Number during import. It would not directly verify this scenario's selected implementation: Add an index column for tracing while preserving business identifiers.

The result supports language filtering without proliferating name columns. That check would be meaningful for the separate case: A sales dimension table has two separate columns for English and German product names but a locale dimension is preferred. It would not directly verify this scenario's selected implementation: Add an index column for tracing while preserving business identifiers.

'Convert the OrderDate column to the Date type in Power Query before load' is validated when 'The date field behaves as a date and chronological filters use the intended days'. It does not demonstrate 'The resulting record count reflects one row per incident-severity pair', which requires 'Split the delimited list into rows rather than into new numbered columns'.

A successful 'Retain currency context and convert through a defined exchange-rate methodology before combined totals' would show 'Reconciled totals use a common reporting currency and state the rate date'. This case needs 'Split the delimited list into rows rather than into new numbered columns' and evidence that 'The resulting record count reflects one row per incident-severity pair', not the other outcome.

The check for 'Arrange base and referenced queries as an acyclic dependency chain' is 'The query-dependency view contains no cycles and all enabled outputs refresh'. That does not establish 'The resulting record count reflects one row per incident-severity pair'; the stated requirement calls for 'Split the delimited list into rows rather than into new numbered columns'.

The query-dependency view contains no cycles and all enabled outputs refresh. That check would be meaningful for the separate case: A Power Query dataflow output is reused by many downstream queries but transformations must not create a circular reference. It would not directly verify this scenario's selected implementation: Split the delimited list into rows rather than into new numbered columns.

The date field behaves as a date and chronological filters use the intended days. That check would be meaningful for the separate case: A source exports order dates as text but the model must support a date hierarchy and time intelligence. It would not directly verify this scenario's selected implementation: Split the delimited list into rows rather than into new numbered columns.

Reconciled totals use a common reporting currency and state the rate date. That check would be meaningful for the separate case: An order table reports sales in different currencies and an analyst plans to sum raw amounts across currencies. It would not directly verify this scenario's selected implementation: Split the delimited list into rows rather than into new numbered columns.

'Use Append after validating matching columns, not Merge on customer ID' addresses the mechanism measured by 'The resulting table contains both historic and new event rows rather than horizontally joined fields'. The current outcome is 'Both old and new extracts populate the target columns and schema mismatches are flagged' after 'Define a controlled schema normalization step before combining files', which the distractor leaves unverified.

'The result has month-value rows rather than twelve separate month measures' would demonstrate 'Unpivot the month columns into Month and Value fields', but not 'Both old and new extracts populate the target columns and schema mismatches are flagged'. The latter requires 'Define a controlled schema normalization step before combining files' in this scenario.

Using 'Disable load for the unnecessary duplicate or remove it after dependency review' supports the result 'Only the intended fact table loads and dependent queries still work'. It does not implement 'Define a controlled schema normalization step before combining files' or prove 'Both old and new extracts populate the target columns and schema mismatches are flagged' for this question.

The resulting table contains both historic and new event rows rather than horizontally joined fields. That check would be meaningful for the separate case: A marketing analyst wants to keep all historic records while appending new daily records from a matching query. It would not directly verify this scenario's selected implementation: Define a controlled schema normalization step before combining files.

The result has month-value rows rather than twelve separate month measures. That check would be meaningful for the separate case: A wide quarterly table has one column per month but report designers need month as a slicer category. It would not directly verify this scenario's selected implementation: Define a controlled schema normalization step before combining files.

Only the intended fact table loads and dependent queries still work. That check would be meaningful for the separate case: A query's duplicate stage adds no new rows but creates two loaded copies of a large fact table. It would not directly verify this scenario's selected implementation: Define a controlled schema normalization step before combining files.

'Derive a normalized Date-only key from the timestamps for the relationship' is validated when 'Every intended transaction matches the corresponding calendar date without time mismatches'. It does not demonstrate 'The result supports language filtering without proliferating name columns', which requires 'Unpivot language-specific name fields into Language and ProductName rows'.

A successful 'Pivot the attribute-name column using an appropriate value aggregation' would show 'The output contains the expected named attribute columns for each entity'. This case needs 'Unpivot language-specific name fields into Language and ProductName rows' and evidence that 'The result supports language filtering without proliferating name columns', not the other outcome.

The check for 'Remove irrelevant columns before loading to reduce model size' is 'Loaded tables omit unnecessary text while all required analytical fields remain'. That does not establish 'The result supports language filtering without proliferating name columns'; the stated requirement calls for 'Unpivot language-specific name fields into Language and ProductName rows'.

Loaded tables omit unnecessary text while all required analytical fields remain. That check would be meaningful for the separate case: A fact query includes columns for note text and debug payloads that reports never use. It would not directly verify this scenario's selected implementation: Unpivot language-specific name fields into Language and ProductName rows.

The output contains the expected named attribute columns for each entity. That check would be meaningful for the separate case: A key-value table lists attributes in rows but the model needs each attribute as a field. It would not directly verify this scenario's selected implementation: Unpivot language-specific name fields into Language and ProductName rows.

Every intended transaction matches the corresponding calendar date without time mismatches. That check would be meaningful for the separate case: A semantic model's fact table uses date-time stamps but needs to relate to a day-grain Date dimension. It would not directly verify this scenario's selected implementation: Unpivot language-specific name fields into Language and ProductName rows.