Exam Bundle

All orders remain after expansion, with missing product attributes represented as null. That check would be meaningful for the separate case: A data team wants to preserve all left-side orders even when a product master lacks a matching SKU. It would not directly verify this scenario's selected implementation: Split the text column by an agreed delimiter or pattern and check irregular names.

The index is sequential in the chosen order and business-key uniqueness is unaffected. That check would be meaningful for the separate case: A table needs row numbers to help diagnose source ordering but the model's natural business keys must stay unchanged. It would not directly verify this scenario's selected implementation: Split the text column by an agreed delimiter or pattern and check irregular names.

Each sample invoice's derived year and month match its date. That check would be meaningful for the separate case: An analyst needs a normalized Year and Month field from an invoice date. It would not directly verify this scenario's selected implementation: Split the text column by an agreed delimiter or pattern and check irregular names.

'Split the delimited list into rows rather than into new numbered columns' is validated when 'The resulting record count reflects one row per incident-severity pair'. It does not demonstrate 'A value like 1 234,50 converts to the intended 1234.50 rather than an error', which requires 'Use Change Type with Locale and choose the source's culture for decimal parsing'.

A successful 'Use a left anti join between submitted vendors and the approved register' would show 'The result contains only vendor records not found in the register'. This case needs 'Use Change Type with Locale and choose the source's culture for decimal parsing' and evidence that 'A value like 1 234,50 converts to the intended 1234.50 rather than an error', not the other outcome.

The check for 'Split the composite address using a validated delimiter and retain unparseable exceptions' is 'The new columns can filter locations without silently dropping malformed addresses'. That does not establish 'A value like 1 234,50 converts to the intended 1234.50 rather than an error'; the stated requirement calls for 'Use Change Type with Locale and choose the source's culture for decimal parsing'.

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: Use Change Type with Locale and choose the source's culture for decimal parsing.

The result contains only vendor records not found in the register. That check would be meaningful for the separate case: A quality team wants records that have no match in a registered-vendor list. It would not directly verify this scenario's selected implementation: Use Change Type with Locale and choose the source's culture for decimal parsing.

The new columns can filter locations without silently dropping malformed addresses. That check would be meaningful for the separate case: A data source has 'City, State, PostalCode' in one text column and filtering needs separate geography fields. It would not directly verify this scenario's selected implementation: Use Change Type with Locale and choose the source's culture for decimal parsing.

'Define a controlled schema normalization step before combining files' addresses the mechanism measured by 'Both old and new extracts populate the target columns and schema mismatches are flagged'. The current outcome is 'Column names match the approved schema and title text is not loaded as sales data' after 'Remove nondata leading rows and promote the intended header row', which the distractor leaves unverified.

'The output has the expected respondent grain and handles duplicate answers explicitly' would demonstrate 'Pivot selected question codes into respondent-level columns with controlled aggregation', but not 'Column names match the approved schema and title text is not loaded as sales data'. The latter requires 'Remove nondata leading rows and promote the intended header row' in this scenario.

Using 'Use an inner merge on the agreed invoice key' supports the result 'The output includes only invoices with a matching key in both lists'. It does not implement 'Remove nondata leading rows and promote the intended header row' or prove 'Column names match the approved schema and title text is not loaded as sales data' for this question.

The output has the expected respondent grain and handles duplicate answers explicitly. That check would be meaningful for the separate case: A survey has several question rows per respondent and needs one row per respondent for profile attributes. It would not directly verify this scenario's selected implementation: Remove nondata leading rows and promote the intended header row.

The output includes only invoices with a matching key in both lists. That check would be meaningful for the separate case: An invoice process needs rows shared by two tax-review lists, not every row from either list. It would not directly verify this scenario's selected implementation: Remove nondata leading rows and promote the intended header row.

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: Remove nondata leading rows and promote the intended header row.

'Transpose the appropriate rectangular table in Power Query after clarifying headers' is validated when 'Previous row labels become columns while values retain the intended alignment'. It does not demonstrate 'Each sample invoice's derived year and month match its date', which requires 'Add typed date-derived columns with Date.Year and Date.Month where appropriate'.

A successful 'Expand the record column into selected, permitted contact properties' would show 'The transformed preview shows email and phone as separate fields for each customer'. This case needs 'Add typed date-derived columns with Date.Year and Date.Month where appropriate' and evidence that 'Each sample invoice's derived year and month match its date', not the other outcome.

The check for 'Unpivot language-specific name fields into Language and ProductName rows' is 'The result supports language filtering without proliferating name columns'. That does not establish 'Each sample invoice's derived year and month match its date'; the stated requirement calls for 'Add typed date-derived columns with Date.Year and Date.Month where appropriate'.

Previous row labels become columns while values retain the intended alignment. That check would be meaningful for the separate case: An analyst has a table showing daily sales but needs a transposed matrix for a one-off interface. It would not directly verify this scenario's selected implementation: Add typed date-derived columns with Date.Year and Date.Month where appropriate.

The transformed preview shows email and phone as separate fields for each customer. That check would be meaningful for the separate case: An app export contains nested records of customer contact details in one column. It would not directly verify this scenario's selected implementation: Add typed date-derived columns with Date.Year and Date.Month where appropriate.

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 typed date-derived columns with Date.Year and Date.Month where appropriate.

'Use a left outer merge from Orders to Products' addresses the mechanism measured by 'All orders remain after expansion, with missing product attributes represented as null'. The current outcome is 'The new columns can filter locations without silently dropping malformed addresses' after 'Split the composite address using a validated delimiter and retain unparseable exceptions', which the distractor leaves unverified.

'All regions populate the same normalized columns without duplicate case-variant fields' would demonstrate 'Normalize schema column names and types before an append operation', but not 'The new columns can filter locations without silently dropping malformed addresses'. The latter requires 'Split the composite address using a validated delimiter and retain unparseable exceptions' in this scenario.

Using 'Arrange base and referenced queries as an acyclic dependency chain' supports the result 'The query-dependency view contains no cycles and all enabled outputs refresh'. It does not implement 'Split the composite address using a validated delimiter and retain unparseable exceptions' or prove 'The new columns can filter locations without silently dropping malformed addresses' for this question.

All orders remain after expansion, with missing product attributes represented as null. That check would be meaningful for the separate case: A data team wants to preserve all left-side orders even when a product master lacks a matching SKU. It would not directly verify this scenario's selected implementation: Split the composite address using a validated delimiter and retain unparseable exceptions.

All regions populate the same normalized columns without duplicate case-variant fields. That check would be meaningful for the separate case: A seasonal store's regional CSV files use inconsistent case in their column headers. It would not directly verify this scenario's selected implementation: Split the composite address using a validated delimiter and retain unparseable exceptions.

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 composite address using a validated delimiter and retain unparseable exceptions.

'Use a left anti join between submitted vendors and the approved register' is validated when 'The result contains only vendor records not found in the register'. It does not demonstrate 'The output has the expected respondent grain and handles duplicate answers explicitly', which requires 'Pivot selected question codes into respondent-level columns with controlled aggregation'.

A successful 'Add an index column for tracing while preserving business identifiers' would show 'The index is sequential in the chosen order and business-key uniqueness is unaffected'. This case needs 'Pivot selected question codes into respondent-level columns with controlled aggregation' and evidence that 'The output has the expected respondent grain and handles duplicate answers explicitly', not the other outcome.

The check for 'Use Append after validating matching columns, not Merge on customer ID' is 'The resulting table contains both historic and new event rows rather than horizontally joined fields'. That does not establish 'The output has the expected respondent grain and handles duplicate answers explicitly'; the stated requirement calls for 'Pivot selected question codes into respondent-level columns with controlled aggregation'.

The result contains only vendor records not found in the register. That check would be meaningful for the separate case: A quality team wants records that have no match in a registered-vendor list. It would not directly verify this scenario's selected implementation: Pivot selected question codes into respondent-level columns with controlled aggregation.

The index is sequential in the chosen order and business-key uniqueness is unaffected. That check would be meaningful for the separate case: A table needs row numbers to help diagnose source ordering but the model's natural business keys must stay unchanged. It would not directly verify this scenario's selected implementation: Pivot selected question codes into respondent-level columns with controlled aggregation.

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: Pivot selected question codes into respondent-level columns with controlled aggregation.