Exam Bundle

Query diagnostics show less data fetched and the source performs supported filtering. That check would be meaningful for the separate case: An analyst filters data only after several expensive text operations and SQL query folding is possible. It would not directly verify this scenario's selected implementation: Create a referenced query based on the cleaned base query.

Column names match the approved schema and title text is not loaded as sales data. That check would be meaningful for the separate case: A branch export uses headers on row four after three report-title rows. It would not directly verify this scenario's selected implementation: Create a referenced query based on the cleaned base query.

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: Create a referenced query based on the cleaned base query.

'Use a left anti join between submitted vendors and the approved register' addresses the mechanism measured by 'The result contains only vendor records not found in the register'. The current outcome is 'Changes to one query's later steps do not automatically rewrite the duplicated chain' after 'Duplicate the query to create a separate transformation chain', which the distractor leaves unverified.

'A row represents one product-day balance and sums are evaluated at the correct grain' would demonstrate 'Document product-day grain and avoid inventing transaction identifiers when shaping facts', but not 'Changes to one query's later steps do not automatically rewrite the duplicated chain'. The latter requires 'Duplicate the query to create a separate transformation chain' in this scenario.

Using 'Add typed date-derived columns with Date.Year and Date.Month where appropriate' supports the result 'Each sample invoice's derived year and month match its date'. It does not implement 'Duplicate the query to create a separate transformation chain' or prove 'Changes to one query's later steps do not automatically rewrite the duplicated chain' for this question.

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: Duplicate the query to create a separate transformation chain.

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: Duplicate the query to create a separate transformation chain.

A row represents one product-day balance and sums are evaluated at the correct grain. That check would be meaningful for the separate case: An inventory fact source reports product-level totals with a grain of product-day but the model designer assumes transaction-level detail. It would not directly verify this scenario's selected implementation: Duplicate the query to create a separate transformation chain.

'Use an inner merge on the agreed invoice key' is validated when 'The output includes only invoices with a matching key in both lists'. It does not demonstrate 'The three output tables load and the staging query is absent from the model Fields list', which requires 'Disable Enable load for the staging query while keeping referenced outputs enabled'.

A successful 'Split the text column by an agreed delimiter or pattern and check irregular names' would show 'Sample known names yield the approved components without truncation'. This case needs 'Disable Enable load for the staging query while keeping referenced outputs enabled' and evidence that 'The three output tables load and the staging query is absent from the model Fields list', 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 'The three output tables load and the staging query is absent from the model Fields list'; the stated requirement calls for 'Disable Enable load for the staging query while keeping referenced outputs enabled'.

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: Disable Enable load for the staging query while keeping referenced outputs enabled.

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: Disable Enable load for the staging query while keeping referenced outputs enabled.

Sample known names yield the approved components without truncation. That check would be meaningful for the separate case: A file includes one column with full name and the model needs FirstName and LastName for report labels. It would not directly verify this scenario's selected implementation: Disable Enable load for the staging query while keeping referenced outputs enabled.

'Use Change Type with Locale and choose the source's culture for decimal parsing' addresses the mechanism measured by 'A value like 1 234,50 converts to the intended 1234.50 rather than an error'. The current outcome is 'Query diagnostics show less data fetched and the source performs supported filtering' after 'Push appropriate foldable filters and projections early in the query', which the distractor leaves unverified.

'Previous row labels become columns while values retain the intended alignment' would demonstrate 'Transpose the appropriate rectangular table in Power Query after clarifying headers', but not 'Query diagnostics show less data fetched and the source performs supported filtering'. The latter requires 'Push appropriate foldable filters and projections early in the query' in this scenario.

Using 'Pivot selected question codes into respondent-level columns with controlled aggregation' supports the result 'The output has the expected respondent grain and handles duplicate answers explicitly'. It does not implement 'Push appropriate foldable filters and projections early in the query' or prove 'Query diagnostics show less data fetched and the source performs supported filtering' for this question.

A value like 1 234,50 converts to the intended 1234.50 rather than an error. That check would be meaningful for the separate case: A currency column contains a decimal point depending on a French locale and transforms incorrectly under US locale. It would not directly verify this scenario's selected implementation: Push appropriate foldable filters and projections early in the query.

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: Push appropriate foldable filters and projections early in the query.

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: Push appropriate foldable filters and projections early in the query.

'Normalize schema column names and types before an append operation' is validated when 'All regions populate the same normalized columns without duplicate case-variant fields'. It does not demonstrate 'A row represents one product-day balance and sums are evaluated at the correct grain', which requires 'Document product-day grain and avoid inventing transaction identifiers when shaping facts'.

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 'Document product-day grain and avoid inventing transaction identifiers when shaping facts' and evidence that 'A row represents one product-day balance and sums are evaluated at the correct grain', not the other outcome.

The check for 'Remove nondata leading rows and promote the intended header row' is 'Column names match the approved schema and title text is not loaded as sales data'. That does not establish 'A row represents one product-day balance and sums are evaluated at the correct grain'; the stated requirement calls for 'Document product-day grain and avoid inventing transaction identifiers when shaping facts'.

Column names match the approved schema and title text is not loaded as sales data. That check would be meaningful for the separate case: A branch export uses headers on row four after three report-title rows. It would not directly verify this scenario's selected implementation: Document product-day grain and avoid inventing transaction identifiers when shaping facts.

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: Document product-day grain and avoid inventing transaction identifiers when shaping facts.

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: Document product-day grain and avoid inventing transaction identifiers when shaping facts.

'Add an index column for tracing while preserving business identifiers' addresses the mechanism measured by 'The index is sequential in the chosen order and business-key uniqueness is unaffected'. The current outcome is 'Sample known names yield the approved components without truncation' after 'Split the text column by an agreed delimiter or pattern and check irregular names', which the distractor leaves unverified.

'All orders remain after expansion, with missing product attributes represented as null' would demonstrate 'Use a left outer merge from Orders to Products', but not 'Sample known names yield the approved components without truncation'. The latter requires 'Split the text column by an agreed delimiter or pattern and check irregular names' in this scenario.

Using 'Add typed date-derived columns with Date.Year and Date.Month where appropriate' supports the result 'Each sample invoice's derived year and month match its date'. It does not implement 'Split the text column by an agreed delimiter or pattern and check irregular names' or prove 'Sample known names yield the approved components without truncation' 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 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.