Exam Bundle

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: Merge queries on CustomerID with a join that retains all required shipments.

The fact holds numeric events and foreign keys while dimensions contain descriptive attributes. That check would be meaningful for the separate case: A warehouse table combines ProductKey, CustomerKey, DateKey and SalesAmount events with repeated descriptions. It would not directly verify this scenario's selected implementation: Merge queries on CustomerID with a join that retains all required shipments.

The three output tables load and the staging query is absent from the model Fields list. That check would be meaningful for the separate case: An intermediate staging query feeds three downstream fact tables but should not appear as a visible model table. It would not directly verify this scenario's selected implementation: Merge queries on CustomerID with a join that retains all required shipments.

'Push appropriate foldable filters and projections early in the query' is validated when 'Query diagnostics show less data fetched and the source performs supported filtering'. It does not demonstrate 'The resulting table has the combined regional orders without a side-by-side join', which requires 'Append the regional queries after aligning their column names and types'.

A successful 'Add typed date-derived columns with Date.Year and Date.Month where appropriate' would show 'Each sample invoice's derived year and month match its date'. This case needs 'Append the regional queries after aligning their column names and types' and evidence that 'The resulting table has the combined regional orders without a side-by-side join', not the other outcome.

The check for 'Identify or create a stable surrogate key in an upstream or Power Query-supported process' is 'Relationships use the stable key and product descriptions can change without merging unrelated entities'. That does not establish 'The resulting table has the combined regional orders without a side-by-side join'; the stated requirement calls for 'Append the regional queries after aligning their column names and types'.

Relationships use the stable key and product descriptions can change without merging unrelated entities. That check would be meaningful for the separate case: A reporting source lacks a single stable key for each product because SKU values change on rebranding. It would not directly verify this scenario's selected implementation: Append the regional queries after aligning their column names and types.

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: Append the regional queries after aligning their column names and types.

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: Append the regional queries after aligning their column names and types.

'Document product-day grain and avoid inventing transaction identifiers when shaping facts' addresses the mechanism measured by 'A row represents one product-day balance and sums are evaluated at the correct grain'. The current outcome is 'The loaded table contains one row per API record and explicitly typed columns' after 'Expand the list and each record into a tabular Power Query structure', which the distractor leaves unverified.

'Editing a transformation in the base query also updates the dependent regional view' would demonstrate 'Create a referenced query based on the cleaned base query', but not 'The loaded table contains one row per API record and explicitly typed columns'. The latter requires 'Expand the list and each record into a tabular Power Query structure' in this scenario.

Using 'Split the composite address using a validated delimiter and retain unparseable exceptions' supports the result 'The new columns can filter locations without silently dropping malformed addresses'. It does not implement 'Expand the list and each record into a tabular Power Query structure' or prove 'The loaded table contains one row per API record and explicitly typed columns' for this question.

Editing a transformation in the base query also updates the dependent regional view. That check would be meaningful for the separate case: An analyst copies a query for a filtered regional view but wants upstream cleanup changes to flow through automatically. It would not directly verify this scenario's selected implementation: Expand the list and each record into a tabular Power Query structure.

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: Expand the list and each record into a tabular Power Query structure.

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: Expand the list and each record into a tabular Power Query structure.

'Pivot selected question codes into respondent-level columns with controlled aggregation' is validated when 'The output has the expected respondent grain and handles duplicate answers explicitly'. It does not demonstrate 'The fact holds numeric events and foreign keys while dimensions contain descriptive attributes', which requires 'Separate transactional facts from distinct product, customer and date dimensions at a defined grain'.

A successful 'Duplicate the query to create a separate transformation chain' would show 'Changes to one query's later steps do not automatically rewrite the duplicated chain'. This case needs 'Separate transactional facts from distinct product, customer and date dimensions at a defined grain' and evidence that 'The fact holds numeric events and foreign keys while dimensions contain descriptive attributes', not the other outcome.

The check for 'Split the text column by an agreed delimiter or pattern and check irregular names' is 'Sample known names yield the approved components without truncation'. That does not establish 'The fact holds numeric events and foreign keys while dimensions contain descriptive attributes'; the stated requirement calls for 'Separate transactional facts from distinct product, customer and date dimensions at a defined grain'.

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: Separate transactional facts from distinct product, customer and date dimensions at a defined grain.

Changes to one query's later steps do not automatically rewrite the duplicated chain. That check would be meaningful for the separate case: A sandbox calculation should remain independent after the author changes the original query steps. It would not directly verify this scenario's selected implementation: Separate transactional facts from distinct product, customer and date dimensions at a defined grain.

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: Separate transactional facts from distinct product, customer and date dimensions at a defined grain.

'Expand the record column into selected, permitted contact properties' addresses the mechanism measured by 'The transformed preview shows email and phone as separate fields for each customer'. The current outcome is 'Relationships use the stable key and product descriptions can change without merging unrelated entities' after 'Identify or create a stable surrogate key in an upstream or Power Query-supported process', which the distractor leaves unverified.

'The three output tables load and the staging query is absent from the model Fields list' would demonstrate 'Disable Enable load for the staging query while keeping referenced outputs enabled', but not 'Relationships use the stable key and product descriptions can change without merging unrelated entities'. The latter requires 'Identify or create a stable surrogate key in an upstream or Power Query-supported process' in this scenario.

Using 'Use Change Type with Locale and choose the source's culture for decimal parsing' supports the result 'A value like 1 234,50 converts to the intended 1234.50 rather than an error'. It does not implement 'Identify or create a stable surrogate key in an upstream or Power Query-supported process' or prove 'Relationships use the stable key and product descriptions can change without merging unrelated entities' for this question.

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: Identify or create a stable surrogate key in an upstream or Power Query-supported process.

The three output tables load and the staging query is absent from the model Fields list. That check would be meaningful for the separate case: An intermediate staging query feeds three downstream fact tables but should not appear as a visible model table. It would not directly verify this scenario's selected implementation: Identify or create a stable surrogate key in an upstream or Power Query-supported process.

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: Identify or create a stable surrogate key in an upstream or Power Query-supported process.

'Push appropriate foldable filters and projections early in the query' is validated when 'Query diagnostics show less data fetched and the source performs supported filtering'. It does not demonstrate 'Editing a transformation in the base query also updates the dependent regional view', which requires 'Create a referenced query based on the cleaned base query'.

A successful 'Use a left outer merge from Orders to Products' would show 'All orders remain after expansion, with missing product attributes represented as null'. This case needs 'Create a referenced query based on the cleaned base query' and evidence that 'Editing a transformation in the base query also updates the dependent regional view', 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 'Editing a transformation in the base query also updates the dependent regional view'; the stated requirement calls for 'Create a referenced query based on the cleaned base query'.

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.