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The source uses camelCase customerId, but the target silver contract requires the snake_case column name customer_id.
Which acceptance result most directly demonstrates that the intended solution works?
Renaming a column changes its schema label, not the business key values it contains.
The output contains completed orders only and retains all qualifying rows. That finding does not establish that the silver schema contains customer_id and the sampled identifiers match their source values.
A respondent with a null array remains represented once with a null exploded value. That finding does not establish that the silver schema contains customer_id and the sampled identifiers match their source values.
Payment count remains unchanged for one-to-one merchant keys and unmatched payments have null merchant fields. That finding does not establish that the silver schema contains customer_id and the sampled identifiers match their source values.
An analyst must retain only completed purchases from a DataFrame containing completed, pending and refunded orders.
Which implementation decision best addresses this requirement?
Row filtering limits the dataset to the specified business condition before aggregation. The selected implementation is the one directly aligned with the constraints in the scenario.
Exploding an array changes grain from one purchase row to individual item rows.
The split operation exposes components of a delimited string without manual driver-side string processing.
A left anti join returns only left-side rows without any matching right-side key.
An analyst must retain only completed purchases from a DataFrame containing completed, pending and refunded orders.
Which acceptance result most directly demonstrates that the intended solution works?
Row filtering limits the dataset to the specified business condition before aggregation.
A purchase with three items produces three item rows linked to the same parent order. That finding does not establish that the output contains completed orders only and retains all qualifying rows.
The derived region equals the first path component for representative valid inputs. That finding does not establish that the output contains completed orders only and retains all qualifying rows.
The output contains missing master keys and excludes all already matched keys. That finding does not establish that the output contains completed orders only and retains all qualifying rows.
A JSON purchase record contains an array of items and the silver table needs one row for each purchased item.
Which implementation decision best addresses this requirement?
Exploding an array changes grain from one purchase row to individual item rows. The selected implementation is the one directly aligned with the constraints in the scenario.
Outer exploding preserves the parent row in cases where ordinary explode would produce no element row.
An inner join retains records present on both sides of the join condition.
Left semi joins filter the left DataFrame by existence of a right match without adding right-side columns.
A JSON purchase record contains an array of items and the silver table needs one row for each purchased item.
Which acceptance result most directly demonstrates that the intended solution works?
Exploding an array changes grain from one purchase row to individual item rows.
A respondent with a null array remains represented once with a null exploded value. That finding does not establish that a purchase with three items produces three item rows linked to the same parent order.
A sale with a missing customer key is absent from the joined result while matched sales remain. That finding does not establish that a purchase with three items produces three item rows linked to the same parent order.
Only eligible transaction rows remain and the output schema contains the left-side columns. That finding does not establish that a purchase with three items produces three item rows linked to the same parent order.
A survey record has a null array but the data contract still requires preserving the source respondent when flattening.
Which implementation decision best addresses this requirement?
Outer exploding preserves the parent row in cases where ordinary explode would produce no element row. The selected implementation is the one directly aligned with the constraints in the scenario.
The split operation exposes components of a delimited string without manual driver-side string processing.
A left join preserves the full left-side payment population and returns null right-side attributes for unmatched merchants.
Broadcasting a sufficiently small dimension can avoid a large shuffle join when memory and thresholds permit.
A survey record has a null array but the data contract still requires preserving the source respondent when flattening.
Which acceptance result most directly demonstrates that the intended solution works?
Outer exploding preserves the parent row in cases where ordinary explode would produce no element row.
The derived region equals the first path component for representative valid inputs. That finding does not establish that a respondent with a null array remains represented once with a null exploded value.
Payment count remains unchanged for one-to-one merchant keys and unmatched payments have null merchant fields. That finding does not establish that a respondent with a null array remains represented once with a null exploded value.
The physical plan shows a broadcast join and the query runs within memory constraints. That finding does not establish that a respondent with a null array remains represented once with a null exploded value.
A source stores full product paths like 'region/category/item' in one field, and a silver table requires the region segment separately.
Which implementation decision best addresses this requirement?
The split operation exposes components of a delimited string without manual driver-side string processing. The selected implementation is the one directly aligned with the constraints in the scenario.
An inner join retains records present on both sides of the join condition.
A left anti join returns only left-side rows without any matching right-side key.
Name-based union reduces accidental column swaps when schemas have different field order.
A source stores full product paths like 'region/category/item' in one field, and a silver table requires the region segment separately.
Which acceptance result most directly demonstrates that the intended solution works?
The split operation exposes components of a delimited string without manual driver-side string processing.
A sale with a missing customer key is absent from the joined result while matched sales remain. That finding does not establish that the derived region equals the first path component for representative valid inputs.
The output contains missing master keys and excludes all already matched keys. That finding does not establish that the derived region equals the first path component for representative valid inputs.
Rows from both sources retain the correct values under each named column. That finding does not establish that the derived region equals the first path component for representative valid inputs.
A reporting engineer joins sales to customers and requires only sales with a matching customer dimension row.
Which implementation decision best addresses this requirement?
An inner join retains records present on both sides of the join condition. The selected implementation is the one directly aligned with the constraints in the scenario.
A left join preserves the full left-side payment population and returns null right-side attributes for unmatched merchants.
Left semi joins filter the left DataFrame by existence of a right match without adding right-side columns.
PySpark DataFrame union appends rows without guaranteeing deduplication; distinct is a separate operation.
A reporting engineer joins sales to customers and requires only sales with a matching customer dimension row.
Which acceptance result most directly demonstrates that the intended solution works?
An inner join retains records present on both sides of the join condition.
Payment count remains unchanged for one-to-one merchant keys and unmatched payments have null merchant fields. That finding does not establish that a sale with a missing customer key is absent from the joined result while matched sales remain.
Only eligible transaction rows remain and the output schema contains the left-side columns. That finding does not establish that a sale with a missing customer key is absent from the joined result while matched sales remain.
The output has one instance of every identical row after explicit deduplication. That finding does not establish that a sale with a missing customer key is absent from the joined result while matched sales remain.
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