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Two DataFrames have identical logical columns but their physical column orders differ after separate ingestion changes.
Which acceptance result most directly demonstrates that the intended solution works?
Name-based union reduces accidental column swaps when schemas have different field order.
The output has one instance of every identical row after explicit deduplication. That finding does not establish that rows from both sources retain the correct values under each named column.
Each fact row matches only the dimension record with its key and effective date. That finding does not establish that rows from both sources retain the correct values under each named column.
A day with five events from two devices yields a distinct-device count of two. That finding does not establish that rows from both sources retain the correct values under each named column.
A PySpark developer expects DataFrame.union to remove duplicate rows just like a SQL UNION statement.
Which implementation decision best addresses this requirement?
PySpark DataFrame union appends rows without guaranteeing deduplication; distinct is a separate operation. The selected implementation is the one directly aligned with the constraints in the scenario.
A cross join constructs every pair of rows and can grow explosively for large inputs.
SQL and DataFrame ordinary equality do not match null to null; null-safe comparison has different semantics.
Approximate distinct aggregates can reduce resource costs when exact cardinality is not required.
A PySpark developer expects DataFrame.union to remove duplicate rows just like a SQL UNION statement.
Which acceptance result most directly demonstrates that the intended solution works?
PySpark DataFrame union appends rows without guaranteeing deduplication; distinct is a separate operation.
The controlled input produces exactly thirty segment-tier pairs. That finding does not establish that the output has one instance of every identical row after explicit deduplication.
The test demonstrates the chosen null policy, including how two null keys are treated. That finding does not establish that the output has one instance of every identical row after explicit deduplication.
The result is close to a sampled exact distinct count within the accepted accuracy tolerance. That finding does not establish that the output has one instance of every identical row after explicit deduplication.
A report needs all combinations of ten marketing segments and three geographic tiers without any join keys.
Which implementation decision best addresses this requirement?
A cross join constructs every pair of rows and can grow explosively for large inputs. The selected implementation is the one directly aligned with the constraints in the scenario.
A multi-key join constrains rows to the same account and date rather than multiplying cross-date matches.
Grouping creates one aggregate per grouping key and sum computes the requested financial measure.
An arithmetic mean answers the per-shipment average question; sum answers a different aggregate.
A report needs all combinations of ten marketing segments and three geographic tiers without any join keys.
Which acceptance result most directly demonstrates that the intended solution works?
A cross join constructs every pair of rows and can grow explosively for large inputs.
Each fact row matches only the dimension record with its key and effective date. That finding does not establish that the controlled input produces exactly thirty segment-tier pairs.
Two rows for the same store and day contribute one summed revenue record. That finding does not establish that the controlled input produces exactly thirty segment-tier pairs.
For durations of 10 and 20 minutes the reported route average is 15 minutes. That finding does not establish that the controlled input produces exactly thirty segment-tier pairs.
A fact and dimension use both account_id and effective_date as keys, and joining only account_id produces duplicate matches.
Which implementation decision best addresses this requirement?
A multi-key join constrains rows to the same account and date rather than multiplying cross-date matches. The selected implementation is the one directly aligned with the constraints in the scenario.
SQL and DataFrame ordinary equality do not match null to null; null-safe comparison has different semantics.
Counting distinct identifiers avoids treating repeated events from one device as separate devices.
A deterministic latest-record window supports deduplication by business key with explicit ordering.
A fact and dimension use both account_id and effective_date as keys, and joining only account_id produces duplicate matches.
Which acceptance result most directly demonstrates that the intended solution works?
A multi-key join constrains rows to the same account and date rather than multiplying cross-date matches.
The test demonstrates the chosen null policy, including how two null keys are treated. That finding does not establish that each fact row matches only the dimension record with its key and effective date.
A day with five events from two devices yields a distinct-device count of two. That finding does not establish that each fact row matches only the dimension record with its key and effective date.
The retained customer row has the newest timestamp for each key and ties follow the documented tie-break rule. That finding does not establish that each fact row matches only the dimension record with its key and effective date.
A developer joins facts where some business keys are null and expects ordinary equals comparison to match two null keys.
Which implementation decision best addresses this requirement?
SQL and DataFrame ordinary equality do not match null to null; null-safe comparison has different semantics. The selected implementation is the one directly aligned with the constraints in the scenario.
Grouping creates one aggregate per grouping key and sum computes the requested financial measure.
Approximate distinct aggregates can reduce resource costs when exact cardinality is not required.
DataFrame deduplication removes repeated records according to the chosen identity columns.
A developer joins facts where some business keys are null and expects ordinary equals comparison to match two null keys.
Which acceptance result most directly demonstrates that the intended solution works?
SQL and DataFrame ordinary equality do not match null to null; null-safe comparison has different semantics.
Two rows for the same store and day contribute one summed revenue record. That finding does not establish that the test demonstrates the chosen null policy, including how two null keys are treated.
The result is close to a sampled exact distinct count within the accepted accuracy tolerance. That finding does not establish that the test demonstrates the chosen null policy, including how two null keys are treated.
Exact repeated input rows appear once after the transformation. That finding does not establish that the test demonstrates the chosen null policy, including how two null keys are treated.
A sales dashboard requires gross revenue grouped by store and business day from an orders DataFrame.
Which implementation decision best addresses this requirement?
Grouping creates one aggregate per grouping key and sum computes the requested financial measure. The selected implementation is the one directly aligned with the constraints in the scenario.
Counting distinct identifiers avoids treating repeated events from one device as separate devices.
An arithmetic mean answers the per-shipment average question; sum answers a different aggregate.
The key choice determines the unit of uniqueness and must reflect the data grain.
A sales dashboard requires gross revenue grouped by store and business day from an orders DataFrame.
Which acceptance result most directly demonstrates that the intended solution works?
Grouping creates one aggregate per grouping key and sum computes the requested financial measure.
A day with five events from two devices yields a distinct-device count of two. That finding does not establish that two rows for the same store and day contribute one summed revenue record.
For durations of 10 and 20 minutes the reported route average is 15 minutes. That finding does not establish that two rows for the same store and day contribute one summed revenue record.
Two legitimate orders for the same customer remain while true duplicate order lines collapse. That finding does not establish that two rows for the same store and day contribute one summed revenue record.
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