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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.
A security team needs the number of distinct device identifiers per day rather than the number of raw events.
Which implementation decision best addresses this requirement?
Counting distinct identifiers avoids treating repeated events from one device as separate devices. The selected implementation is the one directly aligned with the constraints in the scenario.
Approximate distinct aggregates can reduce resource costs when exact cardinality is not required.
A deterministic latest-record window supports deduplication by business key with explicit ordering.
Distributed aggregates avoid materializing large source datasets in driver memory.
A security team needs the number of distinct device identifiers per day rather than the number of raw events.
Which acceptance result most directly demonstrates that the intended solution works?
Counting distinct identifiers avoids treating repeated events from one device as separate devices.
The result is close to a sampled exact distinct count within the accepted accuracy tolerance. That finding does not establish that a day with five events from two devices yields a distinct-device count of two.
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 a day with five events from two devices yields a distinct-device count of two.
The calculation completes with distributed stages and bounded driver memory. That finding does not establish that a day with five events from two devices yields a distinct-device count of two.
A marketing team wants a fast approximate unique-user count on a huge events DataFrame and accepts estimation error.
Which implementation decision best addresses this requirement?
Approximate distinct aggregates can reduce resource costs when exact cardinality is not required. The selected implementation is the one directly aligned with the constraints in the scenario.
An arithmetic mean answers the per-shipment average question; sum answers a different aggregate.
DataFrame deduplication removes repeated records according to the chosen identity columns.
Shuffle partition count controls post-shuffle task granularity; excessive partitions add overhead, while too few can create huge tasks.
A marketing team wants a fast approximate unique-user count on a huge events DataFrame and accepts estimation error.
Which acceptance result most directly demonstrates that the intended solution works?
Approximate distinct aggregates can reduce resource costs when exact cardinality is not required.
For durations of 10 and 20 minutes the reported route average is 15 minutes. That finding does not establish that the result is close to a sampled exact distinct count within the accepted accuracy tolerance.
Exact repeated input rows appear once after the transformation. That finding does not establish that the result is close to a sampled exact distinct count within the accepted accuracy tolerance.
The revised stage has a reasonable task count and lower wall time without excessive spill. That finding does not establish that the result is close to a sampled exact distinct count within the accepted accuracy tolerance.
A fulfillment report needs average shipment duration by route rather than the total duration of all shipments.
Which implementation decision best addresses this requirement?
An arithmetic mean answers the per-shipment average question; sum answers a different aggregate. The selected implementation is the one directly aligned with the constraints in the scenario.
A deterministic latest-record window supports deduplication by business key with explicit ordering.
The key choice determines the unit of uniqueness and must reflect the data grain.
Memory settings and partition sizes interact; changing partition count alone is not a complete memory fix.
A fulfillment report needs average shipment duration by route rather than the total duration of all shipments.
Which acceptance result most directly demonstrates that the intended solution works?
An arithmetic mean answers the per-shipment average question; sum answers a different aggregate.
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 for durations of 10 and 20 minutes the reported route average is 15 minutes.
Two legitimate orders for the same customer remain while true duplicate order lines collapse. That finding does not establish that for durations of 10 and 20 minutes the reported route average is 15 minutes.
The representative stage completes without OOM and resource usage stays within configured limits. That finding does not establish that for durations of 10 and 20 minutes the reported route average is 15 minutes.
A customer dimension contains multiple updates for one customer and Silver should retain the latest row per customer based on a reliable event timestamp.
Which implementation decision best addresses this requirement?
A deterministic latest-record window supports deduplication by business key with explicit ordering. The selected implementation is the one directly aligned with the constraints in the scenario.
DataFrame deduplication removes repeated records according to the chosen identity columns.
Distributed aggregates avoid materializing large source datasets in driver memory.
The threshold affects automatic broadcast eligibility and can change join execution plans.
A customer dimension contains multiple updates for one customer and Silver should retain the latest row per customer based on a reliable event timestamp.
Which acceptance result most directly demonstrates that the intended solution works?
A deterministic latest-record window supports deduplication by business key with explicit ordering.
Exact repeated input rows appear once after the transformation. That finding does not establish that the retained customer row has the newest timestamp for each key and ties follow the documented tie-break rule.
The calculation completes with distributed stages and bounded driver memory. That finding does not establish that the retained customer row has the newest timestamp for each key and ties follow the documented tie-break rule.
The physical plan reflects the intended join strategy and measured runtime improves without memory pressure. That finding does not establish that the retained customer row has the newest timestamp for each key and ties follow the documented tie-break rule.
A clickstream DataFrame contains exact duplicate records introduced by upstream retry behavior and should contain one copy of each identical row.
Which implementation decision best addresses this requirement?
DataFrame deduplication removes repeated records according to the chosen identity columns. The selected implementation is the one directly aligned with the constraints in the scenario.
The key choice determines the unit of uniqueness and must reflect the data grain.
Shuffle partition count controls post-shuffle task granularity; excessive partitions add overhead, while too few can create huge tasks.
Caching can reuse a computed dataset but consumes executor resources and should be measured.
A clickstream DataFrame contains exact duplicate records introduced by upstream retry behavior and should contain one copy of each identical row.
Which acceptance result most directly demonstrates that the intended solution works?
DataFrame deduplication removes repeated records according to the chosen identity columns.
Two legitimate orders for the same customer remain while true duplicate order lines collapse. That finding does not establish that exact repeated input rows appear once after the transformation.
The revised stage has a reasonable task count and lower wall time without excessive spill. That finding does not establish that exact repeated input rows appear once after the transformation.
The second computation avoids redundant recomputation and does not create harmful memory eviction. That finding does not establish that exact repeated input rows appear once after the transformation.
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