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A silver ETL receives rows with negative item quantities that should not be allowed into final customer order facts.
Which acceptance result most directly demonstrates that the intended solution works?
Quality checks make business constraints testable rather than merely expecting upstream files to be valid.
Accepted silver orders all contain customer_id and missing-key events are separately visible. That finding does not establish that negative quantity rows are flagged and never appear in the accepted silver facts.
An amended past transaction is reflected in the corresponding published daily total after the approved update. That finding does not establish that negative quantity rows are flagged and never appear in the accepted silver facts.
Both vendors' rows appear with the extra field null for the source that did not provide it. That finding does not establish that negative quantity rows are flagged and never appear in the accepted silver facts.
A pipeline requires customer_id to be present for all published orders, but raw bronze events sometimes omit it.
Which implementation decision best addresses this requirement?
A business-key existence rule protects joins and deduplication from invalid empty keys. The selected implementation is the one directly aligned with the constraints in the scenario.
Cleansing upstream avoids repeated ad hoc date interpretations in downstream BI.
Uneven key distribution can make a few tasks much slower than the others, regardless of total partition count.
Arrays must be handled through collection-aware operations rather than assumed to be scalar fields.
A pipeline requires customer_id to be present for all published orders, but raw bronze events sometimes omit it.
Which acceptance result most directly demonstrates that the intended solution works?
A business-key existence rule protects joins and deduplication from invalid empty keys.
Known date examples are parsed into consistent calendar dates before they contribute to Gold aggregates. That finding does not establish that accepted silver orders all contain customer_id and missing-key events are separately visible.
Stage task durations become more balanced and the hot merchant key no longer dominates elapsed time. That finding does not establish that accepted silver orders all contain customer_id and missing-key events are separately visible.
The filter correctly identifies events containing the specified discount code. That finding does not establish that accepted silver orders all contain customer_id and missing-key events are separately visible.
A source offers multiple date formats and analysts see inconsistent month buckets in Gold.
Which implementation decision best addresses this requirement?
Cleansing upstream avoids repeated ad hoc date interpretations in downstream BI. The selected implementation is the one directly aligned with the constraints in the scenario.
Derived metrics become stale if their update semantics do not account for corrected upstream records.
An inner join filters unmatched records, while a left join preserves the left dataset.
Conditional aggregation counts qualifying records rather than summing nonnumeric labels.
A source offers multiple date formats and analysts see inconsistent month buckets in Gold.
Which acceptance result most directly demonstrates that the intended solution works?
Cleansing upstream avoids repeated ad hoc date interpretations in downstream BI.
An amended past transaction is reflected in the corresponding published daily total after the approved update. That finding does not establish that known date examples are parsed into consistent calendar dates before they contribute to Gold aggregates.
A source event with no optional attributes remains in the result with null right-side fields. That finding does not establish that known date examples are parsed into consistent calendar dates before they contribute to Gold aggregates.
A group with three successful and two failed records reports a success count of three. That finding does not establish that known date examples are parsed into consistent calendar dates before they contribute to Gold aggregates.
A daily sales report combines corrected historical transactions with new data, and its old daily aggregate is never recalculated after corrections.
Which implementation decision best addresses this requirement?
Derived metrics become stale if their update semantics do not account for corrected upstream records. The selected implementation is the one directly aligned with the constraints in the scenario.
Uneven key distribution can make a few tasks much slower than the others, regardless of total partition count.
Name-based alignment and explicit missing-column handling avoid positional corruption.
A rate requires both the numerator and population denominator and cannot be inferred from counts alone.
A daily sales report combines corrected historical transactions with new data, and its old daily aggregate is never recalculated after corrections.
Which acceptance result most directly demonstrates that the intended solution works?
Derived metrics become stale if their update semantics do not account for corrected upstream records.
Stage task durations become more balanced and the hot merchant key no longer dominates elapsed time. That finding does not establish that an amended past transaction is reflected in the corresponding published daily total after the approved update.
Both vendors' rows appear with the extra field null for the source that did not provide it. That finding does not establish that an amended past transaction is reflected in the corresponding published daily total after the approved update.
For two late deliveries out of ten, the resulting late rate is 20 percent. That finding does not establish that an amended past transaction is reflected in the corresponding published daily total after the approved update.
A join between two very large skewed tables times out because one high-frequency merchant key owns most rows.
Which implementation decision best addresses this requirement?
Uneven key distribution can make a few tasks much slower than the others, regardless of total partition count. The selected implementation is the one directly aligned with the constraints in the scenario.
An inner join filters unmatched records, while a left join preserves the left dataset.
Arrays must be handled through collection-aware operations rather than assumed to be scalar fields.
Nested-field projection avoids converting every JSON object into an opaque string.
A join between two very large skewed tables times out because one high-frequency merchant key owns most rows.
Which acceptance result most directly demonstrates that the intended solution works?
Uneven key distribution can make a few tasks much slower than the others, regardless of total partition count.
A source event with no optional attributes remains in the result with null right-side fields. That finding does not establish that stage task durations become more balanced and the hot merchant key no longer dominates elapsed time.
The filter correctly identifies events containing the specified discount code. That finding does not establish that stage task durations become more balanced and the hot merchant key no longer dominates elapsed time.
The output contains city and postal_code columns with values matching the input struct. That finding does not establish that stage task durations become more balanced and the hot merchant key no longer dominates elapsed time.
A silver mapping uses an inner join to an optional attributes table and accidentally drops source events with no attributes.
Which implementation decision best addresses this requirement?
An inner join filters unmatched records, while a left join preserves the left dataset. The selected implementation is the one directly aligned with the constraints in the scenario.
Name-based alignment and explicit missing-column handling avoid positional corruption.
Conditional aggregation counts qualifying records rather than summing nonnumeric labels.
Categorical placeholder values are not valid numeric measurements and need deliberate treatment.
A silver mapping uses an inner join to an optional attributes table and accidentally drops source events with no attributes.
Which acceptance result most directly demonstrates that the intended solution works?
An inner join filters unmatched records, while a left join preserves the left dataset.
Both vendors' rows appear with the extra field null for the source that did not provide it. That finding does not establish that a source event with no optional attributes remains in the result with null right-side fields.
A group with three successful and two failed records reports a success count of three. That finding does not establish that a source event with no optional attributes remains in the result with null right-side fields.
A normal numeric string converts successfully while N/A is represented as missing rather than as an invalid number. That finding does not establish that a source event with no optional attributes remains in the result with null right-side fields.
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