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Databricks Certified Data Engineer Professional · Databricks
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,D,E - Correct answer s : A, D, E — A row is excluded from `silver_line_items` if it violates either the `valid_price` rule or the `valid_qty` rule.; Both `valid_price` and `valid_qty` are tracked as separate named expectations in the pipeline's data quality metrics.; Replacing `expect_all_or_drop` with `expect_all` would cause violating rows to be written to the table while still recording violation counts for both rules. A . The or-drop group decorator removes a row if it fails any one of the rules in the dictionary, so violating either the price rule or the quantity rule is enough to exclude that row from the target table. B . Rolling back the entire update on the first violation describes the fail variant of the group decorator, not the drop variant, which instead removes offending rows and keeps the update running. C . The or-drop decorator drops a row as soon as it fails any single rule in the set, so a row violating only one of the two rules is still excluded rather than being written through. D . Each key in the rules dictionary becomes its own named expectation, so the pipeline's data quality metrics report violation counts for `valid_price` and `valid_qty` independently. E . The plain warn variant of the group decorator keeps every row in the output regardless of rule failures while still tallying how many rows violated each named rule, unlike the drop variant used in the snippet. F . The dictionary keys are just user-chosen expectation names for tracking purposes and have no requirement to match any column name in the source table.