DEA-C01 · Question #222
A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary. The data engineer needs a…
The correct answer is B. Referentiallntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0. The ReferentialIntegrity rule checks that every (city, state) pair in the primary dataset has a matching (ref_city, ref_state) pair in the reference dataset, and setting the threshold to 1.0 enforces a 100% match rate. This directly validates exact correspondence of those…
Question
A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary. The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job. Which rule will meet these requirements?
Options
- ADatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0
- BReferentiallntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0
- CDatasetMatch "reference" "city->ref_city, state->ref_state" = 100
- DReferentialintegrity "city,state" "reference.{ref_city,ref_state}" = 100
How the community answered
(25 responses)- A8% (2)
- B72% (18)
- C4% (1)
- D16% (4)
Explanation
The ReferentialIntegrity rule checks that every (city, state) pair in the primary dataset has a matching (ref_city, ref_state) pair in the reference dataset, and setting the threshold to 1.0 enforces a 100% match rate. This directly validates exact correspondence of those columns without extra overhead.
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