DEA-C01 · Question #300
A company runs a data platform on AWS. The data platform uses AWS Glue to provide a data catalog and to perform processing. The company notices quality issues in the data. The company needs to…
The correct answer is A. Use AWS Glue jobs to implement AWS Glue Data Quality validations that include anomaly. AWS Glue Data Quality provides native, managed data quality rules with built-in anomaly detection, allowing validation of known issues while automatically identifying unexpected data quality problems with minimal operational effort.
Question
A company runs a data platform on AWS. The data platform uses AWS Glue to provide a data catalog and to perform processing. The company notices quality issues in the data. The company needs to implement data quality validations. The validations must include rules for known issues. The validations must have the ability to automatically detect unexpected data quality issues. Which solution will meet these requirements with the LEAST operation overhead?
Options
- AUse AWS Glue jobs to implement AWS Glue Data Quality validations that include anomaly
- BUse AWS Glue jobs to implement data quality rules that use open source data quality
- CUse AWS Glue DataBrew to profile the data. Configure data quality rules based on the data
- DUse AWS Glue jobs to implement data quality validations that use SQL statements.
How the community answered
(31 responses)- A71% (22)
- B3% (1)
- C10% (3)
- D16% (5)
Explanation
AWS Glue Data Quality provides native, managed data quality rules with built-in anomaly detection, allowing validation of known issues while automatically identifying unexpected data quality problems with minimal operational effort.
Topics
Community Discussion
No community discussion yet for this question.