How to enforce custom data quality validation rules in AWS Glue ETL?
A retail company uses AWS Glue for extract, transform, and load (ETL) operations on a dataset that contains information about customer orders. The company wants to implement specific validation rules to ensure data accuracy and consistency. Which solution will meet these requirements?
Community Votes
100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The question tests choosing AWS Glue Data Quality rulesets for tailored validation rules; the trap is confusing job bookmarks or Data Catalog schema management with actual data quality enforcement.
AWS Glue Data Quality rulesets let you codify custom validation checks for ETL datasets, so order data is tested for accuracy and consistency before it is trusted. This page confirms that option B is the right fit when requirements call for specific, business-defined rules rather than generic transforms or metadata cataloging.
A common wrong answer is option C, assuming built-in AWS Glue Data Quality transforms cover any validation requirement, but those transforms target standard checks rather than custom business-specific rulesets.
Community Discussion (3 comments)
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Expert Analysis
Why the Answer Is Correct
Option B matches the requirement because AWS Glue Data Quality rulesets are designed to define custom validation checks for ETL datasets. A ruleset can express specific accuracy and consistency conditions for the customer orders data and then be evaluated as part of the Glue job workflow. This gives the retail company tailored control rather than relying only on generic checks. The phrase “specific validation rules” is the key signal that a configurable, custom ruleset is needed.Why the Other Options Are Wrong
Option A is wrong because AWS Glue job bookmarks track which data has already been processed for incremental runs; they do not validate data accuracy or consistency. Option C is less appropriate because built-in AWS Glue Data Quality transforms are intended for standard validations, while the scenario calls for specific business-defined rules. Option D is wrong because AWS Glue Data Catalog stores schema and metadata; it does not enforce row-level data quality checks. None of these options provides the custom validation logic described in the requirement.Community Comment Notes
Ja13 emphasized that custom rulesets allow you to “define precise data quality checks tailored to your specific needs,” which directly matches the requirement for specific validation rules. tgv focused on the flexibility to implement custom validation rules for the company’s requirements, and HunkyBunky simply concluded “Only B - makes sense.” The commenters consistently treated job bookmarks, built-in transforms, and the Data Catalog as insufficient for tailored quality enforcement.Official Reference
Exam Strategy
When a scenario says “specific validation rules” or business-defined accuracy and consistency checks, map that to AWS Glue Data Quality rulesets rather than generic Glue features. Eliminate options that only track processed data or store metadata, because they do not evaluate row-level quality.
Frequently Asked Questions
Why are built-in AWS Glue Data Quality transforms not enough for specific validation rules?
Built-in transforms provide standard checks, but the scenario asks for business-specific rules; custom rulesets express those exact conditions with DQDL.
Do AWS Glue job bookmarks validate data accuracy and consistency?
No. Job bookmarks only track which data has already been processed during incremental runs; they do not evaluate row-level quality.
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