How to enforce custom data quality validation rules in AWS Glue ETL?

DEA-C01 By islever Editorial Last updated Oct 20, 2026

Ensure data quality. Automate data processing by using AWS services.

Short answer

B Create custom AWS Glue Data Quality rulesets to define tailored accuracy and consistency validation checks for the order dataset.

Question

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?

Use AWS Glue job bookmarks to track the data for accuracy and consistency.
Create custom AWS Glue Data Quality rulesets to define specific data quality checks. Correct Answer
Use the built-in AWS Glue Data Quality transforms for standard data quality validations.
Use AWS Glue Data Catalog to maintain a centralized data schema and metadata repository.

Key Takeaways

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.

Common mistake: 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.

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

https://docs.aws.amazon.com/glue/latest/dg/glue-data-quality.html

This link opens the official vendor documentation in a new tab.

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.

More DEA-C01 FAQ →

Community Votes

100%

100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 3 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 3

Ja13 👍 4 Answered B

B. Create custom AWS Glue Data Quality rulesets to define specific data quality checks. Custom AWS Glue Data Quality rulesets allow you to define precise data quality checks tailored to your specific needs, ensuring that the data meets the required standards of accuracy and consistency. This approach provides flexibility to implement a wide range of validation rules based on your business requirements.
tgv 👍 3 Answered B

This option provides the necessary flexibility to define and implement custom validation rules tailored to the company's specific requirements for data accuracy and consistency.
HunkyBunky 👍 2 Answered B

Only B - makes sense

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