Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Option A is the only choice that directly addresses the root cause: inconsistent postal-code formatting causing processing errors. In AWS Glue, a DynamicFrame can encounter issues when incoming data contains unexpected types or malformed values. Providing an explicit PySpark schema forces the postal code column to be interpreted consistently (for example, as a string with the expected structure), which prevents downstream steps from failing because of unexpected characters or mixed representations. After enforcing a consistent schema, the Glue job can standardize values (such as trimming extra digits, removing invalid characters, and normalizing case) and then write the corrected output back to Amazon S3 so the data lake is fixed at the source.

The document reinforces that AWS Glue is the managed ETL service used to transform data and move it between stores, which is exactly what is needed to correct bad values and persist clean outputs back into the lake. It also highlights that AWS Glue can perform validation as part of the ETL process, supporting the idea that data can be checked and corrected during processing rather than allowing bad values to break the workflow.

Options B, C, and D do not fix data quality or formatting issues; they relate to workflow state sharing, partition filtering, or internal implementation details, not cleansing invalid postal codes.

Publishing AWS Glue data quality scores to Amazon DataZone requires creating a DQDL ruleset, scheduling it to run regularly, and then linking the corresponding AWS Glue table as a data source in the DataZone project. The setup ensures that data quality scores from Glue are correctly published and accessible within Amazon DataZone:

“You can define DQDL rulesets for Glue tables and publish the data quality results to DataZone when the project is configured with an AWS Glue data source and the rulesets are scheduled.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Option C follows the expected flow without unnecessary complexity and aligns perfectly with the integration flow supported by AWS.

Option B best meets a highly resilient DR requirement with an RTO under 1 hour because a Multi-AZ deployment is designed to provide rapid recovery through redundancy across Availability Zones, rather than requiring a restore-and-rebuild process after a failure. A snapshot-based strategy (Option C) can be valuable for backup and regional recovery, but restoring a new cluster from snapshots is a heavier operation and is not the most reliable path to consistently achieving sub-hour RTO during real incidents. The study material highlights snapshot/restore as an operational mechanism for creating a new environment from a Redshift cluster’s saved state, which inherently implies a restore process and associated time to bring resources online.

Option D (cluster relocation) is primarily an operational move mechanism and is not the same as maintaining continuously resilient capacity for unplanned AZ-impacting events. Option A is not the best choice because Multi-AZ resiliency is aligned with modern Redshift architectures, and RA3 is the recommended node family for contemporary Redshift deployments where performance and managed storage characteristics are key for enterprise analytics. The study material reinforces Amazon Redshift as the platform for high-performance enterprise analytics, so a resilience-first configuration is appropriate.