AWS Certified Data Engineer – Associate Practice Test

In April 2025, a healthcare analytics team separating raw, curated, and research zones is updating an AWS data engineering workload. A Glue job intermittently fails after schema changes from a source team. The data engineer needs automated detection, logs for root cause, and notification before downstream dashboards refresh. A new SLA requires alerting before dashboard refresh, not after business users report stale data. The team must keep the design aligned to DEA-C01 Data Operations and Support objectives, avoid unnecessary custom operations, and preserve auditability. Which operations pattern should be implemented?

In June 2025, a genomics lab receiving partner files under strict data-use agreements is updating an AWS data engineering workload. Data engineers already maintain Airflow DAGs with custom operators, backfills, SLAs, and cross-DAG dependencies. Leadership wants managed infrastructure but minimal rewrite of the orchestration logic. The design must support blue/green deployment of transformation code and rollback without losing accepted records. The team must keep the design aligned to DEA-C01 Data Ingestion and Transformation objectives, avoid unnecessary custom operations, and preserve auditability. Which AWS service should be selected?

In August 2025, an online marketplace joining orders, refunds, and fraud signals is updating an AWS data engineering workload. A bucket stores regulated curated data. Auditors need principal, action, object key, and time for GetObject and PutObject calls, retained centrally for investigations. The security team requires centrally queryable evidence of policy changes and object access during investigations. The team must keep the design aligned to DEA-C01 Data Security and Governance objectives, avoid unnecessary custom operations, and preserve auditability. Which logging configuration should be used?

In June 2025, a genomics lab receiving partner files under strict data-use agreements is updating an AWS data engineering workload. The pipeline receives 18,000 JSON events per second with occasional five-minute bursts. Operations needs event-time windows, late-arriving event handling, and dashboard updates within 10 seconds. The design must support blue/green deployment of transformation code and rollback without losing accepted records. The team must keep the design aligned to DEA-C01 Data Ingestion and Transformation objectives, avoid unnecessary custom operations, and preserve auditability. Which design best meets the ingestion and transformation requirement with the least custom operations?

In March 2025, a healthcare analytics team separating raw, curated, and research zones is updating an AWS data engineering workload. A data lake receives vendor extracts with unknown columns. Compliance requires detection of email, account number, and national identifier fields before analyst access is granted. Access requests are reviewed monthly, and the same control must work for Athena, Glue, and Redshift Spectrum consumers. The team must keep the design aligned to DEA-C01 Data Security and Governance objectives, avoid unnecessary custom operations, and preserve auditability. Which governance workflow is most appropriate?