AWS DEA-C01 operations guide covering automation, SQL patterns, monitoring, logging, and troubleshooting decisions.
This chapter covers what happens after a platform is live. DEA-C01 expects operational discipline: automate processing, analyze data with the right tools, observe pipelines properly, and catch quality failures before downstream consumers do.
AWS currently weights Data Operations and Support at 22% of scored content.
| Lesson | Focus |
|---|---|
| 3.1 Automation, Data APIs & Query Operations | Learn how AWS services automate recurring data-processing flows and expose data through queries or APIs. |
| 3.2 Analysis, Visualization & SQL Patterns | Learn the analytics and SQL patterns the exam expects across Athena, Redshift, QuickSight, notebooks, and related tools. |
| 3.3 Monitoring, Logging & Pipeline Troubleshooting | Learn the logging, alerting, audit, and troubleshooting practices that keep data pipelines supportable. |
| 3.4 Data Quality, Consistency & Skew | Learn the quality rules, consistency checks, sampling, and skew concepts that show up in real production data work. |