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.
flowchart LR
A["Automate processing"] --> Q["Expose query and API operations"]
Q --> X["Analyze, visualize, and inspect"]
X --> M["Monitor logs, metrics, audit, and alerts"]
M --> D["Diagnose performance and pipeline failures"]
D --> V["Enforce quality, consistency, sampling, and skew controls"]
Operations questions usually start after the pipeline already exists. Strong answers identify the operational signal first: automation failure, query/access issue, monitoring gap, audit need, performance regression, or data-quality defect.
| 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. |
| Scenario clue | Strong first instinct | Weak answer pattern |
|---|---|---|
| A workflow must run repeatedly with dependencies | MWAA, Step Functions, EventBridge, Lambda, or service-native automation based on control shape | Manual reruns and undocumented console steps |
| Analysts need to inspect or visualize data | Athena, Redshift, QuickSight, notebooks, or DataBrew based on user and access pattern | Build another pipeline before checking query/presentation fit |
| A pipeline failed but the cause is unclear | CloudWatch metrics/logs, service run history, CloudTrail, and recent-change evidence | Randomly rerun or add compute first |
| Audit asks who changed or accessed something | CloudTrail and centralized log evidence | Screenshots or informal operator notes |
| Output looks wrong despite successful jobs | data-quality rules, reconciliation, sampling, dedupe, and skew analysis | Treat “job succeeded” as proof data is trustworthy |
Do not jump straight from symptom to fix. DEA-C01 operations answers usually reward evidence-first troubleshooting: identify the signal, inspect the right logs or metrics, then choose the smallest safe remediation.