Apache Airflow Fundamentals

Certilum focus

For Apache Airflow Fundamentals, Certilum combines theoretical summaries with guided practice questions where you instrument DAGs with custom sensors and xComs. Our practice exams simulate the Astronomer environment, including scenario‑based drills on fixing broken DAGs and optimizing task concurrency.

Cognitive load

Medium. The exam requires a balance of conceptual understanding of Airflow architecture and practical task-authoring skills. You need to memorize DAG properties, operator parameters, and scheduling rules, but deeper analysis is required for debugging tasks and choosing the right executor for a given scenario.

Study strategy

Start by building at least 10 DAGs using different operators (Python, Bash, Postgres, Kubernetes). Focus on TaskFlow API and dynamic task mapping as these are heavily tested. Use Astronomer's official documentation and their hosted training modules. Practice deploying Airflow on Kubernetes (K8s) and configuring the Celery executor. Join the Airflow Slack community for real-world troubleshooting tips. Dedicating 30–40 hours over 2 weeks with hands-on labs is recommended.

Skills measured

• Architect and write production-grade DAGs using the TaskFlow API
• Configure and troubleshoot executors (Local, Celery, Kubernetes)
• Implement connections, variables, and Secret Backends for secure integration
• Monitor pipeline health using Airflow UI and external logging tools
• Apply DAG testing strategies including unit tests and backfills
• Deploy Airflow on Astronomer's platform using CI/CD pipelines

Mandatory preconditions

• Familiarity with Python programming (functions, decorators, context managers)
• Basic understanding of cron-based scheduling and Unix cron syntax
• Experience with at least one database (Postgre preferred)
• Hands-on exposure to virtual environments or Docker containers