Apache Airflow Fundamentals · 30% of the exam

Airflow Concepts and Architecture: free practice questions

5 sample questions from our 15-question bank for this domain — answers and explanations included. These are the same scenario-based style as the real Astronomer exam.

1. Which executor runs tasks on a single machine and can run multiple tasks in parallel using local subprocesses?

  • A. LocalExecutor✓ Correct
  • B. SequentialExecutor
  • C. CeleryExecutor
  • D. KubernetesExecutor
Explanation

The LocalExecutor runs tasks in parallel using subprocesses on a single machine. The SequentialExecutor (B) runs one task at a time (no parallelism), while Celery (C) and Kubernetes (D) executors distribute tasks across multiple workers/pods.

2. A DAG RUN in Airflow represents what?

  • A. An instantiation of a DAG for a specific logical/execution date, containing task instances for that run✓ Correct
  • B. The Python file that defines the DAG
  • C. A single operator
  • D. The Airflow scheduler process
Explanation

A DAG run is a specific execution of a DAG for a given logical/execution date, and it contains the task instances for that run. The DAG file (B) is the definition, an operator (C) defines a task, and the scheduler (D) is a component, not a run.

3. In Airflow, what does DAG stand for and represent?

  • A. Directed Acyclic Graph — a collection of tasks with dependencies and no cycles✓ Correct
  • B. Data Aggregation Group — a set of database tables
  • C. Distributed Application Gateway — a network router
  • D. Dynamic Allocation Grid — a scheduling matrix
Explanation

A DAG (Directed Acyclic Graph) defines a workflow as tasks connected by directed dependencies with no cycles, so execution has a clear order and can't loop back. The other expansions are invented and unrelated to Airflow.

4. What is the role of the Airflow Metadata Database?

  • A. It stores the state of DAGs, task instances, runs, connections, variables, and more✓ Correct
  • B. It runs the Python code inside tasks
  • C. It renders the web UI
  • D. It caches large datasets processed by tasks
Explanation

The metadata database persists Airflow's state — DAG runs, task instance states, connections, variables, XComs, and history. It doesn't execute task code (B), render the UI (C, that's the webserver), or cache datasets (D).

5. Which Airflow component is responsible for determining when DAGs and tasks should run and submitting them for execution?

  • A. The Scheduler✓ Correct
  • B. The Webserver
  • C. The Metadata Database
  • D. The XCom backend
Explanation

The Scheduler monitors DAGs, evaluates schedules and dependencies, and triggers task instances for execution. The Webserver (B) serves the UI, the Metadata Database (C) stores state, and XCom (D) passes data between tasks — none schedules runs.

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