Astronomer Apache Airflow 3 Fundamentals · 30% practice allocation

Astronomer Apache Airflow 3 Fundamentals: Airflow Concepts and Architecture Practice Questions

5 free sample questions from the Airflow Concepts and Architecture domain, with answers and explanations. The full Apache Airflow 3 Fundamentals bank has 55 questions across 4 domains. Questions are scenario-based and written from the official Astronomer exam guide.

1.

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

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

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

2.

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

  • A. The XCom backend
  • B. The Webserver
  • C. The Metadata Database
  • D. The Scheduler✓ Correct
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 (A) passes data between tasks — none schedules runs.

3.

Which executor is designed to run each task in its own isolated pod on a Kubernetes cluster, scaling elastically?

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

The KubernetesExecutor launches a dedicated pod per task instance, giving isolation and elastic scaling on a Kubernetes cluster. Sequential (A) and Local (B) run on a single machine, and DebugExecutor (D) is for debugging DAGs in an IDE, not scalable production execution.

4.

In Airflow, what does DAG stand for and represent?

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

5.

A task instance can move through several states during a DAG run. Which of the following is a valid Airflow task state?

  • A. success (the task completed successfully)✓ Correct
  • B. compiled
  • C. partitioned
  • D. indexed
Explanation

Valid task instance states include queued, running, success, failed, up_for_retry, skipped, and upstream_failed, among others. 'success' is one of them. 'compiled', 'indexed', and 'partitioned' are not Airflow task states.

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