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Apache Airflow Interview Questions

2 free questions to get you started. Unlock all sections (Normal, Code & Logic) with Pro.

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1

What is a DAG in Airflow? Normal

DAG stands for Directed Acyclic Graph. Directed means tasks have a clear order (A → B → C). Acyclic means no loops—you can’t have B depend on C and C depend on B. Graph means a set of tasks (nodes) and dependencies (edges). In Airflow you define DAGs in Python; the scheduler runs tasks according to dependencies and schedule.

2

What is the difference between an Operator and a Sensor? Normal

An Operator performs an action (run a SQL query, call an API, run a script) and then completes. A Sensor waits for a condition to become true (e.g. a file appears, a partition exists) by polling; it keeps checking until the condition is met or the timeout. Use operators for “do something,” sensors for “wait until something is ready.”

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25+ questions across DAGs, Operators, Scheduling, Production, and design — with full answers.

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DAGs & task dependencies
3

How does XCom work and when should you avoid it? Normal

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4

Write a simple DAG with two tasks where task_b runs after task_a. Code

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5

When would you use TaskGroups instead of SubDAGs? Logic

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Operators, sensors & execution
6

What is idempotency and why does it matter for Airflow tasks? Normal

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7

Name common built-in operators (Bash, Python, SQL) and when to use each. Code

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8

How would you pass data between tasks without XCom for large payloads? Logic

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Scheduling & configuration
9

schedule_interval vs timetable (Airflow 2.2+)? Normal

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10

What are Variables and Connections? How do you use them in a DAG? Code

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11

How would you implement dynamic task mapping for 10 different dates? Logic

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Production & best practices
12

Executor types: Sequential, Local, Celery, Kubernetes — when to use which? Normal

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13

How do you test a DAG (syntax and unit tests)? Code

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14

Design a pipeline that loads daily data with backfill and no duplicates. Logic

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