Airflow DAG Authoring Practice Questions - Astronomer Certification Apache Airflow DAG Authoring Exam - Airflow DAG Authoring Practice Questions - Astronomer Certification Apache Airflow DAG Authoring Exam

Question 1

What is the primary advantage of using the @task decorator (TaskFlow API) over the classic PythonOperator?

The @task decorator allows tasks to run on separate Kubernetes pods automatically The @task decorator eliminates boilerplate: return values automatically become XCom, and passing return values between @task functions creates implicit dependencies The @task decorator runs tasks faster because it bypasses the metadata database The @task decorator enables tasks to access global Python variables at parse time

Correct Answer:

The @task decorator eliminates boilerplate: return values automatically become XCom, and passing return values between @task functions creates implicit dependencies

Question 2

In the following TaskFlow API snippet, what is the task dependency created? ```python @dag def my_dag(): data = extract() result = transform(data) load(result) ```

All three tasks run in parallel extract → transform → load, with XCom values passed between them automatically Only extract and load are connected; transform is independent The dependencies must be explicitly set with >> operators as well

Correct Answer:

extract → transform → load, with XCom values passed between them automatically

Question 3

How do you mix a classic BashOperator with a TaskFlow API @task function and create a dependency where the @task runs after the BashOperator?

It is not possible to mix classic operators with TaskFlow API in the same DAG Use the >> operator: bash_task >> taskflow_func() Wrap the BashOperator in a @task decorator to make it compatible Use the depends_on=[bash_task] parameter in the @task decorator

Correct Answer:

Use the >> operator: bash_task >> taskflow_func()

Question 4

What does the @task.branch decorator (TaskFlow API) return, and what effect does it have on downstream tasks?

It returns a boolean; True continues all downstream tasks, False skips them It returns a task_id string (or list of task_ids); only those tasks run, the rest are marked skipped It returns an XCom value that BranchPythonOperator reads to decide the branch It returns a DAG run configuration that changes the schedule

Correct Answer:

It returns a task_id string (or list of task_ids); only those tasks run, the rest are marked skipped

Question 5

A DAG uses BranchPythonOperator to choose between branch_a and branch_b. A final 'notify' task must run regardless of which branch executes. What trigger_rule should 'notify' have?

all_success (default) all_done none_failed_min_one_success none_failed

Correct Answer:

none_failed

Question 1

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Prepare with the Airflow DAG Authoring Practice Questions - Astronomer Certification Apache Airflow DAG Authoring Exam practice quiz. This question bank includes 100 questions covering task, airflow, taskflow, parameter, and decorator. Use it to review important concepts, identify knowledge gaps, and build confidence for the related exam, course, or assessment.

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Airflow DAG Authoring Practice Questions - Astronomer Certification Apache Airflow DAG Authoring Exam

This practice set contains 100 questions from the matching question bank and focuses on task, airflow, taskflow, parameter, and decorator. Work through each question carefully, review the provided solutions, and revisit topics that need more study before your next attempt.

This is an independent study resource intended for practice and review; it is not an official examination or an endorsement by any organization named in the title.

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Airflow DAG Authoring Practice Questions - Astronomer Certification Apache Airflow DAG Authoring Exam

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