Why DAG design determines pipeline reliability in Apache Airflow - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a DAG with a daily schedule.

Apache Airflow

dag = DAG('example_dag', schedule_interval=[1], start_date=datetime(2024, 1, 1))

Drag options to blanks, or click blank then click option'

A'@daily'

BNone

C'@weekly'

D'@hourly'

Attempts:

3 left

💡 Hint

Common Mistakes

Using '@hourly' runs the DAG every hour, not daily.

Using None disables scheduling.

The schedule_interval set to '@daily' makes the DAG run once every day.

2fill in blank

medium

Complete the code to set task dependencies so task2 runs after task1.

Apache Airflow

task1 >> [1]

Drag options to blanks, or click blank then click option'

Atask3

Btask4

Ctask1

Dtask2

Attempts:

3 left

💡 Hint

Common Mistakes

Setting task1 after itself causes a circular dependency.

Choosing a task not defined in the DAG.

The >> operator sets the order so task2 runs after task1.

3fill in blank

hard

Fix the error in the DAG definition by completing the missing argument.

Apache Airflow

dag = DAG('my_dag', schedule_interval='@daily', [1]=datetime(2024, 1, 1))

Drag options to blanks, or click blank then click option'

Aend_date

Bstart_date

Cexecution_date

Drun_date

Attempts:

3 left

💡 Hint

Common Mistakes

Using end_date instead of start_date.

Using non-existent arguments like execution_date.

The start_date argument tells Airflow when to start scheduling the DAG.

4fill in blank

hard

Fill both blanks to create a task that runs a Python function using the correct operator and DAG.

Apache Airflow

task = [1](task_id='print_hello', python_callable=print_hello, dag=[2])

Drag options to blanks, or click blank then click option'

APythonOperator

BBashOperator

Cdag

Dmy_dag

Attempts:

3 left

💡 Hint

Common Mistakes

Using BashOperator for Python functions.

Passing the wrong DAG variable.

PythonOperator runs Python functions, and dag is the DAG instance.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps task IDs to their start dates if the start date is after 2024-01-01.

Apache Airflow

task_start_dates = {task.task_id: task.[1] for task in tasks if task.[2] [3] datetime(2024, 1, 1)}

Drag options to blanks, or click blank then click option'

Astart_date

Bend_date

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using end_date instead of start_date.

Using the wrong comparison operator.

This comprehension collects start_date of tasks where the start_date is after January 1, 2024.