DAG parsing and import errors in Apache Airflow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the DAG class from Airflow.

Apache Airflow

from airflow.[1] import DAG

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

Aoperators

Bhooks

Cmodels

Dutils

Attempts:

3 left

💡 Hint

Common Mistakes

Importing DAG from the wrong module causes import errors during DAG parsing.

The DAG class is located in the airflow.models module, so importing it from there is correct.

2fill in blank

medium

Complete the code to define the DAG's start date using Airflow's datetime utility.

Apache Airflow

from airflow.utils.dates import [1]

start_date = [1](1)

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

Adays_ago

Bdatetime

Ctimedelta

Dtimezone

Attempts:

3 left

💡 Hint

Common Mistakes

Using Python's standard datetime instead of Airflow's utility can cause timezone problems.

The days_ago function is used to set relative start dates in Airflow DAGs, which helps avoid timezone issues.

3fill in blank

hard

Fix the error in the DAG import statement to avoid parsing errors.

Apache Airflow

from airflow.operators.bash import [1]Operator

bash_task = [1]Operator(task_id='print_date', bash_command='date')

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

Abash

BBash

CBashOperator

Dbashoperator

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase or incorrect casing causes import or attribute errors.

The correct class name is BashOperator with uppercase B and O. The import path uses bash module but the class name must be exact.

4fill in blank

hard

Fill both blanks to correctly define a DAG with a daily schedule and a default argument for retries.

Apache Airflow

default_args = {'retries': [1]
dag = DAG('example_dag', default_args=default_args, schedule_interval=[2])

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

A1

B'@daily'

C'@hourly'

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 retries disables retries, which might not be desired.

Using wrong schedule strings causes DAG not to run as expected.

Setting retries to 1 means the task will retry once on failure. The schedule_interval '@daily' runs the DAG every day.

5fill in blank

hard

Fill all three blanks to create a task using PythonOperator with a callable function and assign it to the DAG.

Apache Airflow

def my_task():
    print('Hello Airflow')

from airflow.operators.python import [1]
task = [1](task_id=[2], python_callable=[3], dag=dag)

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

APythonOperator

B'my_task'

Cmy_task

D'print_hello'

Attempts:

3 left

💡 Hint

Common Mistakes

Putting the function name in quotes for python_callable causes errors.

Using incorrect task_id strings or missing dag parameter causes DAG parsing failures.

The PythonOperator runs Python functions. The task_id is a string identifier, here 'print_hello'. The python_callable is the function my_task defined above.