Creating a basic DAG file in Apache Airflow - Interactive 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'

Amodels

Bsensors

Chooks

Doperators

Attempts:

3 left

💡 Hint

Common Mistakes

Importing DAG from operators or hooks instead of models.

The DAG class is imported from the models module in Airflow.

2fill in blank

medium

Complete the code to set the DAG's start date using datetime.

Apache Airflow

from datetime import datetime
start_date = datetime([1])

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

A'2023-01-01'

B1, 1, 2023

Cdatetime(2023, 1, 1)

D2023, 1, 1

Attempts:

3 left

💡 Hint

Common Mistakes

Passing arguments in wrong order or as strings.

The datetime constructor takes year, month, day as integers in that order.

3fill in blank

hard

Fix the error in the DAG definition by completing the default_args dictionary key for retries.

Apache Airflow

default_args = {
    'owner': 'airflow',
    'start_date': start_date,
    'retries': [1]
}

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

A3

B'3'

CTrue

D'True'

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes around the number or using boolean values.

The retries value must be an integer, not a string or boolean.

4fill in blank

hard

Fill both blanks to create a DAG object with id 'example_dag' and schedule interval '@daily'.

Apache Airflow

dag = DAG(
    dag_id=[1],
    schedule_interval=[2]
)

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

A'example_dag'

B'@hourly'

C'@daily'

D'example'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong strings for dag_id or schedule_interval.

The dag_id should be the string 'example_dag' and the schedule_interval '@daily' for daily runs.

5fill in blank

hard

Fill all three blanks to define a PythonOperator task named 'print_date' that calls the 'print_date' function and uses the DAG object.

Apache Airflow

from airflow.operators.python import PythonOperator

def print_date():
    print('Current date')

print_date_task = PythonOperator(
    task_id=[1],
    python_callable=[2],
    dag=[3]
)

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

A'print_date'

Bprint_date

Cdag

D'print_date_task'

Attempts:

3 left

💡 Hint

Common Mistakes

Putting quotes around the function name or wrong task_id string.

The task_id is the string 'print_date', the python_callable is the function print_date, and the dag is the DAG object named dag.

Practice

1. What is the main purpose of a DAG file in Apache Airflow?

easy

A. To configure the Airflow webserver settings

B. To store logs generated by tasks

C. To install Python packages required by tasks

D. To define tasks and their schedule for workflow automation

Solution

1. Step 1: Understand what a DAG file represents

   A DAG file in Airflow is a Python script that defines a Directed Acyclic Graph, which represents tasks and their dependencies.

2. Step 2: Identify the role of the DAG file

   The DAG file schedules and organizes tasks to automate workflows, not for logging, configuration, or package installation.

3. Final Answer:

   To define tasks and their schedule for workflow automation -> Option D

4. Quick Check:

   DAG file purpose = Define tasks and schedule [OK]

Hint: DAG files define workflows, not configs or logs [OK]

Common Mistakes:

• Confusing DAG file with Airflow config files
• Thinking DAG files store logs
• Assuming DAG files install packages

2. Which of the following is the correct way to import the DAG class in an Airflow DAG file?

easy

A. import DAG from airflow.models

B. import airflow.DAG

C. from airflow.models import DAG

D. from airflow import DAG

Solution

1. Step 1: Recall the correct import syntax in Python

   Python imports use 'from module import class' syntax for specific classes.

2. Step 2: Match with Airflow's module structure

   The recommended import used in official Airflow documentation is 'from airflow.models import DAG'.

3. Final Answer:

   from airflow.models import DAG -> Option C

4. Quick Check:

   Correct import syntax = from airflow.models import DAG [OK]

Hint: Use 'from airflow.models import DAG' to import DAG class [OK]

Common Mistakes:

• Using incorrect import syntax
• Trying to import DAG directly from airflow
• Using invalid Python import statements

3. Given the following DAG snippet, what will be the task_id of the BashOperator task?

from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime

default_args = {'start_date': datetime(2024, 1, 1)}
with DAG('example_dag', default_args=default_args, schedule_interval='@daily') as dag:
    task = BashOperator(task_id='print_date', bash_command='date')

medium

A. print_date

B. task

C. example_dag

D. bash_command

Solution

1. Step 1: Identify the task_id parameter in BashOperator

   The BashOperator is created with task_id='print_date', which names the task.

2. Step 2: Understand task_id usage

   task_id is the unique identifier for the task in the DAG, so the task's id is 'print_date'.

3. Final Answer:

   print_date -> Option A

4. Quick Check:

   task_id value = print_date [OK]

Hint: Look for task_id argument in operator constructor [OK]

Common Mistakes:

• Confusing variable name with task_id
• Using DAG id as task_id
• Mistaking bash_command as task_id

4. Identify the error in this DAG file snippet:

from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime

with DAG('my_dag', start_date=datetime(2024, 1, 1), schedule_interval='@daily') as dag:
    task1 = BashOperator(task_id='task1', bash_command='echo Hello')
    task2 = BashOperator(task_id='task2', bash_command='echo World')
    task1 >> task2

print('DAG created')

medium

A. No error, DAG is correctly defined

B. Missing default_args dictionary for start_date

C. Incorrect import of BashOperator

D. Using print statement inside DAG context

Solution

1. Step 1: Check imports and DAG definition

   Imports are correct, DAG is created with start_date and schedule_interval directly in constructor.

2. Step 2: Verify task creation and dependencies

   Tasks are created with valid task_ids and bash commands, and dependency is set with 'task1 >> task2'.

3. Step 3: Confirm print statement usage

   Print outside DAG context is allowed and does not affect DAG creation.

4. Final Answer:

   No error, DAG is correctly defined -> Option A

5. Quick Check:

   DAG syntax correct = No error [OK]

Hint: Check if DAG and tasks follow Airflow syntax rules [OK]

Common Mistakes:

• Assuming default_args is mandatory
• Thinking print inside DAG causes error
• Confusing import paths for operators

5. You want to create a DAG that runs a task every Monday at 7 AM starting from March 1, 2024. Which schedule_interval and start_date combination is correct in the DAG definition?

hard

A. schedule_interval='@weekly', start_date=datetime(2024, 3, 4)

B. schedule_interval='0 7 * * 1', start_date=datetime(2024, 3, 1)

C. schedule_interval='@daily', start_date=datetime(2024, 3, 1, 7, 0)

D. schedule_interval='0 7 * * 0', start_date=datetime(2024, 3, 1)

Solution

1. Step 1: Determine the cron expression for Monday 7 AM

   Cron '0 7 * * 1' runs every Monday at 7:00 AM (1=Monday).

2. Step 2: Validate start_date

   March 1, 2024 (Friday) is before the first Monday (March 4), so first run on March 4, 7 AM.

3. Step 3: Eliminate other options

   A: '@weekly'='0 0 * * 0' (Sunday midnight), wrong day/time. C: daily. D: Sunday (0).

4. Final Answer:

   schedule_interval='0 7 * * 1', start_date=datetime(2024, 3, 1) -> Option B

5. Quick Check:

   Monday 7AM cron = '0 7 * * 1' [OK]

Hint: Use cron '0 7 * * 1' for Mon 7AM [OK]

Common Mistakes:

• Setting start_date on wrong weekday
• Confusing cron day numbers (0=Sunday, 1=Monday)
• Using daily schedule for weekly task