What is Apache Airflow - Interactive Quiz & Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the main Airflow DAG class.

Apache Airflow

from airflow import [1]

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

AOperator

BTask

CDAG

DScheduler

Attempts:

3 left

💡 Hint

Common Mistakes

Importing Task instead of DAG

Using Scheduler which is a component, not a class

The DAG class is the main structure to define workflows in Airflow.

2fill in blank

medium

Complete the code to define a simple Airflow DAG with an ID.

Apache Airflow

dag = DAG(dag_id='[1]')

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

Atask1

Brun_task

Cexecute

Dmy_workflow

Attempts:

3 left

💡 Hint

Common Mistakes

Using function names or task names instead of a workflow ID

The dag_id is a unique identifier for the DAG. 'my_workflow' is a common example name.

3fill in blank

hard

Fix the error in the Airflow task definition by completing the operator name.

Apache Airflow

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

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

APythonOperator

BBashOperator

CEmailOperator

DDummyOperator

Attempts:

3 left

💡 Hint

Common Mistakes

Using BashOperator when Python code is needed

Using DummyOperator which does nothing

PythonOperator runs Python functions as tasks in Airflow.

4fill in blank

hard

Fill both blanks to create a DAG that runs daily starting from 2023-01-01.

Apache Airflow

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

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

A'@daily'

B'@hourly'

Cdatetime(2023, 1, 1)

Ddatetime(2022, 12, 31)

Attempts:

3 left

💡 Hint

Common Mistakes

Using '@hourly' for daily schedule

Wrong start_date causing no runs

'@daily' sets the DAG to run once every day. The start_date must be a datetime object for the first run.

5fill in blank

hard

Fill all three blanks to define a PythonOperator task that calls a function named 'hello'.

Apache Airflow

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

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

A'say_hello'

Bhello

Cdag

D'hello_task'

Attempts:

3 left

💡 Hint

Common Mistakes

Putting function name in quotes

Using wrong task_id string

The task_id is a string identifier. The python_callable is the function to run. The dag is the DAG object.

Practice

1. What is the main purpose of Apache Airflow?

easy

A. To monitor network traffic

B. To store large amounts of data

C. To create user interfaces for websites

D. To automate and schedule tasks in a specific order

Solution

1. Step 1: Understand Apache Airflow's role

   Apache Airflow is designed to automate and schedule tasks, not to store data or create interfaces.

2. Step 2: Identify the correct purpose

   It manages workflows by running tasks in a defined order automatically.

3. Final Answer:

   To automate and schedule tasks in a specific order -> Option D

4. Quick Check:

   Automation and scheduling = A [OK]

Hint: Airflow schedules and runs tasks automatically [OK]

Common Mistakes:

• Confusing Airflow with data storage tools
• Thinking Airflow builds websites
• Assuming Airflow monitors networks

2. Which of the following is the correct way to define a DAG in Apache Airflow using Python?

easy

A. dag = DAG('my_dag', schedule_interval='@daily')

B. dag = DAG('my_dag' schedule_interval='@daily')

C. dag = DAG['my_dag', schedule_interval='@daily']

D. dag = DAG{'my_dag', schedule_interval='@daily'}

Solution

1. Step 1: Check Python syntax for function calls

   Function calls use parentheses with arguments separated by commas.

2. Step 2: Identify correct syntax

   dag = DAG('my_dag', schedule_interval='@daily') uses parentheses and commas correctly; others have missing commas or wrong brackets.

3. Final Answer:

   dag = DAG('my_dag', schedule_interval='@daily') -> Option A

4. Quick Check:

   Function call syntax = A [OK]

Hint: Use parentheses and commas for function arguments [OK]

Common Mistakes:

• Missing commas between arguments
• Using square or curly brackets instead of parentheses
• Incorrect argument separation

3. Given the following Airflow DAG snippet, what will be the order of task execution?

from airflow import DAG
from airflow.operators.dummy import DummyOperator
from datetime import datetime

dag = DAG('example_dag', start_date=datetime(2023,1,1))

t1 = DummyOperator(task_id='task1', dag=dag)
t2 = DummyOperator(task_id='task2', dag=dag)
t3 = DummyOperator(task_id='task3', dag=dag)

t1 >> t2 >> t3

medium

A. All tasks run at the same time

B. task3 runs, then task2, then task1

C. task1 runs, then task2, then task3

D. task2 runs first, then task1, then task3

Solution

1. Step 1: Understand the '>>' operator in Airflow

   The '>>' operator sets task dependencies, meaning left task runs before right task.

2. Step 2: Trace the task order

   t1 >> t2 >> t3 means task1 runs first, then task2, then task3.

3. Final Answer:

   task1 runs, then task2, then task3 -> Option C

4. Quick Check:

   Dependency chain = task1 -> task2 -> task3 [OK]

Hint: The '>>' operator means 'runs before' [OK]

Common Mistakes:

• Thinking tasks run in reverse order
• Assuming tasks run in parallel without dependencies
• Misreading the '>>' operator

4. You wrote this Airflow DAG code but it fails to run:

from airflow import DAG
from airflow.operators.dummy import DummyOperator
from datetime import datetime

dag = DAG('my_dag', start_date=datetime(2023,1,1))

t1 = DummyOperator(task_id='task1')
t2 = DummyOperator(task_id='task2')

t1 >> t2

What is the error?

medium

A. Task IDs must be integers

B. Missing DAG parameter in DummyOperator tasks

C. start_date must be a string

D. Wrong import for DummyOperator

Solution

1. Step 1: Check task definitions

   Tasks need to be linked to a DAG by passing the 'dag' parameter.

2. Step 2: Identify missing 'dag' argument

   t1 and t2 are missing 'dag=dag', so Airflow doesn't know which DAG they belong to.

3. Final Answer:

   Missing DAG parameter in DummyOperator tasks -> Option B

4. Quick Check:

   Tasks need DAG assigned = D [OK]

Hint: Always assign tasks to a DAG with dag=dag [OK]

Common Mistakes:

• Forgetting to pass dag=dag to tasks
• Incorrect imports causing errors
• Wrong data types for start_date or task_id

5. You want to create an Airflow workflow that retries a task up to 3 times if it fails. Which DAG task parameter should you set?

hard

A. retries=3

B. retry_count=3

C. max_retry=3

D. retry_limit=3

Solution

1. Step 1: Recall Airflow retry parameter

   Airflow uses 'retries' to specify how many times to retry a failed task.

2. Step 2: Match parameter name

   The correct parameter is 'retries', not retry_count, max_retry, or retry_limit.

3. Final Answer:

   retries=3 -> Option A

4. Quick Check:

   Retries parameter controls retry count = B [OK]

Hint: Use 'retries' to set retry attempts in Airflow tasks [OK]

Common Mistakes:

• Using incorrect parameter names like retry_count
• Confusing retry count with timeout
• Not setting retries at all