TaskFlow API for cleaner XCom in Apache Airflow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a task using the TaskFlow API decorator.

Apache Airflow

@dag
 def my_dag():
     @[1]
     def extract():
         return 'data'

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

Apython

Boperator

Ctask

Ddag

Attempts:

3 left

💡 Hint

Common Mistakes

Using @dag instead of @task for the function.

Using @operator which is not a valid decorator here.

The @task decorator defines a task in the TaskFlow API.

2fill in blank

medium

Complete the code to pass data between tasks using the TaskFlow API.

Apache Airflow

@dag
 def my_dag():
     @task
     def extract():
         return 'data'

     @task
     def process(data):
         print(data)

     processed = process([1])

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

Aextract()

Bextract

Cprocess()

Dprocess

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the function object extract instead of calling it.

Calling the wrong function.

Calling extract() runs the task and passes its output to process.

3fill in blank

hard

Fix the error in the task definition to correctly return a value for XCom.

Apache Airflow

@dag
 def my_dag():
     @task
     def extract():
         [1] 'data'

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

Areturn

Bprint

Cyield

Dpass

Attempts:

3 left

💡 Hint

Common Mistakes

Using print instead of return, which does not push data to XCom.

Using yield which is for generators, not needed here.

Tasks must return values to push them to XCom for downstream tasks.

4fill in blank

hard

Fill both blanks to create a DAG that runs the tasks in order using the TaskFlow API.

Apache Airflow

@dag
 def my_dag():
     @task
     def extract():
         return 'data'

     @task
     def process(data):
         print(data)

     data = extract()
     [1] = process([2])

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

Aresult

Bdata

Cextract()

Dprocess()

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the function call extract() again instead of the variable.

Not assigning the output of process to a variable.

Assign the output of process to result and pass data as input.

5fill in blank

hard

Fill all three blanks to define a DAG with three tasks where data flows from extract to transform to load.

Apache Airflow

@dag
 def my_dag():
     @task
     def extract():
         return 'raw data'

     @task
     def transform(data):
         return data.upper()

     @task
     def load(data):
         print(f'Loading: {data}')

     raw = [1]()
     transformed = [2]([3])
     load(transformed)

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

Aextract

Btransform

Craw

Dload

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the wrong variable to transform.

Calling load with raw instead of transformed data.

Call extract() to get raw data, pass it to transform, then load the transformed data.

Practice

1. What is the main benefit of using the TaskFlow API in Airflow for passing data between tasks?

easy

A. It automatically handles data sharing between tasks using return values.

B. It requires manual XCom push and pull commands for data sharing.

C. It disables task dependencies to speed up execution.

D. It only works with BashOperator tasks.

Solution

1. Step 1: Understand TaskFlow API purpose

   The TaskFlow API simplifies task communication by using Python functions and decorators.

2. Step 2: Recognize automatic XCom handling

   Return values from decorated tasks are automatically shared with downstream tasks via XCom without manual commands.

3. Final Answer:

   It automatically handles data sharing between tasks using return values. -> Option A

4. Quick Check:

   TaskFlow API = automatic XCom via return [OK]

Hint: TaskFlow API shares data automatically via return values [OK]

Common Mistakes:

• Thinking manual XCom push/pull is needed
• Believing it disables dependencies
• Assuming it only works with BashOperator

2. Which of the following is the correct way to define a task using the TaskFlow API in Airflow?

easy

A. def my_task(): pass

B. @dag
def my_task(): pass

C. @task
def my_task(): pass

D. my_task = PythonOperator(task_id='my_task')

Solution

1. Step 1: Identify TaskFlow API syntax

   Tasks are defined as Python functions decorated with @task to enable TaskFlow features.

2. Step 2: Compare options

   @task def my_task(): pass uses @task decorator correctly; others either lack decorator or use wrong decorators/operators.

3. Final Answer:

   @task\ndef my_task(): pass -> Option C

4. Quick Check:

   TaskFlow task = function + @task decorator [OK]

Hint: Use @task decorator above function to define TaskFlow task [OK]

Common Mistakes:

• Omitting @task decorator
• Confusing @dag with @task
• Using PythonOperator instead of TaskFlow

3. Given the following code snippet using TaskFlow API, what will be printed?

@task
def add(x, y):
    return x + y

@task
def print_result(value):
    print(f'Result is {value}')

with DAG('example_dag', start_date=datetime(2023,1,1)) as dag:
    sum_value = add(3, 4)
    print_result(sum_value)

medium

A. No output printed

B. Result is

C. SyntaxError

D. Result is 7

Solution

1. Step 1: Understand TaskFlow return and XCom

   The add task returns 7, which is passed automatically to print_result.

2. Step 2: Trace print statement execution

   print_result prints the string with the value 7, so output is 'Result is 7'.

3. Final Answer:

   Result is 7 -> Option D

4. Quick Check:

   Returned value passed and printed = 'Result is 7' [OK]

Hint: Returned values flow automatically and print as expected [OK]

Common Mistakes:

• Expecting raw XCom object printed
• Confusing syntax error with correct code
• Thinking no output prints without manual XCom pull

4. What is wrong with this TaskFlow API code snippet?

@task
def multiply(x, y):
    print(x * y)

with DAG('dag', start_date=datetime(2023,1,1)) as dag:
    result = multiply(2, 5)
    print(f'Result is {result}')

medium

A. The DAG is missing the schedule_interval parameter.

B. The multiply task should return the result instead of printing it.

C. The print statement inside the DAG is invalid syntax.

D. The @task decorator is missing parentheses.

Solution

1. Step 1: Identify how TaskFlow passes data

   TaskFlow passes data via return values, not print statements inside tasks.

2. Step 2: Analyze code behavior

   Since multiply only prints and returns None, result is None, so printing it shows 'Result is None'.

3. Final Answer:

   The multiply task should return the result instead of printing it. -> Option B

4. Quick Check:

   Return value needed for XCom, not print [OK]

Hint: Return values to share data, not print inside tasks [OK]

Common Mistakes:

• Thinking print shares data between tasks
• Confusing DAG parameters with task errors
• Believing @task needs parentheses always

5. You want to create a DAG using TaskFlow API where task A returns a list of numbers, and task B receives this list and returns the sum. Which code snippet correctly implements this pattern?

hard

A. @task
def task_a():
return [1, 2, 3]

@task
def task_b(numbers):
return sum(numbers)

with DAG('dag', start_date=datetime(2023,1,1)) as dag:
nums = task_a()
total = task_b(nums)

B. @task
def task_a():
print([1, 2, 3])

@task
def task_b(numbers):
print(sum(numbers))

with DAG('dag', start_date=datetime(2023,1,1)) as dag:
nums = task_a()
total = task_b(nums)

C. @task
def task_a():
return '1,2,3'

@task
def task_b(numbers):
return sum(numbers)

with DAG('dag', start_date=datetime(2023,1,1)) as dag:
nums = task_a()
total = task_b(nums)

D. @task
def task_a():
return [1, 2, 3]

@task
def task_b(numbers):
return sum(numbers)

with DAG('dag', start_date=datetime(2023,1,1)) as dag:
total = task_b(task_a())

Solution

1. Step 1: Check task A returns a list

   @task
   def task_a():
       return [1, 2, 3]
   
   @task
   def task_b(numbers):
       return sum(numbers)
   
   with DAG('dag', start_date=datetime(2023,1,1)) as dag:
       nums = task_a()
       total = task_b(nums)

   returns a list [1, 2, 3] correctly;

   @task
   def task_a():
       print([1, 2, 3])
   
   @task
   def task_b(numbers):
       print(sum(numbers))
   
   with DAG('dag', start_date=datetime(2023,1,1)) as dag:
       nums = task_a()
       total = task_b(nums)

   prints instead of returning;

   @task
   def task_a():
       return '1,2,3'
   
   @task
   def task_b(numbers):
       return sum(numbers)
   
   with DAG('dag', start_date=datetime(2023,1,1)) as dag:
       nums = task_a()
       total = task_b(nums)

   returns a string, not list.

2. Step 2: Check task B sums the list argument

   @task
   def task_a():
       return [1, 2, 3]
   
   @task
   def task_b(numbers):
       return sum(numbers)
   
   with DAG('dag', start_date=datetime(2023,1,1)) as dag:
       nums = task_a()
       total = task_b(nums)

   passes the list to task B which sums it correctly;

   @task
   def task_a():
       return [1, 2, 3]
   
   @task
   def task_b(numbers):
       return sum(numbers)
   
   with DAG('dag', start_date=datetime(2023,1,1)) as dag:
       total = task_b(task_a())

   skips assigning intermediate variable but is valid but less clear.

3. Final Answer:

   Option A correctly returns a list and passes it to sum in task B. -> Option A

4. Quick Check:

   Return list and pass it to sum task [OK]

Hint: Return data structures, pass as args, sum in next task [OK]

Common Mistakes:

• Using print instead of return in tasks
• Passing string instead of list to sum
• Ignoring task dependencies or variable assignment