Cost optimization for cloud resources in Apache Airflow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the default retry delay to 5 minutes in an Airflow DAG.

Apache Airflow

default_args = {
    'retry_delay': timedelta(minutes=[1])
}

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

A10

B5

C15

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using too short retry delays causing frequent retries and higher costs.

Using too long retry delays delaying task recovery.

The retry delay is set to 5 minutes using timedelta(minutes=5) to optimize task retries and avoid unnecessary costs.

2fill in blank

medium

Complete the code to schedule the DAG to run once every day at midnight.

Apache Airflow

dag = DAG(
    'cost_optimization',
    schedule_interval='[1]',
    default_args=default_args
)

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

A'@hourly'

B'@daily'

C'@weekly'

D'0 0 * * *'

Attempts:

3 left

💡 Hint

Common Mistakes

Using '@hourly' which runs every hour, increasing costs.

Using '@weekly' which runs once a week, missing daily checks.

The cron expression '0 0 * * *' schedules the DAG to run daily at midnight, which helps in regular cost monitoring.

3fill in blank

hard

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

Apache Airflow

task1 = [1](
    task_id='check_unused_resources',
    bash_command='echo Checking unused cloud resources',
    dag=dag
)

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

ADummyOperator

BPythonOperator

CBashOperator

DEmailOperator

Attempts:

3 left

💡 Hint

Common Mistakes

Using PythonOperator without a Python callable.

Using DummyOperator which does nothing.

The BashOperator runs bash commands, which fits the task of echoing a message for checking resources.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps resource names to their cost if the cost is greater than 0.

Apache Airflow

costs = {resource: [1] for resource, cost in resource_costs.items() if cost [2] 0}

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

Acost

B<

C>

Dresource

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' causing incorrect filtering.

Mapping resource to resource name instead of cost.

The comprehension maps each resource to its cost only if the cost is greater than zero, helping to focus on resources that incur costs.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of resources with cost above 100, using uppercase resource names as keys.

Apache Airflow

filtered_costs = { [1]: [2] for [3], cost in resource_costs.items() if cost > 100}

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

Aresource.upper()

Bcost

Cresource

Dcosts

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'costs' as a variable instead of 'cost'.

Not converting resource names to uppercase.

This comprehension creates a dictionary with uppercase resource names as keys and their costs as values, filtering only those with costs above 100 to optimize expenses.

Practice

1. What is the main goal of cost optimization for cloud resources in Airflow?

easy

A. To increase the number of cloud resources used

B. To run all tasks continuously without stopping

C. To use cloud resources only when needed to save money

D. To avoid monitoring cloud resource usage

Solution

1. Step 1: Understand cost optimization meaning

   Cost optimization means using resources efficiently to reduce expenses.

2. Step 2: Relate to Airflow usage

   Airflow helps automate resource usage only when needed, saving money.

3. Final Answer:

   To use cloud resources only when needed to save money -> Option C

4. Quick Check:

   Cost optimization = Use resources only when needed [OK]

Hint: Cost optimization means saving money by using resources smartly [OK]

Common Mistakes:

• Thinking more resources always mean better performance
• Ignoring the need to stop unused resources
• Believing monitoring is unnecessary

2. Which Airflow feature helps automate starting and stopping cloud resources on a schedule?

easy

A. DAG scheduling

B. Manual triggering only

C. Static resource allocation

D. Ignoring resource states

Solution

1. Step 1: Identify Airflow automation features

   DAG scheduling allows tasks to run automatically at set times.

2. Step 2: Connect scheduling to resource control

   Scheduling can start or stop cloud resources based on time, saving costs.

3. Final Answer:

   DAG scheduling -> Option A

4. Quick Check:

   DAG scheduling = Automate resource start/stop [OK]

Hint: Airflow DAGs run tasks on schedule to control resources [OK]

Common Mistakes:

• Thinking manual triggers automate resource control
• Confusing static allocation with automation
• Ignoring the role of scheduling in cost saving

3. Given this Airflow DAG snippet, what happens to cloud resources?

with DAG('resource_control', schedule_interval='0 8 * * *') as dag:
    start_instance = BashOperator(
        task_id='start_instance',
        bash_command='cloud-cli start instance'
    )
    stop_instance = BashOperator(
        task_id='stop_instance',
        bash_command='cloud-cli stop instance',
        trigger_rule='all_done'
    )

start_instance >> stop_instance

medium

A. Instance stops before it starts

B. Instance starts at 8 AM and stops immediately after

C. Instance starts but never stops

D. Instance stops at 8 AM and starts later

Solution

1. Step 1: Analyze DAG schedule and tasks

   DAG runs daily at 8 AM, starting the instance first.

2. Step 2: Understand task order and trigger rules

   stop_instance runs after start_instance completes, stopping the instance.

3. Final Answer:

   Instance starts at 8 AM and stops immediately after -> Option B

4. Quick Check:

   Task order start >> stop means start then stop [OK]

Hint: Task order with >> shows start then stop sequence [OK]

Common Mistakes:

• Assuming stop runs before start
• Ignoring trigger_rule effects
• Thinking instance stays running all day

4. You wrote this Airflow task to stop a cloud instance:

stop_instance = BashOperator(
    task_id='stop_instance',
    bash_command='cloud-cli stop instance',
    trigger_rule='all_done'
)

Why does this code cause an error?

medium

A. task_id must be 'stop' not 'stop_instance'

B. trigger_rule is not a valid parameter

C. BashOperator cannot run cloud-cli commands

D. Missing comma between bash_command and trigger_rule

Solution

1. Step 1: Check syntax of BashOperator parameters

   Parameters must be separated by commas; here comma is missing after bash_command.

2. Step 2: Identify cause of syntax error

   Missing comma causes Python syntax error, preventing DAG parsing.

3. Final Answer:

   Missing comma between bash_command and trigger_rule -> Option D

4. Quick Check:

   Comma missing = Syntax error [OK]

Hint: Check commas between parameters in Python code [OK]

Common Mistakes:

• Thinking trigger_rule is invalid parameter
• Assuming BashOperator can't run shell commands
• Believing task_id naming causes error

5. You want to optimize cloud costs by running an Airflow DAG that starts instances only on weekdays at 9 AM and stops them at 6 PM. Which schedule_interval and task setup is best?

hard

A. schedule_interval='0 9 * * 1-5' to start, and a separate DAG with '0 18 * * 1-5' to stop

B. schedule_interval='@daily' for both start and stop tasks in one DAG

C. schedule_interval='0 9 * * *' to start and stop in same DAG immediately

D. No schedule_interval, run tasks manually when needed

Solution

1. Step 1: Choose schedule for weekdays only

   '0 9 * * 1-5' runs at 9 AM Monday to Friday, matching requirement.

2. Step 2: Separate start and stop times

   Stopping at 6 PM requires a different schedule '0 18 * * 1-5', best in separate DAG for clarity.

3. Final Answer:

   schedule_interval='0 9 * * 1-5' to start, and a separate DAG with '0 18 * * 1-5' to stop -> Option A

4. Quick Check:

   Separate schedules for start and stop on weekdays [OK]

Hint: Use cron for weekdays and separate DAGs for start/stop [OK]

Common Mistakes:

• Using '@daily' runs every day, not weekdays only
• Starting and stopping immediately in one DAG
• Relying on manual runs instead of automation