Airflow Fundamentals Certification Preparation Questions

This document contains preparation questions for the Airflow Fundamentals Certification, covering key concepts, components, and configurations in Apache Airflow. Each question includes the correct answer(s) marked with a +.

Question 1

Which of the following are most likely the main requirements for running Airflow for production in a multi-team environment? (Select all that apply)

Multi-tenancy
Scalability
Low cost
High availability

Question 2

Which of the following is the most likely way a solo developer will run Airflow for the purposes of experimentation and learning?

On a local machine or a free virtual machine (VM)
On a paid cloud virtual machine (VM) service (e.g., EC2)
On a multi-deployment cloud solution (e.g., EKS)
On a highly secure on-premises architecture

Question 3

Which of the following are the most likely ways Airflow will be run in production by a single team? (Select all that apply)

On a container cloud offering (e.g., EKS)
On a fully-managed Airflow service (e.g., Astro)
On cloud virtual machines (e.g., EC2)
On a single developer’s machine

Question 4

Which of the following is the most likely way a developer or team is going to run Airflow if they have to adhere to strict security or regulatory guidelines?

On a cloud software
On-premise
On a single developer’s machine

Question 5

Which of the following are most likely the main requirements for running Airflow for production as a single developer? (Select all that apply)

High levels of scalability
Low infrastructure overhead
Low cost
Multi-tenancy

Question 6

What is a task in Airflow?

A configuration file for how Airflow is set up on a machine
The entire data pipeline
The direction the data pipeline executes
A single unit of work in a DAG

Question 7

Assume you are building a DAG and want to run a Python script. What type of Operator would you use to accomplish this goal?

Sensor Operator
Transfer Operator
Action Operator
There are no Operators that allow a DAG to run a Python script

Question 8

Fill in the blank: In Airflow, an Operator is the specific work a _____ does.

Answer: ______

Question 9

Assume you are building a DAG and want to move data from one system or data source to another. What type of Operator would you use to accomplish this goal?

Transfer Operator
Sensor Operator
There are no Operators that allow a DAG to move data from one system or data source to another
Action Operator

Question 10

Assume you are building a DAG and want to wait for a file to load before executing the next task. What type of Operator would you use to accomplish this goal?

Action Operator
There are no Operators that allow a DAG to wait for something to load
Sensor Operator
Transfer Operator

Question 11

Which of the following are characteristics of an Airflow DAG? (Select all that apply)

Question 12

Which of the following scenarios would be a valid use-case for Airflow? (Select all that apply)

Periodic analysis on sensor data from manufacturing equipment to forecast potential failures and schedule maintenance automatically +
As a dedicated system for processing real-time, high-volume data streams for a stock trading application
A healthcare application that schedules email reminders for patients for their appointments
An AI chatbot that gives tips and solutions based on customer errors and actions

Question 13

The benefits of using a modern data orchestration tool like Airflow is because it offers the ability to... (Select all that apply)

Integrate with hundreds of external systems/software
Schedule data based on events in addition to time
Author pipelines as configuration files
Use a rich feature set with features like built-in observability
Store, secure, and preserve large quantities of data permanently

Question 14

Which of the following best describes the role of the scheduler in Airflow?

To define connections to external systems (e.g., Data Warehouses)
To store metadata about the state of tasks
To execute the instructions defined in tasks
To determine which tasks need to be executed and when

Question 15

Which Airflow component is in charge of executing the logic of a DAG's tasks?

The scheduler
The worker
The metadata database
The executor

Question 16

Which of the following best describes the role of an executor in Airflow?

To determine how and where tasks are executed
To visualize the pipeline runs
To execute the instructions of tasks
To store tasks ready to be executed

Question 17

After creating your DAG in a Python file, which action do you need to take in order for Airflow to start the process of detecting and running your DAG?

Launch the API server
Restart Airflow and make sure the DAG is visible on the UI
Wait for the DAG File Processor to pick up the tasks defined in the DAG
Put the Python file in the dags directory

Question 18

By default, how long can it take for the Airflow DAG File Processor to detect a new DAG file in the dags directory?

5 minutes
1 minute
30 seconds
5 seconds

Question 19

Do the workers have direct access to the Airflow metadata database?

No
Yes

Question 20

In which part of a DAG file would you specify how often you want to trigger the DAG?

In the DAG's dependencies
In the import statements
In the DAG's tasks
In the DAG object definition

Airflow Fundamentals Certification Preparation Questions (21-38)

Question 21

Assume a DAG has 3 tasks: task_extract, task_transform, and task_load. Which of the following represents the dependencies in a DAG that needs to meet the following conditions: task_transform is downstream of task_extract, task_load is downstream of task_transform?

task_extract << task_load << task_transform
task_extract >> task_transform >> task_load
task_extract >> task_load >> task_transform

Question 22

Which of the following Airflow components does the metadata database communicate with?

The queue
The scheduler
The API server
The worker(s)

Question 23

What Astro CLI command will start running an Airflow project?

Answer: astro dev start

Question 24

What is the purpose of the airflow_settings.yaml file in a new Airflow project generated by the Astro CLI?

For storing Python files corresponding to data pipelines
For specifying additional Python packages to install, along with their versions, to extend the functionality of the Airflow environment
For storing configurations such as connections and variables to prevent loss when recreating the local development environment
For configuring Airflow instances via environment variables

Question 25

What Astro CLI command will generate a new Airflow project?

Answer: astro dev init

Question 26

Which of the following are benefits of using the Astro CLI for local development? (Select all that apply)

Its ability to dynamically generate new DAGs using AI
A variety of useful commands for managing an Airflow instance
Its ability to generate a standard project directory
To provide a streamlined experience for installing Airflow with limited prerequisites

Question 27

What is the purpose of the include directory in a new Airflow project generated by the Astro CLI?

For storing configurations such as connections and variables to prevent loss when recreating the local development environment
For configuring Airflow instances via environment variables
For storing files like SQL queries, bash scripts, or Python functions needed in data pipelines to keep them clean and organized
For the customization of the Airflow instance by adding new operators or modifying the UI

Question 28

What's the best view to use to visualize your assets?

Asset View
DAGs View
Graph View

Question 29

What's the best view to check the historical states of the DAG Runs and Task Instances for a given DAG?

Grid View
Graph View
DAGs View

Question 30

What's the best view to get overall metrics of your Airflow instance?

The Home View
Code View
Asset View

Question 31

What's the best view to know if code updates have been applied?

Home View
Code View
Graph View

Question 32

What does the last column on the DAG View show?

DAG run states of current and previous DAG Runs for a given DAG
Task's states of the current or latest DAG Run for a given DAG

Question 33

What's the role of the start date?

Define when the DAG starts being scheduled
Define the trigger interval
Avoid running past non-triggered DAG Runs

Question 34

What happens if you don't define a start date?

Nothing, it's optional
That raises an error

Question 35

What's the role of tags?

They allow better organizing DAGs
They allow filtering DAGs
They prevent from running DAGs that do not belong to the current user

Question 36

How can you avoid assigning the dag object to every task you create?

with DAG(...)
dag = ...
@dag(...)
You can't. You must assign the dag object to every task

Question 37

What happens when two DAGs share the same DAG id?

The two DAGs appear on the UI
You get an error
One DAG randomly shows up on the UI

Question 38

Does task_a >> task_b >> task_c is equivalent to task_c << task_b << task_a?

Yes
No

Question 39

If a DAG runs every day at midnight with a start_date=datetime(2025, 1, 1). When will the 3rd DAG run be triggered?

2025-01-01 00:00
2025-01-02 00:00
2025-01-03 00:00
2025-01-04 00:00

Question 40

If a DAG with a "@daily" schedule is triggered at 00:00 on 2025-02-02. What's the logical date value for this DAG Run?

2025-02-01 00:00
2025-02-02 00:00
2025-02-03 00:00

Airflow Fundamentals Certification Preparation Questions (41-60)

Question 41

If a DAG with a "@daily" schedule is triggered at 00:00 on 2025-02-02. What's the data_interval_end value for this DAG Run (Assuming data intervals are used)?

2025-02-01
2025-02-02
2025-02-03
2025-02-04

Question 42

With catchup=False, what happens when you run your DAG for the first time, and it has a start_date defined as 30 days ago?

Nothing
The latest non-triggered DAG Run is triggered
All non-triggered DAG Runs get triggered

Question 43

logical_date = data_interval_start = data_interval_end by default?

Yes
No

Question 44

Select the 4 factors that define the uniqueness of an XCOM

Question 45

Is it possible to push an XCOM without explicitly specifying a key?

Yes
No

Question 46

An XCOM is pushed into...

The scheduler
The worker
The webserver
The database

Question 47

With Postgres, can you share 2Gb of data between 2 tasks with an XCOM?

Yes
No

Question 48

How does the Scheduler know which XCOM to choose for a given DAG Run when multiple XCOMs have the same key, dag_id, and task_id?

It selects one XCOM randomly
It selects the XCOM based on the logical date
You can't have multiple XCOMs with the same key, dag_id, and task_id

Question 49

This DAG doesn’t show up on the UI. Why?

from airflow.decorators import dag,task
from pendulum import datetime
@dag(
    'test_dag',
    start_date = datetime(2023,3,1)
)
def test_dag():
    @task
    def test_task():
        return ' Hello World'
    test_task()

The schedule parameter is missing
The start_date is in the past
test_dag() is not called

Question 50

On manually triggering this DAG you don't see any task execution. Why?

from airflow.decorators import dag,task
from pendulum import datetime
@dag(
    'test_dag',
    start_date = datetime(3030,3,1)
)
def test_dag():
    @task
    def test_task():
        return 'Hello World'
    test_task()

test_dag()

There is no end_date
The start_date is in the future
Because there is only one task and no proper pipeline

Question 51

How many running DAG runs will you get as soon as you unpause this DAG?

from airflow.decorators import dag,task
from pendulum import datetime
@dag(
    'test_dag',
    start_date = datetime(2023,1,1),
    schedule = '@daily',
    catchup = False
)
def test_dag():
    @task
    def my_task():
        return 'Hello World'
    my_task()
test_dag()

0
1
16
32

Question 52

You just finished writing your DAG and saved the file in the dags folder. How long will it take to appear on the UI?

By default it may take up to 5 minutes or more.
It will be added instantly
It may take up to 30 seconds.

Question 53

Is it possible to run tasks that are dependent on different versions of Python in the same DAG?

Yes
No

Question 54

You can't find the connection type Amazon Web Services. What should you do?

Install the apache-airflow-providers-amazon
Install boto3
Use the http connection type

Question 55

If the file never arrives in the S3 bucket. When will the S3KeySensor time out?

In 24 hours
In 7 days
Never

Question 56

Does the Sensor instantly detect the file when it arrives in the bucket?

Yes
No, it depends on the poke_interval

Question 57

Manually trigger the DAG first_dag, wait for having 3 tasks running then trigger the DAG second_dag. What's the task's state of runme in second_dag?

running
queued
scheduled

Question 58

How many worker slots are used?

3
2
1

Question 59

Turn off the schedule of both DAGs and delete the two DAG runs by going to Browse and DAG Runs. Now, open the file first_dag.py, add a new parameter mode='reschedule' in partial(), save the file, and manually trigger first_dag. What is the status of the three tasks waiting_for_files?

scheduled
queued
running
up_for_reschedule

Question 60

How many worker slots are running?

1
0
3

Airflow Fundamentals Certification Preparation Questions (61-79)

Question 61

Manually trigger the DAG second_dag. Does the task runme run?

Yes
No

Question 62

Create a new empty file data_1.csv in the folder include. Go back to the Airflow UI and wait a minute. What do you see?

Nothing
3 tasks are still up_for_reschedule
1 sensor has been successfully executed

Question 63

A Sensor can be used for (Choose all that apply):

waiting for files to appear in an S3 bucket
waiting for a task in another DAG to complete
waiting for data to be present in a SQL table
waiting for a specified date and time

Question 64

You have a sensor that waits for a file to arrive in an S3 bucket. Your DAG runs every 10 mins, and it takes 8 mins to complete. What is the most appropriate timeout duration for the sensor? (in seconds)

60 * 60 * 24 * 7
60 * 60
60 * 5

Question 65

What mode doesn't take a worker slot while a Sensor waits?

poke
reschedule
none

Question 66

What Sensor(s) can be used to apply logic conditions? (Choose all that apply)

PythonSensor
@task.sensor
S3KeySensor

Question 67

What parameter can be useful to check for data to be present in a database without putting too much workload on each poke?

timeout
exponential_backoff
mode

Question 68

What is the purpose of airflow db init and when might you use it?

Creates a new user in Airflow
Generates a report of all DAGs currently in your Airflow environment
Starts the Airflow web server
Initializes the Airflow metadata database, typically used when setting up Airflow for the first time

Question 69

What is the use case for airflow config get-value and how might you use it to troubleshoot issues?

Lists all of the DAG files that have failed to import
Retrieves the value of a specific configuration option, useful for verifying that configuration settings are correctly set
Verifies the validity of the Airflow metadata database schema
Reserializes a DAG file to ensure that it can be properly loaded by Airflow

Question 70

You've recently upgraded your Airflow installation, but now your DAGs are not showing up in the UI. Which commands could you use to identify any import/parsing errors that might be preventing the DAG from being loaded?

airflow dags list-import-errors
airflow dags show
airflow dags report
airflow dags backfill

Question 71

Why would you use Airflow's backfill functionality?

To test DAGs before deploying them to a production environment
To reschedule a DAG to run at a different frequency
To fill in missing historical data that was not previously captured
To remove all past DAG runs and start fresh with a new schedule

Question 72

What does the airflow db check command do?

Upgrades the Airflow database schema to the latest version
Checks the connection to the Airflow database
Removes all data from the Airflow database
Initializes the Airflow database

Question 73

Can_you create two connections with identical IDs?

Yes
No

Question 74

In Airflow, what is a connection?

A connection is a way to define the order in which tasks in a DAG should be executed
A connection is a type of sensor that waits for a specific condition to be met before proceeding with a task
A connection is a way to store and reuse credentials or configuration settings for external systems that are used in Airflow tasks

Question 75

What is the purpose of defining a connection in Airflow?

The purpose of defining a connection in Airflow is to avoid hard-coding sensitive information like passwords or API keys into Airflow DAGs, and to allow for easy management and reuse of connection information across multiple DAGs
The purpose of defining a connection in Airflow is to store output data from tasks for later analysis
The purpose of defining a connection in Airflow is to specify the order in which tasks should be executed within a DAG

Question 76

What are the different types of connections available in Airflow?

Airflow only supports one type of connection, which is the SSH connection type
Airflow comes with several built-in connection types, including HTTP, MySQL, Postgres, SSH, and more. It also allows you to define custom connection types if needed
Airflow supports a very limited set of connection types, including MongoDB, Cassandra, and Redis

Question 77

With 3 DAGs that fetch data from the same API. Does it make sense to store this API in a variable?

Yes
No

Question 78

A variable can be stored in...

The metadata database
A secret backend
An environment variable
The metaverse

Question 79

You create a variable with an environment variable. Does Airflow generate a connection to fetch the value of this variable?

Yes
No

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
README.md		README.md

Kavoondev/Apache-Airflow-Certification

Folders and files

Latest commit

History

Repository files navigation