Training_Apache Airflow_Quiz1

What is Apache Airflow?

• Apache Airflow is a platform used to programmatically author, schedule, and monitor workflows.

• It is a language used to build business logic

• Apache Airflow is a is Python's micro-framework for web app development.

• Apache Airflow is a structured language for app development

What are the key features of Apache Airflow?

• Machine learning model training

• Real-time data analysis

• Database management

• Some key features of Apache Airflow include workflow scheduling, task dependency management, data processing, and monitoring capabilities.

Explain the concept of Directed Acyclic Graphs (DAGs) in Apache Airflow.

• DAGs in Apache Airflow have no impact on task scheduling

• DAGs in Apache Airflow are used to define a workflow and the dependencies between tasks, allowing for the scheduling and monitoring of complex data pipelines.

• DAGs in Apache Airflow are used for storing static data

• DAGs in Apache Airflow are only used for visualizing data pipelines

What are the different components of Apache Airflow architecture?

• Scheduler, Executor, Metadata Database, Web Interface

• API Gateway, Load Balancer, Database

• User Interface, Task Manager, Notification System

• Data Storage, Authentication, Frontend

How does Apache Airflow handle task dependencies?

• Apache Airflow uses a stack data structure to manage task dependencies

• Apache Airflow handles task dependencies based on alphabetical order of task names

• Apache Airflow handles task dependencies using directed acyclic graphs (DAGs) to define the order in which tasks should be executed.

• Apache Airflow uses random selection to determine task dependencies

Explain the role of the Scheduler in Apache Airflow.

• The Scheduler in Apache Airflow is responsible for managing the database connections.

• The Scheduler in Apache Airflow is responsible for generating reports on task execution.

• The Scheduler in Apache Airflow is responsible for handling user authentication.

• The Scheduler in Apache Airflow is responsible for scheduling the execution of tasks and managing the dependencies between them.

What are the different ways to trigger a DAG in Apache Airflow?

• By sending an email

• manually, on a schedule, or by external triggers.

• By clicking the .py file from the dags folder

Fill the below blank
from airflow _____ DAG

Which of the options is correct to fill the ______?

from airflow._______.python_operator import PythonOperator

• operator

• operators

• DAG

• dag

Fill the blank with an appropriate method name

schedule_interval = datetime._______(days =1)