Airflow Skills Mock Test: Pipeline Orchestration & DAG Management

Learning Objectives

Test your knowledge of Airflow’s architecture and the role of its key components.

Assess your ability to define, create, and manage DAGs, including task dependencies.

Evaluate your skills in scheduling DAGs, setting execution dates, and handling retries and alerts

Test your understanding of various operators and sensors in Airflow and their applications

Assess your ability to optimize complex DAGs, visualize dependencies, and implement effective debugging strategies.

Overview

This mock test is designed to assess your skills in Apache Airflow by presenting a series of questions that cover key concepts and practical applications. The test will evaluate your understanding of Airflow's architecture, components, and the creation and management of Directed Acyclic Graphs (DAGs). You'll answer questions on defining and scheduling DAGs, setting task dependencies, and handling retries and alerts. Additionally, the assessment will cover best practices for optimizing and debugging DAGs, ensuring that you can effectively use Airflow for pipeline orchestration and data management. This test simulates real-world scenarios to gauge your proficiency and readiness in applying Airflow to complex workflows.

Prerequisites

Basic Knowledge of Python
Fundamental Understanding of ETL/ELT Processes
Basic understanding of workflow management and task orchestration systems
Basic understanding of Apache Airflow, including its purpose, architecture, core components, and how it is used for orchestrating data workflows.
Familiarity with Directed Acyclic Graphs (DAGs), including the role of default_args, DAG parameters, and how to configure them.
Understanding how to use sub-DAGs and task groups for organizing and managing complex workflows in Airflow.