Airflow and MWAA Interview Questions

 

• What is Apache Airflow? Explain its role as a workflow orchestration platform. 
• What are DAGs (Directed Acyclic Graphs)? Describe how they represent workflows. 
• What are operators in Airflow? Explain their purpose in defining tasks. 
• What are executors in Airflow? Discuss different executor options (e.g., LocalExecutor, CeleryExecutor, KubernetesExecutor) and their use cases. 
• What is Airflow metadata? Explain how it's stored and accessed (e.g., in a database, through MWAA's API). 
• How does MWAA differ from a self-managed Airflow environment? Focus on features like scalability, security, and management overhead. 
• What are some use cases for MWAA? Discuss scenarios where MWAA is a good fit (e.g., ETL pipelines, machine learning workflows). 
• What are the benefits of using MWAA? Highlight its advantages like simplified infrastructure management and built-in security. 
• How do you handle DAG versioning and deployment in MWAA? Explain the process of updating DAGs in a production environment. 
• How does MWAA handle scaling and resource allocation? Discuss how it manages workers and other resources. 
• How do you troubleshoot issues in Airflow workflows? Explain the process of debugging and fixing errors. 
• How do you monitor the health and performance of your MWAA environment? Explain how to use CloudWatch and other monitoring tools. 

Scenario-Based Questions:

• How would you design a DAG to pull data from an API, process it, and load it into a data warehouse? 
• How would you handle a situation where a task fails in your workflow? 
• How would you manage credentials for accessing external services (e.g., databases, APIs) within Airflow? 
• How would you optimize the performance of a long-running DAG? 
• How would you implement error handling and retry logic in your DAGs? 
• How would you schedule a DAG to run at a specific time every day? 
• How would you implement branching logic in a DAG? 
• How would you use XComs to pass data between tasks in a DAG? 
• How would you use macros in your DAGs? 
• How would you deploy a new version of a DAG to a production environment? 

AWS Specific Questions:

• What AWS services are integrated with MWAA?
• How does MWAA integrate with VPCs and IAM?
• How do you configure logging and monitoring for MWAA using CloudWatch?
• How do you manage access control and security for your MWAA environment?
• How does MWAA handle security best practices? 
• 1. Apache Airflow Fundamentals:
  • What is Apache Airflow? A platform for programmatically authoring, scheduling, and monitoring workflows. 
  • What is a DAG? A Directed Acyclic Graph, representing a workflow with tasks and dependencies, ensuring no circular dependencies. 
  • What is an Airflow task? A single, discrete unit of work within a DAG, often defined by an operator. 
  • What are Airflow operators? Building blocks that define specific actions within a task, like running a Python script or interacting with a database. 
  • What are Airflow executors? Components that manage task execution, allowing for different scaling and resource allocation strategies (Local, Celery, Kubernetes, etc.). 
  • What are XComs? Mechanism for tasks to exchange small amounts of data during execution. 
  • What are variables in Airflow? Global, persistent storage for configuration or runtime values. 
  • What are Trigger rules? Define when a task should be executed based on the status of its upstream tasks. 
  • What are the different states a task can be in? Examples include: none, scheduled, running, success, failed, skipped. 
  • What are some common use cases for Airflow? Data pipelines, ETL processes, machine learning workflows, etc. 

  • What is MWAA? A managed service for running Airflow on AWS, removing the operational burden of managing the infrastructure.
  • What versions of Airflow does MWAA support? MWAA supports specific versions of Airflow, and older versions may not be supported.
  • What are the benefits of using MWAA? Improved scalability, availability, and security compared to self-managed Airflow.
  • What are some MWAA environment specifications? Questions about task storage, operating systems, and custom images.
  • Does MWAA support Spot Instances or custom domains? Questions about specific features and configurations.
  • Can you SSH into an MWAA environment? MWAA restricts SSH access for security reasons.
  • How does MWAA handle task execution and orchestration? Focus on how MWAA leverages AWS services like SQS for task execution and workflow orchestration.
  • How is scaling handled in MWAA? Questions about metrics used to determine scaling needs and how custom metrics can be created.
  • Can you integrate custom SQS queues in MWAA? Questions about extending Airflow's capabilities within the MWAA environment. 

  3. Airflow and MWAA in Practice:

  • How do you create a new DAG in Airflow? Questions about defining DAG structure, tasks, and dependencies. 
  • How do you schedule a DAG in Airflow? Questions about using Airflow's scheduling features to run DAGs at specific intervals. 
  • How do you handle dependencies between tasks in a DAG? Understanding how to use trigger rules and task dependencies.