You need to design a DAG that can be easily triggered based on external events or data availability. How can you achieve this functionality?

• A.Rely on manual intervention to trigger the DAG whenever necessary.
• B.Schedule the DAG to run continuously at a fixed interval.
• C.Utilize Airflow sensors like FileSensor or S3KeySensor to wait for specific conditions before triggering the DAG.
• D.Implement custom code within the DAG to actively check for external events or data availability.

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For a Hive table that is both partitioned and bucketed, what considerations must be taken into account to optimize a join query involving this table?

• A.Ensuring the join columns are neither partitioned nor bucketed as it may lead to increased complexity.
• B.The join should exclusively rely on the partitioned columns, ignoring the bucketed columns for optimal performance.
• C.Both the partitioning and bucketing columns should align with the join columns where possible to maximize the efficiency of data retrieval.
• D.Bucketing considerations are irrelevant in the context of join queries, with partitioning being the sole factor impacting performance.

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In the context of Airflow, what is the purpose of the "XCom" feature?

• A.To execute commands across different operating systems.
• B.To trigger external systems when a DAG reaches a specific state.
• C.To enable communication between different tasks within the same DAG.
• D.To manage dependencies and package installations across tasks.

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You're working with a team of data engineers who collaborate on developing and maintaining Airflow DAGs. How can you ensure version control and maintain a consistent development workflow?

• A.Store all DAG code in a shared folder on a local file system, relying on manual coordination between team members.
• B.Utilize a version control system like Git to track changes in the DAG code and enable collaborative development.
• C.Grant full access to the Airflow web Ul to all team members for code modifications, regardless of their roles.
• D.Implement a custom workflow management system outside of Airflow for managing DAG development.

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What happens when a task in Airflow is marked as "skipped"?

• A.The entire DAG is marked as failed, and no further tasks are executed.
• B.The skipped task's downstream tasks are automatically executed.
• C.The skipped task's downstream tasks are also skipped, unless their conditions for execution are met independently.
• D.Airflow retries the skipped task until it succeeds.

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