You need to handle potential errors and retries within your Airflow ETL pipeline. How can you achieve this functionality?

• A.Implement exception handling within your custom Python operators and define retry behavior manually.
• B.Use the BranchPythonOperator to check for specific error conditions and branch the DAG execution accordingly.
• C.Configure the DAG to retry failed tasks automatically with a set number of retries.
• D.All of the above

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What is the primary purpose of the Airflow Scheduler in Apache Airflow?

• A.To execute the code for each task in a DAG
• B.To monitor the state of tasks and trigger them based on their dependencies
• C.To provide a user interface for monitoring and managing DAGs
• D.To distribute tasks across workers for execution

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Due to regulatory requirements, you need to permanently delete specific sensitive records from an Iceberg table. Which of the following techniques would be most appropriate?

• A.Issue a standard Iceberg DELETE query, as deleted data will automatically be expunged from the table.
• B.Use Iceberg's EXPIRE SNAPSHOTS procedure to remove snapshots containing the sensitive data.
• C.Modify the data files directly using low-level tools to overwrite the sensitive data.
• D.Implement a custom process, leveraging Iceberg's row-level updates to delete the sensitive records and then using file-level operations to physically remove the data.

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Your Iceberg table has a hidden partition by month(event_timestamp). You frequently query with filters on the event_timestamp column. What potential problem might you encounter, and how would you address it?

• A.Performance issues due to unnecessary file scanning; consider adding event_timestamp as an explicit partition.
• B.Errors due to incorrect partition discovery; you'll need to manually update Iceberg table metadata.
• C.Compatibility issues with older Spark versions; ensure you're using a version supporting hidden partitioning
• D.No problems; hidden partitioning is designed for this use case.

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You need to process data stored in AWS S3 using SparkSQL. Which of the following options correctly reads a JSON file stored in S3 into a DataFrame and performs a SQL query on it?

• A.
• B.
• C.
• D.

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