Your Spark application involves complex data transformations requiring multiple shuffles. How can you leverage Spark's in- memory caching mechanisms to improve performance?
A Use rdd.persist(StorageLevel.MEMORY_ONLY) for all intermediate RDDs

• A.Implement custom caching logic using in-memory data structures
• B.Leverage Spark SQL caching with materialized views
• C.Tune Spark configuration parameters like spark.storage.memoryFraction

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In the context of caching data for reuse in Spark, how does the Tungsten project contribute to enhancing memory management and execution efficiency?

• A.By providing a graphical interface for memory management
• B.By rewriting Spark operations in a lower-level language for direct memory access
• C.By optimizing Spark's internal memory management and data processing operations
• D.By enforcing strict data typing to reduce runtime type checking overhead

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You're deploying your Airflow DAGs to a production environment. What are some key considerations for ensuring security and reliability?

• A.Implement role-based access control (RBAC. to restrict access to sensitive DAGs and resources.
• B.Configure Airflow to run with high resource limits to handle unexpected spikes in workload.
• C.Disable task logging to improve DAG execution performance.
• D.Schedule DAG runs as frequently as possible to ensure real-time data processing.

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You need to monitor the performance and resource usage of your Airflow ETL pipelines. How can you achieve this?

• A.Utilize Airflow's built-in metrics and monitoring features like the Airflow web UI and command-line tools.
• B.Implement custom monitoring scripts to track specific metrics not readily available in Airflow.
• C.You cannot directly monitor Airflow pipelines; rely on external monitoring tools for the underlying infrastructure.
• D.Monitor the resource utilization of the entire system, assuming it reflects the pipeline's performance.

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