If a Spark Driver pod in Kubernetes is reaching its CPU limit and experiencing performance issues, what is the most appropriate first action?

• A.Increase the CPU requests in the pod's YAML configuration.
• B.Decrease the memory limits in the pod's YAML configuration.
• C.Increase the CPU limits in the pod's YAML configuration.
• D.Restart the Kubernetes cluster.

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You're designing a schema for an Iceberg table that will store time-series sensor dat a. Which of the following considerations is most important for optimal query performance and storage efficiency?

• A.Using nested structs to represent the complex structure of sensor readings.
• B.Partitioning the data by both sensor ID and timestamp.
• C.Normalizing sensor data across multiple tables to reduce redundancy.
• D.Pre-aggregating sensor data at hourly intervals before inserting into the Iceberg table.

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You have deployed a Spark application on Kubernetes, which is experiencing intermittent failures. To improve fault tolerance, you decide to implement checkpointing. Which of the following is the best approach to add checkpointing in a PySpark application?

• A.
• B.Enable checkpointing in Kubernetes configuration files.
• C.
• D.Implement checkpointing at the application level outside of Spark.

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If you want to set a minimum and maximum number of Executor pods for a Spark application in Kubernetes, which pair of PySpark configuration settings would you use?

• A.'spark.executor.instances', 'spark.executor.cores'
• B.'spark.dynamicAllocation.minExecutors', 'spark.dynamicAllocation.maxExecutors'
• C.'spark.executor.memory', 'spark.executor.memoryoverhead'
• D.'spark.kubernetes.container.image', 'spark.kubernetes.executor.limit.cores'

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What does setting the Spark configuration parameter 'spark.sql.shuffle.partitions' impact?
A The default level of parallelism for joins and aggregations

• A.The serialization format of data
• B.The compression codec used for shuffle files
• C.The memory allocation for executor instances

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