You're working with a large dataset stored in multiple Parquet files across different HDFS directories. How can you efficiently load and process this data using Spark, ensuring data locality and minimizing shuffle operations?

• A.Directly load all files using spark.read.parquet("/path/to/data/")
• B.Use spark.read.parquet("/path/to/data/") with recursive directory listing
• C.Implement a custom function to read each Parquet file individually
• D.Leverage Spark SQL catalogs and partition discovery

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You need to create a new Hive table from a Spark DataFrame. What are the different approaches you can consider?

• A.Directly write the DataFrame to a directory in HDFS and define a corresponding Hive table schema
• B.Use the DataFrame.write.saveAsTable("table_name") method with appropriate options
• C.Convert the DataFrame to a temporary table and then use HiveQL commands to create a permanent table
• D.All of the above

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You're deploying your Airflow ETL pipelines to a production environment. What are some best practices to ensure reliability and scalability?

• A.Configure Airflow to run with high resource limits to handle unexpected spikes in workload.
• B.Implement robust error handling and retry mechanisms within your DAGs.
• C.Utilize version control for your DAG code and configuration files to track changes and facilitate rollbacks.
• D.All of the above

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Your team is using PySpark and wants to ensure task re-execution in case of a node failure. What mechanism in Spark ensures that tasks are retried on other nodes upon failure?

• A.Checkpointing
• B.Data Replication
• C.Task Re-execution
• D.Master Node Redundancy

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

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