What is the impact of query vectorization in Cloudera's Optimization Framework?

• A.It slows down query execution by adding complexity
• B.It enables the execution of SQL commands
• C.It improves query performance by processing batches of rows together
• D.It encrypts query results for security

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You need to optimize the performance of a Spark query that involves joining data from multiple Hive tables. What strategies can you employ to improve efficiency?

• A.Increase the number of Spark executors without any further optimization
• B.Use broadcast joins for small tables involved in the join operation
• C.Pre-partition tables based on the join columns for faster data co-location
• D.All of the above

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What is a primary consideration when deciding to cache data in a distributed computing environment like Apache Spark?

• A.Ensuring data is encrypted before caching
• B.Caching every dataset regardless of its access frequency
• C.The trade-off between memory usage and computational efficiency
• D.Using disk storage for all cached data to improve fault tolerance

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You're building an Airflow DAG that consists of multiple interdependent ETL pipelines. How can you ensure they execute in the correct order and avoid conflicts?

• A.Schedule each pipeline separately with appropriate scheduling intervals.
• B.Utilize Airflow sub-DAGs to group related tasks and define dependencies between them.
• C.Implement a custom script to manage the execution order of the pipelines.
• D.Run all pipelines simultaneously, assuming they are independent.

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Why is it recommended to use the DataFrame API over RDDs for most data processing tasks in Spark?

• A.DataFrames provide more fine-grained control over partitioning and parallelism.
• B.DataFrames automatically optimize queries using the Catalyst optimizer and Tungsten execution engine.
• C.RDDs are deprecated and will be removed in future versions of Spark.
• D.DataFrames require less memory and compute resources compared to RDDs.

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