Online Access Free CDP-3002 Exam Questions

When leveraging Spark's DataFrame API for caching, what implicit optimization does Spark perform to enhance processing efficiency?

• A.Automatic conversion of all operations to map-reduce jobs
• B.Inlining of functions to reduce the overhead of JVM method calls
• C.Automatic selection of the optimal storage level based on the DataFrame's size
• D.Predicate pushdown and other logical optimizations before physical execution

You're working with a large Spark DataFrame and need to perform an aggregation operation (e.g., can you improve the performance of the aggregation? SUM, COUNT). How

• A.Increase the number of Spark executors without further optimization
• B.Use Spark SQL's built-in aggregation functions like SUM and COUNT
• C.Leverage partitioning techniques to group relevant data together
• D.All of the above

Which Spark configuration parameter should be increased to improve performance when dealing with large broadcast variables?

• A.'spark.executor.memory'
• B.'spark.broadcast.blockSize'
• C.'spark.driver.memory'
• D.'spark.default.parallelism'

In the context of Cloudera's Optimization Framework, what is the purpose of dynamic partition pruning?

• A.To increase the size of partitions dynamically based on data volume
• B.To dynamically eliminate unnecessary partitions from a query plan based on runtime statistics
• C.To update partition metadata in real-time
• D.To partition data dynamically based on query execution plans

What is the recommended way to handle dependencies between data quality checks in Apache Airflow to ensure that checks are performed in a specific sequence?

• A.Use the depends_on_past parameter in each data quality check task.
• B.Use the SequentialExecutor for the Airflow environment.
• C.Explicitly set task dependencies using the set_upstream or set_downstream methods.
• D.Implement each data quality check as a separate DAG.