Cloudera CDP-3002 : CDP Data Engineer - Certification Exam

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Cloudera CDP Data Engineer - Certification Sample Questions:

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

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

2. Which Spark SQL join strategy is selected by default when the Catalyst optimizer cannot determine the size of tables involved in a join?

A) SortMergeJoin
B) CartesianJoin
C) BroadcastHashJoin
D) ShuffleHashJoin

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

4. Which of the following strategies would NOT be recommended for managing skewed data during join operations in Spark?

A) Applying a filter to remove outliers that cause data skewness before joining.
B) Increasing the number of partitions to distribute the skewed data more evenly.
C) Salting the keys to distribute the data more evenly across partitions.
D) Using a broadcast join assuming one dataset is small enough to fit into memory.

5. What are the potential consequences of persisting all intermediate RDDs in Spark with the MEMORY ONLY storage level?

A) All intermediate data will be automatically persisted to disk
B) No impact on the application, as Spark manages memory efficiently
C) Increased risk of application crashes due to memory exhaustion
D) Improved performance due to faster data access

Solutions: