Cloudera Certification CDP-3002

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Cloudera CDP Data Engineer - Certification Sample Questions:

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

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

2. Considering the dynamic nature of data workloads, how can Spark's dynamic resource allocation feature impact caching strategies?

A) By encrypting cached data to enhance security when reallocating resources dynamically.
B) By increasing the memory available for caching as the data workload increases, without manual intervention.
C) By automatically persisting all cached data to disk to prevent data loss during node decommissioning.
D) By potentially evicting cached data when executors are removed due to reduced workload, affecting performance.

3. How does Spark handle data shuffling during distributed processing?

A) By storing all data on a single node
B) By transferring only required data between executors
C) Spark doesn't perform data shuffling
D) By broadcasting all data to each executor

4. Which of the following best describes the benefit of partition pruning in Spark SQL?

A) It dynamically adjusts partition sizes based on the query execution plan.
B) It decreases the amount of data shuffled during join operations.
C) It selectively processes partitions that meet certain criteria, reducing 1/0.
D) It increases the number of partitions to maximize parallelism.

5. You are optimizing a SparkSQL query in your PySpark application running on Kubernetes. The query involves a join operation between a large DataFrame and a much smaller DataFrame. To minimize shuffling and optimize network utilization, which join strategy would you likely use?

A) Cartesian join
B) Broadcast join
C) Shuffle join
D) Sort merge join

Solutions: