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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You notice a significant performance overhead when persisting a large RDD to disk. What potential factors could contribute to this, and how can you mitigate them?

A) Using HDFS as the storage location instead of local disks
B) All of the above
C) Inefficient serialization format for the RDD data
D) Insufficient disk space on the cluster nodes

2. When tuning Spark applications, why is it important to adjust the spark.executor.cores configuration?

A) To directly control the amount of memory available to each executor
B) To specify the number of tasks that can be run in parallel on a single executor
C) To determine the number of partitions created during shuffle operations
D) To limit the maximum size of Spark's internal data structures

3. An Airflow DAG designed to run a sequence of data validation checks generates a dynamic number of validation tasks based on the incoming data's characteristics. Each validation task must complete successfully before a final data processing task can begin. Which Airflow feature is most suitable for implementing this pattern?

A) A BranchPythonOperator with a follow-up Join task
B) The TriggerDagRunOperator
C) SubDAGs
D) The Dynamic Task Mapping feature

4. You are processing a large dataset using Spark and need to ensure that the results are available for subsequent stages without recomputing. Which approach achieves this efficiently?

A) Leverage Spark's automatic checkpointing mechanism
B) Implement custom logic to save the data to HDFS between stages
C) Store the data in a temporary table using Spark SQL
D) Use rdd.persist() with the appropriate storage level based on your needs

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

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

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