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Cloudera CDP Data Engineer - Certification Sample Questions:
1. Your Spark application encounters performance issues when reading data from a large Hive table. What potential optimization techniques can you explore?
A) Use a different file format for the Hive table, like CSV, for faster parsing
B) Implement custom data compression logic within Spark for improved storage efficiency
C) Increase the number of Spark executors without further optimization
D) Leverage partition pruning to only read relevant data from the table
2. Which of the following is true about persisting RDDs in Apache Spark?
A Persisting an RDD in memory allows for faster access but increases the risk of data loss.
A) Using MEMORY_ONLY_SER storage level reduces memory usage but increases CPU usage due to serialization.
B) Persisting an RDD to disk is generally recommended for all RDDs to prevent data loss.
C) It is not possible to persist an RDD using a combination of memory and disk.
3. Which technologies are typically involved in schema inference processes in Cloudera Data Platform (CDP)?
A) Traditional RDBMS
B) Apache NiFi
C) Apache Spark
D) Apache Avro
E) Manual scripting
4. When monitoring a PySpark application in Kubernetes, you notice that Executor pods are frequently restarting. What is the most likely cause of this issue?
A) Executor pods are not configured to restart upon failure.
B) Dynamic resource allocation is disabled.
C) Too many tasks assigned to each Executor.
D) Insufficient memory allocation for Executor pods.
5. In a PySpark application running on Kubernetes, you want to enable dynamic allocation of Executors. Which configuration setting is essential to turn on this feature?
A) 'spark.kubernetes.executor.dynamicAllocation'
B) 'spark.dynamicAllocation.enabled'
C) 'spark.executor.instances'
D) 'spark.kubernetes.dynamicAllocation.enabled'
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: B,C,D | Question # 4 Answer: D | Question # 5 Answer: B |

