Cloudera CDP-3002 Q&A - in .pdf

  • CDP-3002 pdf
  • Exam Code: CDP-3002
  • Exam Name: CDP Data Engineer - Certification Exam
  • Updated: Sep 15, 2026
  • Q & A: 320 Questions and Answers
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  • Exam Code: CDP-3002
  • Exam Name: CDP Data Engineer - Certification Exam
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  • Q & A: 320 Questions and Answers
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  • CDP-3002 Testing Engine
  • Exam Code: CDP-3002
  • Exam Name: CDP Data Engineer - Certification Exam
  • Updated: Sep 15, 2026
  • Q & A: 320 Questions and Answers
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Cloudera CDP-3002 Exam Syllabus Topics:

SectionObjectives
Data Ingestion and Integration- Data movement and pipelines
  • 1. Data flow orchestration
    • 2. ETL/ELT workflows
      - Batch and streaming ingestion
      • 1. Apache NiFi / Kafka ingestion concepts
        • 2. Data ingestion patterns in CDP
          Data Processing and Transformation- Spark processing
          • 1. Distributed processing concepts
            • 2. Spark SQL and DataFrames
              - SQL analytics engines
              • 1. Data transformation best practices
                • 2. Hive / Impala query optimization
                  Platform Operations- Cluster and workload management
                  • 1. Resource management and scheduling
                    • 2. Monitoring and troubleshooting
                      Data Storage and Modeling- Data modeling
                      • 1. Dimensional modeling concepts
                        • 2. Schema design for analytics
                          - Data lake architecture
                          • 1. Partitioning and file formats (Parquet, ORC)
                            • 2. HDFS / cloud object storage concepts
                              Data Governance and Security- Security frameworks
                              • 1. Ranger / Sentry policies
                                • 2. Authentication and authorization in CDP
                                  - Data governance
                                  • 1. Data lineage and auditing
                                    • 2. Metadata management

                                      Cloudera CDP Data Engineer - Certification Sample Questions:

                                      Question #1

                                      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.executor.instances', 'spark.executor.cores'
                                      • B. 'spark.executor.memory', 'spark.executor.memoryoverhead'
                                      • C. 'spark.kubernetes.container.image', 'spark.kubernetes.executor.limit.cores'
                                      • D. 'spark.dynamicAllocation.minExecutors', 'spark.dynamicAllocation.maxExecutors'
                                      Reveal Solution  Discussion  0

                                      Correct Answer: D  πŸ—³οΈ

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                                      Question #2

                                      Explain the role of Spark MLIib and its functionalities in building machine learning pipelines on Spark.

                                      • A. Spark MLIib is a comprehensive library for machine learning tasks within Spark
                                      • B. It allows integration with external machine learning libraries like TensorFlow or scikit-learn.
                                      • C. It provides pre-built machine learning algorithms and tools for building and evaluating models on Spark.
                                      • D. It offers functionalities for data pre-processing, feature engineering, and model training within Spark.
                                      Reveal Solution  Discussion  0

                                      Correct Answer: A  πŸ—³οΈ

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                                      Question #3

                                      Which of the following is a critical consideration when deciding between using a sort merge join and a shuffle hash join in a distributed data processing system like Spark?

                                      • A. The network latency between nodes in the cluster
                                      • B. The relative size of the datasets and the available memory on each executor
                                      • C. The availability of secondary indexes on the join keys
                                      • D. The version of the Spark cluster being used
                                      Reveal Solution  Discussion  0

                                      Correct Answer: B  πŸ—³οΈ

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                                      Question #4

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

                                      • A. CartesianJoin
                                      • B. ShuffleHashJoin
                                      • C. SortMergeJoin
                                      • D. BroadcastHashJoin
                                      Reveal Solution  Discussion  0

                                      Correct Answer: C  πŸ—³οΈ

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                                      Question #5

                                      Discuss the trade-offs between using wide tables (many columns) and narrow tables (few columns) in Spark and the implications for data processing efficiency.

                                      • A. Wide tables offer better performance for filtering operations, while narrow tables excel in aggregations.
                                      • B. Both A and C.
                                      • C. Wide tables require more storage and may lead to performance overhead due to data serialization.
                                      • D. Spark can optimize data access patterns for both wide and narrow tables equally.
                                      Reveal Solution  Discussion  0

                                      Correct Answer: B  πŸ—³οΈ

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