Cloudera CDP-3002 : CDP Data Engineer - Certification Exam

  • Exam Code: CDP-3002
  • Exam Name: CDP Data Engineer - Certification Exam
  • Updated: Sep 20, 2026   Q&As: 320 Questions and Answers

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Cloudera CDP-3002 Exam Syllabus Topics:
SectionWeightObjectives
Topic 1: Workflow Orchestration15%- Pipeline Development
  • 1. Workflow monitoring & alerting
    • 2. End-to-end data pipeline design
      - Apache Airflow
      • 1. Incremental data extraction
        • 2. DAG design & scheduling
          • 3. Task dependencies & error handling
            Topic 2: Apache Spark Development & Processing48%- Spark Architecture & Execution Model
            • 1. Spark application lifecycle
              • 2. Distributed data processing concepts
                - Spark Streaming & Structured Streaming
                • 1. Checkpointing & fault tolerance
                  • 2. Micro-batch & stream processing
                    - Spark SQL & DataFrames
                    • 1. Query optimization & execution plans
                      • 2. DataFrame/Dataset API usage
                        - Performance Optimization
                        • 1. Caching & persistence strategies
                          • 2. Job tuning, partitioning & bucketing
                            Topic 3: Deployment & Operations10%- Security & Governance
                            • 1. Data lineage & compliance
                              • 2. Access control & authentication
                                - CDP Data Engineering Service
                                • 1. Environment configuration
                                  • 2. API & CLI usage
                                    Topic 4: Data Storage & Modeling22%- Distributed Persistence
                                    • 1. HDFS & cloud storage integration
                                      • 2. Data locality & access patterns
                                        - Apache Iceberg
                                        • 1. Partitioning & layout optimization
                                          • 2. Table management & ACID compliance
                                            - Data Formats & Storage
                                            • 1. Columnar formats (Parquet, ORC)
                                              • 2. Schema design & evolution
                                                Topic 5: Integration & Optimization5%- Troubleshooting
                                                • 1. Resource management
                                                  • 2. Bottleneck identification
                                                    - Hive & Spark Integration
                                                    • 1. Cross-engine query execution
                                                      Cloudera CDP Data Engineer - Certification Sample Questions:
                                                      Question #1

                                                      You're deploying a Spark application in a Kubernetes cluster. Which YAML specification would correctly define a SparkApplication in cluster mode using Scala and the Spark Pi example?

                                                      • A. Option B
                                                      • B. Option C
                                                      • C. Option D
                                                      • D. Option A
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: B  🗳️

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                                                      Question #2

                                                      You need to enable secure access to Iceberg tables in CDP, controlling permissions at the table, column, and row level. Which of the following approaches would you investigate?

                                                      • A. Implement custom Spark IJDFs to enforce authorization logic.
                                                      • B. Integrate with CDP's Kerberos-based authentication system.
                                                      • C. Configure role-based access control (RBA directly within Iceberg table properties.
                                                      • D. Leverage Apache Ranger's integration with Iceberg.
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: D  🗳️

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                                                      Question #3

                                                      Which Airflow operator is best used for executing a Python function as part of a DAG?

                                                      • A. SimpleHttpOperator
                                                      • B. PythonOperator
                                                      • C. Dockeroperator
                                                      • D. BashOperator
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: B  🗳️

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                                                      Question #4

                                                      You need to design your Airflow DAG for data quality checks to be scalable and manageable as the number of datasets and checks grows. How can you achieve this?

                                                      • A. Hardcode all data quality checks and data sources directly within the DAG code.
                                                      • B. Leverage external configuration files (e.g., YAML or JSON) to define data quality checks and associated parameters.
                                                      • C. Utilize Airflow variables to store configuration details like data source paths and check thresholds.
                                                      • D. Implement a modular design using sub-DAGs, where each sub-DAG encapsulates the data quality checks for a specific dataset.
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: B,D  🗳️

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                                                      Question #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.executor.instances'
                                                      • B. 'spark.dynamicAllocation.enabled'
                                                      • C. 'spark.kubernetes.dynamicAllocation.enabled'
                                                      • D. 'spark.kubernetes.executor.dynamicAllocation'
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: B  🗳️

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