Cloudera CDP-3002 real exam prep : CDP Data Engineer - Certification Exam

  • Exam Code: CDP-3002
  • Exam Name: CDP Data Engineer - Certification Exam
  • Updated: Aug 17, 2026
  • Q&As: 320 Questions and Answers

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Cloudera CDP-3002 Exam Syllabus Topics:

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

                                                      Cloudera CDP Data Engineer - Certification Sample Questions:

                                                      1. Consider the following code snippet:# Sample DataFrame (assuming it exists) df = spark.createDataFrame(...)
                                                      # Attempt to explode a nested array column (fix the error)
                                                      df_exploded = df.withColumn("items", F.explode(df["items"]))
                                                      df_exploded.show()
                                                      What is the error in this code, and how can it be fixed?

                                                      A) The error is missing parentheses around the column name in the explode function. Fix: F.explode(df("items"))
                                                      B) The error is using withColumn instead of a dedicated method for exploding arrays. Fix: Use df.withColumnExploded. (This function doesn't exist in Spark)
                                                      C) The error is attempting to modify the original DataFrame in-place. Fix: Use a separate variable to store the exploded DataFrame.
                                                      D) There is no error in the code snippet.


                                                      2. For a dataset that is used multiple times in different transformations but is not large enough to warrant disk storage, which caching level in Apache Spark is most appropriate?

                                                      A) DISK ONLY
                                                      B) MEMORY AND DISK SER
                                                      C) MEMORY ONLY
                                                      D) MEMORY ONLY SER


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

                                                      A) Dockeroperator
                                                      B) PythonOperator
                                                      C) SimpleHttpOperator
                                                      D) BashOperator


                                                      4. In Spark, what is the advantage of using the 'coalesce' method over the 'repartition' method when reducing the number of partitions in an RDD?

                                                      A) 'coalesce' reduces the number of partitions without a full data shuffle, enhancing performance.
                                                      B) 'coalesce' can increase the number of partitions without shuffling.
                                                      C) 'coalesce' triggers a full shuffle of the data, improving data distribution.
                                                      D) 'repartition' is incapable of reducing the number of partitions.


                                                      5. How does partition pruning contribute to query performance in the Cloudera Data Platform?

                                                      A) By increasing the number of partitions
                                                      B) By encrypting data at rest
                                                      C) By removing unnecessary data replication
                                                      D) By selecting only relevant partitions for query execution


                                                      Solutions:

                                                      Question # 1
                                                      Answer: C
                                                      Question # 2
                                                      Answer: C
                                                      Question # 3
                                                      Answer: B
                                                      Question # 4
                                                      Answer: A
                                                      Question # 5
                                                      Answer: D

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