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Cloudera CDP Data Engineer - Certification : CDP-3002

CDP-3002

Exam Code: CDP-3002

Exam Name: CDP Data Engineer - Certification Exam

Updated: Aug 25, 2026

Q & A: 320 Questions and Answers

CDP-3002 Free Demo download:

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Cloudera CDP-3002 Exam Syllabus Topics:

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

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

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


                                                      2. You need to filter data from a Hive table based on a specific date range. Which approach would be most efficient and maintainable?

                                                      A) Leverage HiveQL's built-in filtering capabilities with a WHERE clause
                                                      B) Convert the Hive table to a temporary table and then use Spark SQL filtering
                                                      C) Implement a custom filter function in Spark to process each row individually
                                                      D) Use Spark SQL functions like filter with a date comparison expression


                                                      3. What is the best practice for handling DAG dependencies in Apache Airflow when one DAG's output is another DAG's input?

                                                      A) Use the TriggerDagRunOperator to trigger one DAG from another upon completion.
                                                      B) Directly call one DAG from another using the PythonOperator.
                                                      C) Manually trigger the dependent DAG after the first DAG completes.
                                                      D) Use the ExternalTaskSensor to wait for a task in another DAG to complete.


                                                      4. You want to write the results of a Spark DataFrame back to a Hive table. How can you achieve this efficiently?

                                                      A) Implement a custom function to write individual rows to the Hive table
                                                      B) Convert the DataFrame to a temporary table and then use HiveQL operations
                                                      C) Use the hiveContext.sql("lNSERT INTO table_name SELECT FROM df') method
                                                      D) Use the DataFrame.write.saveAsTable("table_name") method


                                                      5. How does dynamic schema inference impact the processing of streaming data in real-time analytics platforms?

                                                      A) It significantly increases the latency of data processing operations.
                                                      B) It enables the platform to adapt to changes in data format without downtime.
                                                      C) It decreases the overall throughput of the analytics platform.
                                                      D) It requires streaming data to be stored before its schema can be inferred.


                                                      Solutions:

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

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