CDP-3002 exam dumps

Cloudera CDP-3002 Value Package

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  • Exam Code: CDP-3002
  • Exam Name: CDP Data Engineer - Certification Exam
  • No. of Questions: 320 Questions and Answers
  • Updated: Oct 02, 2026

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Cloudera CDP-3002 Exam Syllabus Topics:

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

                                                      Cloudera CDP Data Engineer - Certification Sample Questions:

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

                                                      Correct Answer: D  🗳️

                                                      Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).

                                                      How can you implement a data quality check in Apache Airflow that verifies the row count of a table does not decrease from the previous DAG run?

                                                      • A. Store the row count from the previous run in Airflow Variables and compare it using a Pythonoperator.
                                                      • B. Utilize the PreviousDagRunSensor with a custom Python function for comparison.
                                                      • C. Implement a custom SqlSensor that checks the row count against a stored value in XComs.
                                                      • D. Use the BranchPythonOperator to branch the workflow based on the row count comparison logic.
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: A  🗳️

                                                      Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).

                                                      When optimizing join operations in a distributed data processing environment, why is it important to co-locate join keys?

                                                      • A. To increase the storage capacity required for join operations
                                                      • B. To ensure data integrity by preventing data loss during network transmission
                                                      • C. To minimize data shuffle by ensuring related data is on the same node
                                                      • D. To enhance data encryption methods for secure joins
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: C  🗳️

                                                      Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).

                                                      In a CI/CD pipeline, what is a key consideration when integrating Cloudera Data Engineering (CDE. service API calls for deploying Spark jobs, specifically regarding security?

                                                      • A. Incorporating API token rotation and secure secret management practices
                                                      • B. Hardcoding API keys in the pipeline scripts
                                                      • C. Using generic Spark Docker images for all jobs to avoid API customization
                                                      • D. Disabling TLS for API endpoints to simplify integration
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: A  🗳️

                                                      Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).

                                                      You have a DataFrame containing sales data with columns "product_id", "customer id", and "amount". How can you efficiently calculate the total sales per customer?

                                                      • A. Implement a custom function to group and sum the sales
                                                      • B. Utilize spark SQL's GROUP BY and SUM functions
                                                      • C. Use a loop to iterate through the DataFrame and accumulate the sales for each customer
                                                      • D. Leverage Spark's machine learning library (MLIiB. for aggregation
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: B  🗳️

                                                      Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).

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