Cloudera CDP-3002 Exam : CDP Data Engineer - Certification Exam

Cloudera CDP-3002 exam
  • Exam Code: CDP-3002
  • Exam Name: CDP Data Engineer - Certification Exam
  • Updated: Aug 12, 2026
  • Q & A: 320 Questions and Answers
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Cloudera CDP-3002 Exam Syllabus Topics:

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

                                                      Cloudera CDP Data Engineer - Certification Sample Questions:

                                                      1. What mechanism does Airflow provide to retry failed tasks?

                                                      A) The on failure callback function in DAG definitions
                                                      B) Manual intervention and rerun via the Airflow Webserver
                                                      C) The retry_delay and retries parameters in task definitions
                                                      D) Airflow Scheduler's automatic rerun feature


                                                      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) Implement a custom filter function in Spark to process each row individually
                                                      C) Convert the Hive table to a temporary table and then use Spark SQL filtering
                                                      D) Use Spark SQL functions like filter with a date comparison expression


                                                      3. You're given a DataFrame containing information about flights, including columns "origin", "destination", and "delay_minutes". How can you find the top 5 origin airports with the most delayed flights on average?

                                                      A) Use Spark's machine learning library (MLIiB. for ranking and classification
                                                      B) Use groupBy and avg on "delay_minutes", then sort by the average in descending order and limit to top 5
                                                      C) Leverage Spark SQL's RANK function along with windowing to identify top 5 origins
                                                      D) Implement a custom function to calculate average delays for each origin and then sort and filter


                                                      4. Your ETL pipeline involves complex data transformations that require libraries not readily available in the Airflow environment. How can you ensure these libraries are accessible during pipeline execution?

                                                      A) Install the required libraries directly into the Airflow environment.
                                                      B) Configure Airflow to use a virtual environment with pre-installed libraries.
                                                      C) Utilize system-wide library installations, assuming they are accessible to the Airflow user.
                                                      D) Package the libraries with your DAG code and reference them within the Python operators.


                                                      5. Your Airflow DAG performs data quality checks that involve complex data transformations and aggregations. How can you ensure these checks are executed efficiently and don't impact the performance of the entire pipeline?

                                                      A) Offload complex data processing tasks to separate DAGs designed specifically for data preparation.
                                                      B) Utilize libraries like Pandas or Spark for data manipulation within the PythonOperator, leveraging optimization features where available.
                                                      C) Implement the data transformations and aggregations directly within the checking logic, regardless of efficiency.
                                                      D) Use Airflow's built-in data manipulation operators like SQLExecuteOperator for basic checks, avoiding complex transformations.


                                                      Solutions:

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

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