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Cloudera CDP-3002 Exam Syllabus Topics:

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

                                                      Cloudera CDP Data Engineer - Certification Sample Questions:

                                                      You're tasked with deploying a new Airflow DAG to production. What are some key considerations for ensuring a smooth and successful deployment?

                                                      • A. All of the above
                                                      • B. Thoroughly test the DAG in a staging environment before deploying it to production.
                                                      • C. Deploy the DAG directly to the production environment without any testing or staging phase.
                                                      • D. Utilize infrastructure as code (laC. tools like Terraform to manage Airflow deployment and configuration in a consistent and repeatable manner.
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: A,B,D  🗳️

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

                                                      Correct Answer: A,B  🗳️

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                                                      Which feature in Apache Airflow allows you to retry a data quality check task if it fails initially due to transient issues?

                                                      • A. retries parameter in the task definition
                                                      • B. SLAs
                                                      • C. catchup
                                                      • D. Branching
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: A  🗳️

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                                                      What are the potential consequences of persisting all intermediate RDDs in Spark with the MEMORY ONLY storage level?

                                                      • A. Increased risk of application crashes due to memory exhaustion
                                                      • B. All intermediate data will be automatically persisted to disk
                                                      • C. Improved performance due to faster data access
                                                      • D. No impact on the application, as Spark manages memory efficiently
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: A  🗳️

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                                                      Your team is using PySpark and wants to ensure task re-execution in case of a node failure. What mechanism in Spark ensures that tasks are retried on other nodes upon failure?

                                                      • A. Data Replication
                                                      • B. Master Node Redundancy
                                                      • C. Checkpointing
                                                      • D. Task Re-execution
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: D  🗳️

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