• Exam Code: CDP-3002
  • Exam Name: CDP Data Engineer - Certification Exam
  • Certification Provider: Cloudera
  • Corresponding Certification:Cloudera Certification
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Cloudera CDP-3002 Exam Syllabus Topics:

SectionObjectives
Data Processing and Transformation- Spark processing
  • 1. Distributed processing concepts
    • 2. Spark SQL and DataFrames
      - SQL analytics engines
      • 1. Hive / Impala query optimization
        • 2. Data transformation best practices
          Platform Operations- Cluster and workload management
          • 1. Monitoring and troubleshooting
            • 2. Resource management and scheduling
              Data Storage and Modeling- Data lake architecture
              • 1. Partitioning and file formats (Parquet, ORC)
                • 2. HDFS / cloud object storage concepts
                  - Data modeling
                  • 1. Dimensional modeling concepts
                    • 2. Schema design for analytics
                      Data Ingestion and Integration- Batch and streaming ingestion
                      • 1. Apache NiFi / Kafka ingestion concepts
                        • 2. Data ingestion patterns in CDP
                          - Data movement and pipelines
                          • 1. Data flow orchestration
                            • 2. ETL/ELT workflows
                              Data Governance and Security- Data governance
                              • 1. Data lineage and auditing
                                • 2. Metadata management
                                  - Security frameworks
                                  • 1. Authentication and authorization in CDP
                                    • 2. Ranger / Sentry policies

                                      Cloudera CDP Data Engineer - Certification Sample Questions:

                                      You're designing a scalable and fault-tolerant Spark application for processing large-scale geospatial dat a. What additional considerations should you take into account beyond the general distributed processing principles?

                                      • A. Choose a larger virtual machine size for Spark executors
                                      • B. Leverage geospatial libraries like GeoSpark for efficient spatial operations
                                      • C. Use a dedicated cluster specifically for geospatial processing tasks
                                      • D. Implement custom partitioning strategies based on geospatial coordinates
                                      Reveal Solution  Discussion  0

                                      Correct Answer: B,D  🗳️

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

                                      You're tasked with creating a DAG in Airflow that orchestrates a complex data processing workflow. What are some key considerations for designing an effective DAG?

                                      • A. Implement extensive logging within each task for detailed information, even if it slows down execution.
                                      • B. Use a single DAG for the entire workflow, regardless of its complexity.
                                      • C. Configure the DAG to run continuously without any specific scheduling or triggering mechanism.
                                      • D. Break down the workflow into smaller, modular tasks with clear dependencies.
                                      Reveal Solution  Discussion  0

                                      Correct Answer: D  🗳️

                                      What is the recommended way to handle dependencies between data quality checks in Apache Airflow to ensure that checks are performed in a specific sequence?

                                      • A. Implement each data quality check as a separate DAG.
                                      • B. Use the SequentialExecutor for the Airflow environment.
                                      • C. Explicitly set task dependencies using the set_upstream or set_downstream methods.
                                      • D. Use the depends_on_past parameter in each data quality check task.
                                      Reveal Solution  Discussion  0

                                      Correct Answer: C  🗳️

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

                                      What is the recommended approach in Apache Airflow for ensuring data quality checks are performed after data is loaded into multiple target systems, which might complete their loading processes at different times?

                                      • A. Use a CrossDagDep operator to manage dependencies across multiple DAGs.
                                      • B. Utilize a BranchPythonOperator to dynamically route the workflow based on system availability.
                                      • C. Implement multiple ExternalTaskSensor instances, each waiting for a specific loading task to complete.
                                      • D. Schedule the data quality checks at a fixed delay after the longest expected load time.
                                      Reveal Solution  Discussion  0

                                      Correct Answer: C  🗳️

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

                                      You need to design an Airflow DAG that waits for a specific file to become available before proceeding with the downstream tasks. How can you achieve this dependency?

                                      • A. Schedule the DAG to run periodically, hoping the file becomes available eventually.
                                      • B. Implement a custom loop within a Python operator to continuously check for the file until it appears.
                                      • C. Use the File sensor operator to check for the file's existence and trigger downstream tasks upon its arrival.
                                      • D. Configure the source system to notify Airflow when the file is ready for processing.
                                      Reveal Solution  Discussion  0

                                      Correct Answer: C  🗳️

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

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