Try and practice the latest Cloudera : CDP-3002 real questions & answers

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Cloudera CDP-3002 Exam Syllabus Topics:

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

                                                      Cloudera CDP Data Engineer - Certification Sample Questions:

                                                      1. You need to read data from a Hive table into a Spark DataFrame. Which approach would be the most efficient?

                                                      A) Convert the Hive table to a managed table and then use spark.read.table("table_name")
                                                      B) Use the spark.read.parquet("/path/to/hive/table") method directly
                                                      C) Use the FROM table_name") method
                                                      D) Leverage Spark SQL capabilities with SELECT FROM table_name


                                                      2. 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.


                                                      3. How can you use Apache Airflow to ensure a data quality check stops the workflow if it fails, without failing subsequent tasks that are not dependent on the data quality check?

                                                      A) Use the ShortCircuitOperator with a condition that returns False if the check fails.
                                                      B) Use the BranchPythonOperator to split the workflow conditionally.
                                                      C) Set trigger_rule='all_done' on subsequent tasks.
                                                      D) Use the TaskGroup with trigger_rule='one_failed' for the data quality check task.


                                                      4. 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) Use the depends_on_past parameter in each data quality check task.
                                                      B) Implement each data quality check as a separate DAG.
                                                      C) Use the SequentialExecutor for the Airflow environment.
                                                      D) Explicitly set task dependencies using the set_upstream or set_downstream methods.


                                                      5. You're working with a large dataset that needs to be partitioned and processed in chunks to improve efficiency. How can you achieve this using Airflow operators?

                                                      A) Leverage the File transform operator to partition the data based on specific criteria within the operator itself.
                                                      B) Implement a custom Python script to handle partitioning and then use the BashOperator to execute the script within the DAG.
                                                      C) Use the Split Operator to divide the data into smaller subsets and chain them with downstream processing tasks.
                                                      D) Configure the source system to provide the data pre-partitioned for efficient processing.


                                                      Solutions:

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

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