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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Processing with Spark | 30% | - Spark Structured Streaming - Spark Performance Optimization - DataFrame and Dataset APIs - Spark Core Concepts - Spark SQL and DataFrames |
| Topic 2: Data Pipeline Orchestration | 20% | - Workflow Dependencies - Error Handling and Retries - Apache Airflow on CDP - Pipeline Scheduling and Triggers |
| Topic 3: CDP Platform Operations | 15% | - Cluster Management and Monitoring - Data Lake and Storage - Cloudera Flow Management - Cloudera Data Platform Architecture |
| Topic 4: Data Ingestion and Integration | 20% | - Stream Data Ingestion - Data Transformation and ETL - CDC (Change Data Capture) - Data Federation - Batch Data Ingestion |
| Topic 5: Data Quality and Governance | 15% | - Data Lineage - Data Catalog and Metadata - Data Validation and Cleansing - Access Control and Security |
Cloudera CDP Data Engineer - Certification Sample Questions:
You're building an Airflow DAG that consists of multiple interdependent ETL pipelines. How can you ensure they execute in the correct order and avoid conflicts?
- A. Implement a custom script to manage the execution order of the pipelines.
- B. Schedule each pipeline separately with appropriate scheduling intervals.
- C. Run all pipelines simultaneously, assuming they are independent.
- D. Utilize Airflow sub-DAGs to group related tasks and define dependencies between them.
Correct Answer: D 🗳️
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Your Airflow DAG involves sending notifications upon successful or failed task executions. How can you implement this functionality?
- A. Utilize Airflow variables to store notification details and access them within tasks.
- B. Implement custom logic within each task to send notifications using external libraries.
- C. Use the EmailOperator to send email notifications with details about the task status.
- D. Configure the Airflow web Ul to send alerts based on task execution status.
Correct Answer: C 🗳️
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When deploying a Spark application in Kubernetes, what is the purpose of specifying the '-conf spark.kubernetes.driver.pod.name=spark-driver-pod' in the 'spark-submit' command?
- A. To name the Executor pods.
- B. To specify the name of the Spark Driver pod.
- C. To assign a specific node for the Driver pod.
- D. To define the Docker image for the Driver pod.
Correct Answer: B 🗳️
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When would it be advantageous to use both partitioning and bucketing on a Hive table?
- A. When data needs to be stored in a single file for archival purposes
- B. When managing small datasets to reduce complexity
- C. When data security is a primary concern
- D. When dealing with large datasets that require efficient querying and data sampling
Correct Answer: D 🗳️
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In a PySpark application running on Kubernetes, if one of the Executors fails to execute a task due to a node failure, what action does the Spark Driver take?
- A. It pauses the application until the failed Executor is back online.
- B. It immediately shuts down the entire application.
- C. It reschedules the task on a different Executor.
- D. It requests Kubernetes to allocate more resources to the failed Executor.
Correct Answer: C 🗳️
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