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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Processing with Spark | 30% | - Spark SQL and DataFrames - Spark Performance Optimization - Spark Structured Streaming - DataFrame and Dataset APIs - Spark Core Concepts |
| Data Ingestion and Integration | 20% | - Data Federation - Data Transformation and ETL - Stream Data Ingestion - CDC (Change Data Capture) - Batch Data Ingestion |
| CDP Platform Operations | 15% | - Cloudera Data Platform Architecture - Cloudera Flow Management - Data Lake and Storage - Cluster Management and Monitoring |
| Data Quality and Governance | 15% | - Data Lineage - Data Catalog and Metadata - Data Validation and Cleansing - Access Control and Security |
| Data Pipeline Orchestration | 20% | - Workflow Dependencies - Error Handling and Retries - Apache Airflow on CDP - Pipeline Scheduling and Triggers |
Cloudera CDP Data Engineer - Certification Sample Questions:
You're tasked with optimizing the performance of your ETL pipeline in Airflow. What are some potential strategies to consider?
- A. Increase the number of worker processes in Airflow to parallelize task execution.
- B. Utilize efficient data structures and algorithms within your custom Python operators for data transformation.
- C. Leverage partitioning and bucketing techniques in the data warehouse to improve query performance.
- D. All of the above
Correct Answer: D 🗳️
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In a Kubernetes environment, how does Kubernetes ensure fault tolerance when a pod running a Spark task fails?
- A. Through Pod Replication with ReplicaSets
- B. By checkpointing RDDs
- C. Using a load balancer for master components
- D. By using a Standby Master setup
Correct Answer: A 🗳️
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Your Iceberg table has a hidden partition by month(event_timestamp). You frequently query with filters on the event_timestamp column. What potential problem might you encounter, and how would you address it?
- A. No problems; hidden partitioning is designed for this use case.
- B. Compatibility issues with older Spark versions; ensure you're using a version supporting hidden partitioning
- C. Errors due to incorrect partition discovery; you'll need to manually update Iceberg table metadata.
- D. Performance issues due to unnecessary file scanning; consider adding event_timestamp as an explicit partition.
Correct Answer: D 🗳️
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You need to design a DAG that can be easily monitored and visualized for performance insights. How can you achieve this?
- A. Implement custom logic within each task to send detailed performance metrics to external monitoring tools.
- B. Implement alerts and notifications within the DAG to trigger upon specific events or performance thresholds.
- C. Utilize Airflow's built-in metrics and monitoring features like the Airflow web UI to track DAG execution and task performance.
- D. All of the above
Correct Answer: C,D 🗳️
Considering Hive's optimization mechanisms, under which scenario might partition pruning fail to improve query performance?
- A. When querying data using a non-partition column as a filter
- B. When the table is partitioned on a column frequently used in query filters
- C. When the partitioned table contains a small number of partitions
- D. When querying data using the exact partition key in the WHERE clause
Correct Answer: A 🗳️
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