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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Quality and Governance | 15% | - Data Validation and Cleansing - Data Catalog and Metadata - Data Lineage - Access Control and Security |
| CDP Platform Operations | 15% | - Cluster Management and Monitoring - Cloudera Flow Management - Data Lake and Storage - Cloudera Data Platform Architecture |
| Data Processing with Spark | 30% | - Spark Structured Streaming - Spark Performance Optimization - Spark SQL and DataFrames - Spark Core Concepts - DataFrame and Dataset APIs |
| Data Pipeline Orchestration | 20% | - Workflow Dependencies - Pipeline Scheduling and Triggers - Error Handling and Retries - Apache Airflow on CDP |
| Data Ingestion and Integration | 20% | - CDC (Change Data Capture) - Batch Data Ingestion - Stream Data Ingestion - Data Federation - Data Transformation and ETL |
Cloudera CDP Data Engineer - Certification Sample Questions:
1. In a Kubernetes environment, why is it beneficial to run the Spark Driver in its own pod?
A) To use a different programming language for the Driver.
B) To isolate the Driver from Executor pods for security reasons.
C) To automatically scale the Driver based on workload.
D) To reduce the cost of cloud resources.
2. You need to read data from a Hive table into a Spark DataFrame. Which approach would be the most efficient?
A) Leverage Spark SQL capabilities with SELECT FROM table_name
B) Use the spark.read.parquet("/path/to/hive/table") method directly
C) Use the FROM table_name") method
D) Convert the Hive table to a managed table and then use spark.read.table("table_name")
3. You need to design a DAG that can be easily paused, resumed, or skipped based on specific conditions. How can you achieve this functionality?
A) Implement custom logic within each task to check for pause or skip conditions.
B) Configure the DAG to run continuously without any manual intervention.
C) Utilize Airflow pools to dynamically control the execution of tasks based on resource availability.
D) Leverage Airflow XCom to share information about the desired action (pause, resume, skip) between tasks.
4. You need to design a DAG that can be easily monitored and visualized for performance insights. How can you achieve this?
A) Utilize Airflow's built-in metrics and monitoring features like the Airflow web UI to track DAG execution and task performance.
B) Implement alerts and notifications within the DAG to trigger upon specific events or performance thresholds.
C) Implement custom logic within each task to send detailed performance metrics to external monitoring tools.
D) All of the above
5. What is a primary benefit of schema-on-read compared to schema-on-write in big data processing frameworks?
A) It requires all data to conform to a rigid schema before storage.
B) It significantly reduces the computational overhead during data ingestion.
C) It mandates manual intervention for each data read operation.
D) It allows for greater flexibility in data formats and structures at the time of analysis.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: A,D | Question # 5 Answer: D |







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