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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Workflow Orchestration | 15% | - Apache Airflow
|
| Topic 2: Deployment & Operations | 10% | - Security & Governance
|
| Topic 3: Apache Spark Development & Processing | 48% | - Spark Streaming & Structured Streaming
|
| Topic 4: Data Storage & Modeling | 22% | - Distributed Persistence
|
| Topic 5: Integration & Optimization | 5% | - Hive & Spark Integration
|
Cloudera CDP Data Engineer - Certification Sample Questions:
1. In the context of packaging a PySpark application, what is the purpose of the 'requirements.txt' file?
A) To define the Spark version compatible with the application.
B) To list all the third-party dependencies required by the application.
C) To list the environment variables needed for the application.
D) To specify the Python version required for the application.
2. You're working with an Airflow DAG that performs data quality checks on sensitive dat a. How can you ensure data security during the checks?
A) Implement data masking techniques to obfuscate sensitive information during the checks.
B) Utilize environment variables to store sensitive data and access them within the PythonOperator.
C) Store sensitive data like thresholds and comparison values directly within the DAG code.
D) All of the above
3. How can you leverage Spark Streaming for real-time data processing and analytics?
A) By implementing custom logic for data ingestion, transformation, and output.
B) By utilizing Structured Streaming with Kafka as the source and sink.
C) By defining a streaming DataFrame with a window function.
D) Both A and B.
4. You're building a complex Airflow DAG with numerous tasks and dependencies. How can you improve the DAG's readability and maintainability?
A) Implement extensive logging within each task to capture detailed execution information.
B) All of the above
C) Break down the DAG into smaller sub-DAGs with well-defined functionalities.
D) Use clear and descriptive names for tasks, operators, and variables throughout the DAG code.
5. You are processing a large dataset using Spark and need to ensure that the results are available for subsequent stages without recomputing. Which approach achieves this efficiently?
A) Use rdd.persist() with the appropriate storage level based on your needs
B) Leverage Spark's automatic checkpointing mechanism
C) Implement custom logic to save the data to HDFS between stages
D) Store the data in a temporary table using Spark SQL
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A,B | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: A |
